+
+### Please, follow these steps to contribute:
+1. Reverse a website from this list: [sites-to-reverse](https://github.com/xtekky/gpt4free/issues/40)
+2. Add it to [./testing](https://github.com/xtekky/gpt4free/tree/main/testing)
+3. Refactor it and add it to [./g4f](https://github.com/xtekky/gpt4free/tree/main/g4f)
+
+### We will be grateful to see you as a contributor!
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
index 7f380618841b17503f7167d3414e1bdfb66080dd..3b6692cf7cd394e273860137f9e126bfbb43a68f 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,21 +1,89 @@
-# Use the base image
-FROM hlohaus789/g4f:latest
+FROM seleniarm/node-chromium
-# Set working directory
-WORKDIR /app
+ARG G4F_VERSION
+ARG G4F_USER=g4f
+ARG G4F_USER_ID=1000
+ARG G4F_NO_GUI
+ARG G4F_PASS=secret
-# Create necessary directories
-RUN mkdir -p /app/har_and_cookies /app/generated_images
+ENV G4F_VERSION $G4F_VERSION
+ENV G4F_USER $G4F_USER
+ENV G4F_USER_ID $G4F_USER_ID
+ENV G4F_NO_GUI $G4F_NO_GUI
-# Set volume mount points
-VOLUME ["/app/har_and_cookies", "/app/generated_images"]
+ENV SE_SCREEN_WIDTH 1850
+ENV PYTHONUNBUFFERED 1
+ENV G4F_DIR /app
+ENV G4F_LOGIN_URL http://localhost:7900/?autoconnect=1&resize=scale&password=$G4F_PASS
+ENV HOME /home/$G4F_USER
+ENV PATH $PATH:$HOME/.local/bin
+ENV SE_DOWNLOAD_DIR $HOME/Downloads
+ENV SEL_USER $G4F_USER
+ENV SEL_UID $G4F_USER_ID
+ENV SEL_GID $G4F_USER_ID
-# Expose the specified ports 8080 1337 7900
+USER root
+
+# If docker compose, install git
+RUN if [ "$G4F_VERSION" = "" ] ; then \
+ apt-get -qqy update && \
+ apt-get -qqy install git \
+ ; fi
+
+# Install Python3, pip, remove OpenJDK 11, clean up
+RUN apt-get -qqy update \
+ && apt-get -qqy install python3 python-is-python3 pip \
+ && apt-get -qyy remove openjdk-11-jre-headless \
+ && apt-get -qyy autoremove \
+ && apt-get -qyy clean \
+ && rm -rf /var/lib/apt/lists/* /var/cache/apt/*
+
+# Update entrypoint
+COPY docker/supervisor.conf /etc/supervisor/conf.d/selenium.conf
+COPY docker/supervisor-api.conf /etc/supervisor/conf.d/api.conf
+COPY docker/supervisor-gui.conf /etc/supervisor/conf.d/gui.conf
+
+# If no gui
+RUN if [ "$G4F_NO_GUI" ] ; then \
+ rm /etc/supervisor/conf.d/gui.conf \
+ ; fi
+
+# Change background image
+COPY docker/background.png /usr/share/images/fluxbox/ubuntu-light.png
+
+# Add user, fix permissions
+RUN groupadd -g $G4F_USER_ID $G4F_USER \
+ && useradd -rm -G sudo -u $G4F_USER_ID -g $G4F_USER_ID $G4F_USER \
+ && echo "${G4F_USER}:${G4F_PASS}" | chpasswd \
+ && mkdir "${SE_DOWNLOAD_DIR}" \
+ && chown "${G4F_USER_ID}:${G4F_USER_ID}" $SE_DOWNLOAD_DIR /var/run/supervisor /var/log/supervisor \
+ && chown "${G4F_USER_ID}:${G4F_USER_ID}" -R /opt/bin/ /usr/bin/chromedriver /opt/selenium/
+
+# Switch user
+USER $G4F_USER_ID
+
+# Set VNC password
+RUN mkdir -p ${HOME}/.vnc \
+ && x11vnc -storepasswd ${G4F_PASS} ${HOME}/.vnc/passwd
+
+# Set the working directory in the container.
+WORKDIR $G4F_DIR
+
+# Copy the project's requirements file into the container.
+COPY requirements.txt $G4F_DIR
+
+# Upgrade pip for the latest features and install the project's Python dependencies.
+RUN pip install --break-system-packages --upgrade pip \
+ && pip install --break-system-packages -r requirements.txt \
+ && pip install --break-system-packages \
+ undetected-chromedriver selenium-wire \
+ && pip uninstall -y --break-system-packages \
+ pywebview plyer
+
+# Copy the entire package into the container.
+ADD --chown=$G4F_USER:$G4F_USER g4f $G4F_DIR/g4f
+
+# Expose ports
EXPOSE 7860
-# Increase shared memory size
-# Note: The actual shm-size is set at runtime with --shm-size flag
-RUN mkdir -p /dev/shm && chmod 777 /dev/shm
-# Default command to run the container
-CMD ["sh", "-c", "exec $CMD"]
\ No newline at end of file
diff --git a/Dockerfile copy b/Dockerfile copy
new file mode 100644
index 0000000000000000000000000000000000000000..625312e217ee68ed6713a07f55d5800e293a2f2b
--- /dev/null
+++ b/Dockerfile copy
@@ -0,0 +1,87 @@
+FROM seleniarm/node-chromium
+
+ARG G4F_VERSION
+ARG G4F_USER=g4f
+ARG G4F_USER_ID=1000
+ARG G4F_NO_GUI
+ARG G4F_PASS=secret
+
+ENV G4F_VERSION $G4F_VERSION
+ENV G4F_USER $G4F_USER
+ENV G4F_USER_ID $G4F_USER_ID
+ENV G4F_NO_GUI $G4F_NO_GUI
+
+ENV SE_SCREEN_WIDTH 1850
+ENV PYTHONUNBUFFERED 1
+ENV G4F_DIR /app
+ENV G4F_LOGIN_URL http://localhost:7900/?autoconnect=1&resize=scale&password=$G4F_PASS
+ENV HOME /home/$G4F_USER
+ENV PATH $PATH:$HOME/.local/bin
+ENV SE_DOWNLOAD_DIR $HOME/Downloads
+ENV SEL_USER $G4F_USER
+ENV SEL_UID $G4F_USER_ID
+ENV SEL_GID $G4F_USER_ID
+
+USER root
+
+# If docker compose, install git
+RUN if [ "$G4F_VERSION" = "" ] ; then \
+ apt-get -qqy update && \
+ apt-get -qqy install git \
+ ; fi
+
+# Install Python3, pip, remove OpenJDK 11, clean up
+RUN apt-get -qqy update \
+ && apt-get -qqy install python3 python-is-python3 pip \
+ && apt-get -qyy remove openjdk-11-jre-headless \
+ && apt-get -qyy autoremove \
+ && apt-get -qyy clean \
+ && rm -rf /var/lib/apt/lists/* /var/cache/apt/*
+
+# Update entrypoint
+COPY docker/supervisor.conf /etc/supervisor/conf.d/selenium.conf
+COPY docker/supervisor-api.conf /etc/supervisor/conf.d/api.conf
+COPY docker/supervisor-gui.conf /etc/supervisor/conf.d/gui.conf
+
+# If no gui
+RUN if [ "$G4F_NO_GUI" ] ; then \
+ rm /etc/supervisor/conf.d/gui.conf \
+ ; fi
+
+# Change background image
+COPY docker/background.png /usr/share/images/fluxbox/ubuntu-light.png
+
+# Add user, fix permissions
+RUN groupadd -g $G4F_USER_ID $G4F_USER \
+ && useradd -rm -G sudo -u $G4F_USER_ID -g $G4F_USER_ID $G4F_USER \
+ && echo "${G4F_USER}:${G4F_PASS}" | chpasswd \
+ && mkdir "${SE_DOWNLOAD_DIR}" \
+ && chown "${G4F_USER_ID}:${G4F_USER_ID}" $SE_DOWNLOAD_DIR /var/run/supervisor /var/log/supervisor \
+ && chown "${G4F_USER_ID}:${G4F_USER_ID}" -R /opt/bin/ /usr/bin/chromedriver /opt/selenium/
+
+# Switch user
+USER $G4F_USER_ID
+
+# Set VNC password
+RUN mkdir -p ${HOME}/.vnc \
+ && x11vnc -storepasswd ${G4F_PASS} ${HOME}/.vnc/passwd
+
+# Set the working directory in the container.
+WORKDIR $G4F_DIR
+
+# Copy the project's requirements file into the container.
+COPY requirements.txt $G4F_DIR
+
+# Upgrade pip for the latest features and install the project's Python dependencies.
+RUN pip install --break-system-packages --upgrade pip \
+ && pip install --break-system-packages -r requirements.txt \
+ && pip install --break-system-packages \
+ undetected-chromedriver selenium-wire \
+ && pip uninstall -y --break-system-packages \
+ pywebview plyer
+
+# Copy the entire package into the container.
+ADD --chown=$G4F_USER:$G4F_USER g4f $G4F_DIR/g4f
+
+# Expose ports
+EXPOSE 8080 1337
diff --git a/Dockerfile-armv7 b/Dockerfile-armv7
new file mode 100644
index 0000000000000000000000000000000000000000..866ac63bc2b9f06cba7ba1ca91cf8e3a7c7f2fe8
--- /dev/null
+++ b/Dockerfile-armv7
@@ -0,0 +1,67 @@
+FROM python:slim-bookworm
+
+ARG G4F_VERSION
+ARG G4F_USER=g4f
+ARG G4F_USER_ID=1000
+ARG PYDANTIC_VERSION=1.8.1
+
+ENV G4F_VERSION $G4F_VERSION
+ENV G4F_USER $G4F_USER
+ENV G4F_USER_ID $G4F_USER_ID
+ENV G4F_DIR /app
+
+RUN apt-get update && apt-get upgrade -y \
+ && apt-get install -y git \
+ && apt-get install --quiet --yes --no-install-recommends \
+ build-essential \
+# Add user and user group
+ && groupadd -g $G4F_USER_ID $G4F_USER \
+ && useradd -rm -G sudo -u $G4F_USER_ID -g $G4F_USER_ID $G4F_USER \
+ && mkdir -p /var/log/supervisor \
+ && chown "${G4F_USER_ID}:${G4F_USER_ID}" /var/log/supervisor \
+ && echo "${G4F_USER}:${G4F_USER}" | chpasswd \
+ && python -m pip install --upgrade pip
+
+USER $G4F_USER_ID
+WORKDIR $G4F_DIR
+
+ENV HOME /home/$G4F_USER
+ENV PATH "${HOME}/.local/bin:${PATH}"
+
+# Create app dir and copy the project's requirements file into it
+RUN mkdir -p $G4F_DIR
+COPY requirements-min.txt $G4F_DIR
+COPY requirements-slim.txt $G4F_DIR
+
+# Upgrade pip for the latest features and install the project's Python dependencies.
+RUN pip install --no-cache-dir -r requirements-min.txt \
+ && pip install --no-cache-dir --no-binary setuptools \
+ Cython==0.29.22 \
+ setuptools \
+ # Install PyDantic
+ && pip install \
+ -vvv \
+ --no-cache-dir \
+ --no-binary :all: \
+ --global-option=build_ext \
+ --global-option=-j8 \
+ pydantic==${PYDANTIC_VERSION}
+RUN cat requirements-slim.txt | xargs -n 1 pip install --no-cache-dir || true
+
+# Remove build packages
+RUN pip uninstall --yes \
+ Cython \
+ setuptools
+
+USER root
+
+# Clean up build deps
+RUN apt-get purge --auto-remove --yes \
+ build-essential \
+ && apt-get clean \
+ && rm --recursive --force /var/lib/apt/lists/* /tmp/* /var/tmp/*
+
+USER $G4F_USER_ID
+
+# Copy the entire package into the container.
+ADD --chown=$G4F_USER:$G4F_USER g4f $G4F_DIR/g4f
\ No newline at end of file
diff --git a/Dockerfile-slim b/Dockerfile-slim
new file mode 100644
index 0000000000000000000000000000000000000000..dfc3344dcd07e1e979f2c5cb33fbfaa89d9a83bd
--- /dev/null
+++ b/Dockerfile-slim
@@ -0,0 +1,39 @@
+FROM python:slim-bookworm
+
+ARG G4F_VERSION
+ARG G4F_USER=g4f
+ARG G4F_USER_ID=1000
+
+ENV G4F_VERSION $G4F_VERSION
+ENV G4F_USER $G4F_USER
+ENV G4F_USER_ID $G4F_USER_ID
+ENV G4F_DIR /app
+
+RUN apt-get update && apt-get upgrade -y \
+ && apt-get install -y git \
+# Add user and user group
+ && groupadd -g $G4F_USER_ID $G4F_USER \
+ && useradd -rm -G sudo -u $G4F_USER_ID -g $G4F_USER_ID $G4F_USER \
+ && mkdir -p /var/log/supervisor \
+ && chown "${G4F_USER_ID}:${G4F_USER_ID}" /var/log/supervisor \
+ && echo "${G4F_USER}:${G4F_USER}" | chpasswd \
+ && python -m pip install --upgrade pip \
+ && apt-get clean \
+ && rm --recursive --force /var/lib/apt/lists/* /tmp/* /var/tmp/*
+
+USER $G4F_USER_ID
+WORKDIR $G4F_DIR
+
+ENV HOME /home/$G4F_USER
+ENV PATH "${HOME}/.local/bin:${PATH}"
+
+# Create app dir and copy the project's requirements file into it
+RUN mkdir -p $G4F_DIR
+COPY requirements-slim.txt $G4F_DIR
+
+# Upgrade pip for the latest features and install the project's Python dependencies.
+RUN pip install --no-cache-dir -r requirements-slim.txt \
+ && pip install --no-cache-dir duckduckgo-search>=5.0
+
+# Copy the entire package into the container.
+ADD --chown=$G4F_USER:$G4F_USER g4f $G4F_DIR/g4f
\ No newline at end of file
diff --git a/LEGAL_NOTICE.md b/LEGAL_NOTICE.md
new file mode 100644
index 0000000000000000000000000000000000000000..50a1d141037c6983fff9a99c583c2c1597787b35
--- /dev/null
+++ b/LEGAL_NOTICE.md
@@ -0,0 +1,55 @@
+## Legal Notice
+
+This repository is **not associated with or endorsed** by the providers of the APIs contained herein. This project is intended **for educational purposes only**. It is a personal project aimed at learning and exploration. Owners of any included sites or services may contact me to improve their security or request the removal of their content from this repository.
+
+### **Affiliation Disclaimer**
+
+This repository is not associated with or endorsed by any of the API providers mentioned herein. All trademarks, API services, and other intellectual property referenced are the property of their respective owners. No claim of ownership or affiliation is made by this project.
+
+### **Liability Limitation**
+
+Under no circumstances shall the author of this repository be liable for any direct, indirect, incidental, special, consequential, or punitive damagesāincluding but not limited to loss of profits, data, or useāarising out of or in connection with the repository. This limitation applies regardless of whether such damages were foreseeable or whether the author was advised of the possibility of such damages.
+
+### **No Warranties**
+
+This repository is provided on an "as is" and "as available" basis without any warranties of any kind, express or implied. This includes, but is not limited to, implied warranties of merchantability, fitness for a particular purpose, and non-infringement.
+
+### **User Responsibility**
+
+Users assume all risks associated with the use of this repository. They are solely responsible for any damage or lossāincluding financial lossāthat results from the use or misuse of the repository and its contents.
+
+### **Legal Compliance**
+
+Users are responsible for ensuring that their use of the repository and its contents complies with all applicable local, state, national, and international laws and regulations.
+
+### **Indemnification**
+
+Users agree to indemnify, defend, and hold harmless the author from any claims, liabilities, damages, losses, or expensesāincluding legal feesāarising out of or in any way connected with their use of this repository, violation of these terms, or infringement of any intellectual property or other rights of any person or entity.
+
+### **No Endorsement**
+
+The inclusion of third-party content does not imply endorsement or recommendation of such content by the author.
+
+### **Governing Law and Jurisdiction**
+
+Any disputes arising out of or related to the use of this repository shall be governed by the laws of the author's jurisdiction, without regard to conflict of law principles.
+
+### **Severability**
+
+If any provision of this notice is found to be unlawful, void, or unenforceable, that provision shall be deemed severable from this notice and shall not affect the validity and enforceability of the remaining provisions.
+
+### **Acknowledgment of Understanding**
+
+By using this repository, users acknowledge that they have read, understood, and agree to be bound by these terms.
+
+### **Updates and Changes**
+
+The author reserves the right to modify, update, or remove any content, information, or features in this repository at any time without prior notice. Users are responsible for regularly reviewing the content and any changes made to this repository.
+
+### **Unforeseen Consequences**
+
+The author is not responsible for any consequences, damages, or losses arising from the use or misuse of this repository or the content provided by third-party APIs. Users are solely responsible for their actions and any repercussions that may follow.
+
+### **Educational Purpose**
+
+This project and its content are provided strictly for educational purposes. Users acknowledge that they are using the APIs and models at their own risk and agree to comply with all applicable laws and regulations.
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..e72bfddabc15be5718a7cc061ac10e47741d8219
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+ GNU GENERAL PUBLIC LICENSE
+ Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc.
+ ${markdown_render(message)}
+ ${imageInput.dataset.src
+ ? '
'
+ : ''
+ }
+
+
+ + On the first screenshot is Pydroid and on the second is the Web UI in a browser +
+ +
+ 
+ 
+
[\S\s]+?)\n```", text)
+ if match:
+ text = match.group("code")
+ try:
+ return json.loads(text.strip())
+ except json.JSONDecodeError:
+ print("No valid json:", text)
+ return {}
+
+def read_text(text: str) -> str:
+ """
+ Extracts text from a markdown code block.
+
+ Args:
+ text (str): A string containing a markdown code block.
+
+ Returns:
+ str: The extracted text.
+ """
+ match = re.search(r"```(markdown|)\n(?P[\S\s]+?)\n```", text)
+ if match:
+ return match.group("text")
+ return text
+
+def get_ai_response(prompt: str, as_json: bool = True) -> Union[dict, str]:
+ """
+ Gets a response from g4f API based on the prompt.
+
+ Args:
+ prompt (str): The prompt to send to g4f.
+ as_json (bool): Whether to parse the response as JSON.
+
+ Returns:
+ Union[dict, str]: The parsed response from g4f, either as a dictionary or a string.
+ """
+ response = g4f.ChatCompletion.create(
+ G4F_MODEL,
+ [{'role': 'user', 'content': prompt}],
+ G4F_PROVIDER,
+ ignore_stream_and_auth=True
+ )
+ return read_json(response) if as_json else read_text(response)
+
+def analyze_code(pull: PullRequest, diff: str)-> list[dict]:
+ """
+ Analyzes the code changes in the pull request.
+
+ Args:
+ pull (PullRequest): The pull request object.
+ diff (str): The diff of the pull request.
+
+ Returns:
+ list[dict]: A list of comments generated by the analysis.
+ """
+ comments = []
+ changed_lines = []
+ current_file_path = None
+ offset_line = 0
+
+ for line in diff.split('\n'):
+ if line.startswith('+++ b/'):
+ current_file_path = line[6:]
+ changed_lines = []
+ elif line.startswith('@@'):
+ match = re.search(r'\+([0-9]+?),', line)
+ if match:
+ offset_line = int(match.group(1))
+ elif current_file_path:
+ if (line.startswith('\\') or line.startswith('diff')) and changed_lines:
+ prompt = create_analyze_prompt(changed_lines, pull, current_file_path)
+ response = get_ai_response(prompt)
+ for review in response.get('reviews', []):
+ review['path'] = current_file_path
+ comments.append(review)
+ current_file_path = None
+ elif line.startswith('-'):
+ changed_lines.append(line)
+ else:
+ changed_lines.append(f"{offset_line}:{line}")
+ offset_line += 1
+
+ return comments
+
+def create_analyze_prompt(changed_lines: list[str], pull: PullRequest, file_path: str):
+ """
+ Creates a prompt for the g4f model.
+
+ Args:
+ changed_lines (list[str]): The lines of code that have changed.
+ pull (PullRequest): The pull request object.
+ file_path (str): The path to the file being reviewed.
+
+ Returns:
+ str: The generated prompt.
+ """
+ code = "\n".join(changed_lines)
+ example = '{"reviews": [{"line": , "body": ""}]}'
+ return f"""Your task is to review pull requests. Instructions:
+- Provide the response in following JSON format: {example}
+- Do not give positive comments or compliments.
+- Provide comments and suggestions ONLY if there is something to improve, otherwise "reviews" should be an empty array.
+- Write the comment in GitHub Markdown format.
+- Use the given description only for the overall context and only comment the code.
+- IMPORTANT: NEVER suggest adding comments to the code.
+
+Review the following code diff in the file "{file_path}" and take the pull request title and description into account when writing the response.
+
+Pull request title: {pull.title}
+Pull request description:
+---
+{pull.body}
+---
+
+Each line is prefixed by its number. Code to review:
+```
+{code}
+```
+"""
+
+def create_review_prompt(pull: PullRequest, diff: str):
+ """
+ Creates a prompt to create a review comment.
+
+ Args:
+ pull (PullRequest): The pull request object.
+ diff (str): The diff of the pull request.
+
+ Returns:
+ str: The generated prompt for review.
+ """
+ return f"""Your task is to review a pull request. Instructions:
+- Write in name of g4f copilot. Don't use placeholder.
+- Write the review in GitHub Markdown format.
+- Thank the author for contributing to the project.
+
+Pull request author: {pull.user.name}
+Pull request title: {pull.title}
+Pull request description:
+---
+{pull.body}
+---
+
+Diff:
+```diff
+{diff}
+```
+"""
+
+def main():
+ try:
+ github = Github(GITHUB_TOKEN)
+ pull = get_pr_details(github)
+ if not pull:
+ print(f"No PR number found")
+ exit()
+ diff = get_diff(pull.diff_url)
+ except Exception as e:
+ print(f"Error get details: {e.__class__.__name__}: {e}")
+ exit(1)
+ try:
+ review = get_ai_response(create_review_prompt(pull, diff), False)
+ except Exception as e:
+ print(f"Error create review: {e}")
+ exit(1)
+ if pull.get_reviews().totalCount > 0 or pull.get_issue_comments().totalCount > 0:
+ pull.create_issue_comment(body=review)
+ return
+ try:
+ comments = analyze_code(pull, diff)
+ except Exception as e:
+ print(f"Error analyze: {e}")
+ exit(1)
+ print("Comments:", comments)
+ try:
+ if comments:
+ pull.create_review(body=review, comments=comments)
+ else:
+ pull.create_issue_comment(body=review)
+ except Exception as e:
+ print(f"Error posting review: {e}")
+ exit(1)
+
+if __name__ == "__main__":
+ main()
diff --git a/etc/tool/create_provider.py b/etc/tool/create_provider.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a9827a8accf53f3a228a07adf96ae3eb4fa598f
--- /dev/null
+++ b/etc/tool/create_provider.py
@@ -0,0 +1,134 @@
+
+import sys, re
+from pathlib import Path
+from os import path
+
+sys.path.append(str(Path(__file__).parent.parent.parent))
+
+import g4f
+
+g4f.debug.logging = True
+
+def read_code(text):
+ if match := re.search(r"```(python|py|)\n(?P[\S\s]+?)\n```", text):
+ return match.group("code")
+
+def input_command():
+ print("Enter/Paste the cURL command. Ctrl-D or Ctrl-Z ( windows ) to save it.")
+ contents = []
+ while True:
+ try:
+ line = input()
+ except EOFError:
+ break
+ contents.append(line)
+ return "\n".join(contents)
+
+name = input("Name: ")
+provider_path = f"g4f/Provider/{name}.py"
+
+example = """
+from __future__ import annotations
+
+from aiohttp import ClientSession
+
+from ..typing import AsyncResult, Messages
+from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from .helper import format_prompt
+
+
+class {name}(AsyncGeneratorProvider, ProviderModelMixin):
+ label = ""
+ url = "https://example.com"
+ api_endpoint = "https://example.com/api/completion"
+ working = True
+ needs_auth = False
+ supports_stream = True
+ supports_system_message = True
+ supports_message_history = True
+
+ default_model = ''
+ models = ['', '']
+
+ model_aliases = {
+ "alias1": "model1",
+ }
+
+ @classmethod
+ def get_model(cls, model: str) -> str:
+ if model in cls.models:
+ return model
+ elif model in cls.model_aliases:
+ return cls.model_aliases[model]
+ else:
+ return cls.default_model
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ **kwargs
+ ) -> AsyncResult:
+ model = cls.get_model(model)
+
+ headers = {{
+ "authority": "example.com",
+ "accept": "application/json",
+ "origin": cls.url,
+ "referer": f"{{cls.url}}/chat",
+ }}
+ async with ClientSession(headers=headers) as session:
+ prompt = format_prompt(messages)
+ data = {{
+ "prompt": prompt,
+ "model": model,
+ }}
+ async with session.post(f"{{cls.url}}/api/chat", json=data, proxy=proxy) as response:
+ response.raise_for_status()
+ async for chunk in response.content:
+ if chunk:
+ yield chunk.decode()
+"""
+
+if not path.isfile(provider_path):
+ command = input_command()
+
+ prompt = f"""
+Create a provider from a cURL command. The command is:
+```bash
+{command}
+```
+A example for a provider:
+```python
+{example}
+```
+The name for the provider class:
+{name}
+Replace "hello" with `format_prompt(messages)`.
+And replace "gpt-3.5-turbo" with `model`.
+"""
+
+ print("Create code...")
+ response = []
+ for chunk in g4f.ChatCompletion.create(
+ model=g4f.models.default,
+ messages=[{"role": "user", "content": prompt}],
+ timeout=300,
+ stream=True,
+ ):
+ print(chunk, end="", flush=True)
+ response.append(chunk)
+ print()
+ response = "".join(response)
+
+ if code := read_code(response):
+ with open(provider_path, "w") as file:
+ file.write(code)
+ print("Saved at:", provider_path)
+ with open("g4f/Provider/__init__.py", "a") as file:
+ file.write(f"\nfrom .{name} import {name}")
+else:
+ with open(provider_path, "r") as file:
+ code = file.read()
diff --git a/etc/tool/improve_code.py b/etc/tool/improve_code.py
new file mode 100644
index 0000000000000000000000000000000000000000..8578b478577c653545abf54355cd2275ab5d2204
--- /dev/null
+++ b/etc/tool/improve_code.py
@@ -0,0 +1,45 @@
+
+import sys, re
+from pathlib import Path
+from os import path
+
+sys.path.append(str(Path(__file__).parent.parent.parent))
+
+import g4f
+
+def read_code(text):
+ if match := re.search(r"```(python|py|)\n(?P[\S\s]+?)\n```", text):
+ return match.group("code")
+
+path = input("Path: ")
+
+with open(path, "r") as file:
+ code = file.read()
+
+prompt = f"""
+Improve the code in this file:
+```py
+{code}
+```
+Don't remove anything.
+Add typehints if possible.
+Don't add any typehints to kwargs.
+Don't remove license comments.
+"""
+
+print("Create code...")
+response = []
+for chunk in g4f.ChatCompletion.create(
+ model=g4f.models.default,
+ messages=[{"role": "user", "content": prompt}],
+ timeout=300,
+ stream=True
+):
+ response.append(chunk)
+ print(chunk, end="", flush=True)
+print()
+response = "".join(response)
+
+if code := read_code(response):
+ with open(path, "w") as file:
+ file.write(code)
diff --git a/etc/tool/openapi.py b/etc/tool/openapi.py
new file mode 100644
index 0000000000000000000000000000000000000000..83359e4e7ebf7a6d0b8d0cdcde20848b48bf5f88
--- /dev/null
+++ b/etc/tool/openapi.py
@@ -0,0 +1,11 @@
+import json
+
+from g4f.api import create_app
+
+app = create_app()
+
+with open("openapi.json", "w") as f:
+ data = json.dumps(app.openapi())
+ f.write(data)
+
+print(f"openapi.json - {round(len(data)/1024, 2)} kbytes")
\ No newline at end of file
diff --git a/etc/tool/provider_init.py b/etc/tool/provider_init.py
new file mode 100644
index 0000000000000000000000000000000000000000..22f21d4d9ed32d442ee0cef21057f8928f81189b
--- /dev/null
+++ b/etc/tool/provider_init.py
@@ -0,0 +1,33 @@
+from pathlib import Path
+
+
+def main():
+ content = create_content()
+ with open("g4f/provider/__init__.py", "w", encoding="utf-8") as f:
+ f.write(content)
+
+
+def create_content():
+ path = Path()
+ paths = path.glob("g4f/provider/*.py")
+ paths = [p for p in paths if p.name not in ["__init__.py", "base_provider.py"]]
+ classnames = [p.stem for p in paths]
+
+ import_lines = [f"from .{name} import {name}" for name in classnames]
+ import_content = "\n".join(import_lines)
+
+ classnames.insert(0, "BaseProvider")
+ all_content = [f' "{name}"' for name in classnames]
+ all_content = ",\n".join(all_content)
+ all_content = f"__all__ = [\n{all_content},\n]"
+
+ return f"""from .base_provider import BaseProvider
+{import_content}
+
+
+{all_content}
+"""
+
+
+if __name__ == "__main__":
+ main()
diff --git a/etc/tool/readme_table.py b/etc/tool/readme_table.py
new file mode 100644
index 0000000000000000000000000000000000000000..e89f861bed5d55adad12d9e9ef3c9e310b647af4
--- /dev/null
+++ b/etc/tool/readme_table.py
@@ -0,0 +1,152 @@
+import re
+from urllib.parse import urlparse
+import asyncio
+
+from g4f import models, ChatCompletion
+from g4f.providers.types import BaseRetryProvider, ProviderType
+from etc.testing._providers import get_providers
+from g4f import debug
+
+debug.logging = True
+
+async def test_async(provider: ProviderType):
+ if not provider.working:
+ return False
+ messages = [{"role": "user", "content": "Hello Assistant!"}]
+ try:
+ if "webdriver" in provider.get_parameters():
+ return False
+ response = await asyncio.wait_for(ChatCompletion.create_async(
+ model=models.default,
+ messages=messages,
+ provider=provider
+ ), 30)
+ return bool(response)
+ except Exception as e:
+ if debug.logging:
+ print(f"{provider.__name__}: {e.__class__.__name__}: {e}")
+ return False
+
+def test_async_list(providers: list[ProviderType]):
+ responses: list = [
+ asyncio.run(test_async(_provider))
+ for _provider in providers
+ ]
+ return responses
+
+def print_providers():
+
+ providers = get_providers()
+ responses = test_async_list(providers)
+
+ for type in ("GPT-4", "GPT-3.5", "Other"):
+ lines = [
+ "",
+ f"### {type}",
+ "",
+ "| Website | Provider | GPT-3.5 | GPT-4 | Stream | Status | Auth |",
+ "| ------ | ------- | ------- | ----- | ------ | ------ | ---- |",
+ ]
+ for is_working in (True, False):
+ for idx, _provider in enumerate(providers):
+ if is_working != _provider.working:
+ continue
+ do_continue = False
+ if type == "GPT-4" and _provider.supports_gpt_4:
+ do_continue = True
+ elif type == "GPT-3.5" and not _provider.supports_gpt_4 and _provider.supports_gpt_35_turbo:
+ do_continue = True
+ elif type == "Other" and not _provider.supports_gpt_4 and not _provider.supports_gpt_35_turbo:
+ do_continue = True
+ if not do_continue:
+ continue
+ netloc = urlparse(_provider.url).netloc.replace("www.", "")
+ website = f"[{netloc}]({_provider.url})"
+
+ provider_name = f"`g4f.Provider.{_provider.__name__}`"
+
+ has_gpt_35 = "āļø" if _provider.supports_gpt_35_turbo else "ā"
+ has_gpt_4 = "āļø" if _provider.supports_gpt_4 else "ā"
+ stream = "āļø" if _provider.supports_stream else "ā"
+ if _provider.working:
+ status = ''
+ if responses[idx]:
+ status = ''
+ else:
+ status = ''
+ else:
+ status = ''
+ auth = "āļø" if _provider.needs_auth else "ā"
+
+ lines.append(
+ f"| {website} | {provider_name} | {has_gpt_35} | {has_gpt_4} | {stream} | {status} | {auth} |"
+ )
+ print("\n".join(lines))
+
+def print_models():
+ base_provider_names = {
+ "google": "Google",
+ "openai": "OpenAI",
+ "huggingface": "Huggingface",
+ "anthropic": "Anthropic",
+ "inflection": "Inflection",
+ "meta": "Meta",
+ }
+ provider_urls = {
+ "google": "https://gemini.google.com/",
+ "openai": "https://openai.com/",
+ "huggingface": "https://huggingface.co/",
+ "anthropic": "https://www.anthropic.com/",
+ "inflection": "https://inflection.ai/",
+ "meta": "https://llama.meta.com/",
+ }
+
+ lines = [
+ "| Model | Base Provider | Provider | Website |",
+ "| ----- | ------------- | -------- | ------- |",
+ ]
+ for name, model in models.ModelUtils.convert.items():
+ if name.startswith("gpt-3.5") or name.startswith("gpt-4"):
+ if name not in ("gpt-3.5-turbo", "gpt-4", "gpt-4-turbo"):
+ continue
+ name = re.split(r":|/", model.name)[-1]
+ if model.base_provider not in base_provider_names:
+ continue
+ base_provider = base_provider_names[model.base_provider]
+ if not isinstance(model.best_provider, BaseRetryProvider):
+ provider_name = f"g4f.Provider.{model.best_provider.__name__}"
+ else:
+ provider_name = f"{len(model.best_provider.providers)}+ Providers"
+ provider_url = provider_urls[model.base_provider]
+ netloc = urlparse(provider_url).netloc.replace("www.", "")
+ website = f"[{netloc}]({provider_url})"
+
+ lines.append(f"| {name} | {base_provider} | {provider_name} | {website} |")
+
+ print("\n".join(lines))
+
+def print_image_models():
+ lines = [
+ "| Label | Provider | Image Model | Vision Model | Website |",
+ "| ----- | -------- | ----------- | ------------ | ------- |",
+ ]
+ from g4f.gui.server.api import Api
+ for image_model in Api.get_image_models():
+ provider_url = image_model["url"]
+ netloc = urlparse(provider_url).netloc.replace("www.", "")
+ website = f"[{netloc}]({provider_url})"
+ label = image_model["provider"] if image_model["label"] is None else image_model["label"]
+ if image_model["image_model"] is None:
+ image_model["image_model"] = "ā"
+ if image_model["vision_model"] is None:
+ image_model["vision_model"] = "ā"
+ lines.append(f'| {label} | `g4f.Provider.{image_model["provider"]}` | {image_model["image_model"]}| {image_model["vision_model"]} | {website} |')
+
+ print("\n".join(lines))
+
+if __name__ == "__main__":
+ #print_providers()
+ #print("\n", "-" * 50, "\n")
+ #print_models()
+ print("\n", "-" * 50, "\n")
+ print_image_models()
\ No newline at end of file
diff --git a/etc/tool/translate_readme.py b/etc/tool/translate_readme.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0a9b1f1c95ade97a7e1702531ecaa420e3d6ba8
--- /dev/null
+++ b/etc/tool/translate_readme.py
@@ -0,0 +1,88 @@
+
+import sys
+from pathlib import Path
+import asyncio
+
+sys.path.append(str(Path(__file__).parent.parent.parent))
+
+import g4f
+g4f.debug.logging = True
+from g4f.debug import access_token
+provider = g4f.Provider.OpenaiChat
+
+iso = "GE"
+language = "german"
+translate_prompt = f"""
+Translate this markdown document to {language}.
+Don't translate or change inline code examples.
+```md
+"""
+keep_note = "Keep this: [!Note] as [!Note].\n"
+blocklist = [
+ '## Ā©ļø Copyright',
+ '## š Providers and Models',
+ '## š Related GPT4Free Projects'
+]
+allowlist = [
+ "### Other",
+ "### Models"
+]
+
+def read_text(text):
+ start = end = 0
+ new = text.strip().split('\n')
+ for i, line in enumerate(new):
+ if line.startswith('```'):
+ if not start:
+ start = i + 1
+ end = i
+ return '\n'.join(new[start:end]).strip()
+
+async def translate(text):
+ prompt = translate_prompt + text.strip() + '\n```'
+ if "[!Note]" in text:
+ prompt = keep_note + prompt
+ result = read_text(await provider.create_async(
+ model="",
+ messages=[{"role": "user", "content": prompt}],
+ access_token=access_token
+ ))
+ if text.endswith("```") and not result.endswith("```"):
+ result += "\n```"
+ return result
+
+async def translate_part(part, i):
+ blocklisted = False
+ for headline in blocklist:
+ if headline in part:
+ blocklisted = True
+ if blocklisted:
+ lines = part.split('\n')
+ lines[0] = await translate(lines[0])
+ part = '\n'.join(lines)
+ for trans in allowlist:
+ if trans in part:
+ part = part.replace(trans, await translate(trans))
+ else:
+ part = await translate(part)
+ print(f"[{i}] translated")
+ return part
+
+async def translate_readme(readme) -> str:
+ parts = readme.split('\n## ')
+ print(f"{len(parts)} parts...")
+ parts = await asyncio.gather(
+ *[translate_part("## " + part, i) for i, part in enumerate(parts)]
+ )
+ return "\n\n".join(parts)
+
+with open("README.md", "r") as fp:
+ readme = fp.read()
+
+print("Translate readme...")
+readme = asyncio.run(translate_readme(readme))
+
+file = f"README-{iso}.md"
+with open(file, "w") as fp:
+ fp.write(readme)
+print(f'"{file}" saved')
\ No newline at end of file
diff --git a/etc/tool/vercel.py b/etc/tool/vercel.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5ce964c428ade2716bb5443c4d6025e71a6c379
--- /dev/null
+++ b/etc/tool/vercel.py
@@ -0,0 +1,103 @@
+import json
+import re
+from typing import Any
+
+import quickjs
+from curl_cffi import requests
+
+session = requests.Session(impersonate="chrome107")
+
+
+def get_model_info() -> dict[str, Any]:
+ url = "https://sdk.vercel.ai"
+ response = session.get(url)
+ html = response.text
+ paths_regex = r"static\/chunks.+?\.js"
+ separator_regex = r'"\]\)<\/script>', text)
+ data = json.loads(match.group("json"))
+ challenge_seeds = data["props"]["pageProps"]["challengeSeeds"]
+
+ prompt = messages[-1]["content"]
+ data = {
+ "question": prompt,
+ "question_history": [
+ message["content"] for message in messages[:-1] if message["role"] == "user"
+ ],
+ "answer_history": [
+ message["content"] for message in messages if message["role"] == "assistant"
+ ],
+ "webResults": [],
+ "options": {
+ "date": datetime.now().strftime("%d.%m.%Y"),
+ "language": "en-US",
+ "detailed": True,
+ "anonUserId": "",
+ "answerModel": "GPT-4" if model.startswith("gpt-4") else "Phind-34B",
+ "creativeMode": creative_mode,
+ "customLinks": []
+ },
+ "context": "\n".join([message["content"] for message in messages if message["role"] == "system"]),
+ }
+ data["challenge"] = generate_challenge(data, **challenge_seeds)
+ async with session.post(f"https://https.api.phind.com/infer/", headers=headers, json=data) as response:
+ new_line = False
+ async for line in response.iter_lines():
+ if line.startswith(b"data: "):
+ chunk = line[6:]
+ if chunk.startswith(b''):
+ raise RuntimeError(f"Response: {chunk.decode()}")
+ if chunk.startswith(b'') or chunk.startswith(b''):
+ pass
+ elif chunk.startswith(b"") or chunk.startswith(b""):
+ pass
+ elif chunk.startswith(b"") or chunk.startswith(b""):
+ pass
+ elif chunk:
+ yield chunk.decode()
+ elif new_line:
+ yield "\n"
+ new_line = False
+ else:
+ new_line = True
+
+def deterministic_stringify(obj):
+ def handle_value(value):
+ if isinstance(value, (dict, list)):
+ if isinstance(value, list):
+ return '[' + ','.join(sorted(map(handle_value, value))) + ']'
+ else: # It's a dict
+ return '{' + deterministic_stringify(value) + '}'
+ elif isinstance(value, bool):
+ return 'true' if value else 'false'
+ elif isinstance(value, (int, float)):
+ return format(value, '.8f').rstrip('0').rstrip('.')
+ elif isinstance(value, str):
+ return f'"{value}"'
+ else:
+ return 'null'
+
+ items = sorted(obj.items(), key=lambda x: x[0])
+ return ','.join([f'{k}:{handle_value(v)}' for k, v in items if handle_value(v) is not None])
+
+def prng_general(seed, multiplier, addend, modulus):
+ a = seed * multiplier + addend
+ if a < 0:
+ return ((a%modulus)-modulus)/modulus
+ else:
+ return a%modulus/modulus
+
+def generate_challenge_seed(l):
+ I = deterministic_stringify(l)
+ d = parse.quote(I, safe='')
+ return simple_hash(d)
+
+def simple_hash(s):
+ d = 0
+ for char in s:
+ if len(char) > 1 or ord(char) >= 256:
+ continue
+ d = ((d << 5) - d + ord(char[0])) & 0xFFFFFFFF
+ if d > 0x7FFFFFFF: # 2147483647
+ d -= 0x100000000 # Subtract 2**32
+ return d
+
+def generate_challenge(obj, **kwargs):
+ return prng_general(
+ seed=generate_challenge_seed(obj),
+ **kwargs
+ )
\ No newline at end of file
diff --git a/g4f/Provider/deprecated/V50.py b/g4f/Provider/deprecated/V50.py
new file mode 100644
index 0000000000000000000000000000000000000000..456445f7b90863a7cbbe11b1d516b4f3e231a03f
--- /dev/null
+++ b/g4f/Provider/deprecated/V50.py
@@ -0,0 +1,61 @@
+from __future__ import annotations
+
+import uuid
+
+import requests
+
+from ...typing import Any, CreateResult
+from ..base_provider import AbstractProvider
+
+
+class V50(AbstractProvider):
+ url = 'https://p5.v50.ltd'
+ supports_gpt_35_turbo = True
+ supports_stream = False
+ needs_auth = False
+ working = False
+
+ @staticmethod
+ def create_completion(
+ model: str,
+ messages: list[dict[str, str]],
+ stream: bool, **kwargs: Any) -> CreateResult:
+
+ conversation = (
+ "\n".join(
+ f"{message['role']}: {message['content']}" for message in messages
+ )
+ + "\nassistant: "
+ )
+ payload = {
+ "prompt" : conversation,
+ "options" : {},
+ "systemMessage" : ".",
+ "temperature" : kwargs.get("temperature", 0.4),
+ "top_p" : kwargs.get("top_p", 0.4),
+ "model" : model,
+ "user" : str(uuid.uuid4())
+ }
+
+ headers = {
+ 'authority' : 'p5.v50.ltd',
+ 'accept' : 'application/json, text/plain, */*',
+ 'accept-language' : 'id-ID,id;q=0.9,en-US;q=0.8,en;q=0.7',
+ 'content-type' : 'application/json',
+ 'origin' : 'https://p5.v50.ltd',
+ 'referer' : 'https://p5.v50.ltd/',
+ 'sec-ch-ua-platform': '"Windows"',
+ 'sec-fetch-dest' : 'empty',
+ 'sec-fetch-mode' : 'cors',
+ 'sec-fetch-site' : 'same-origin',
+ 'user-agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36'
+ }
+ response = requests.post(
+ "https://p5.v50.ltd/api/chat-process",
+ json=payload,
+ headers=headers,
+ proxies=kwargs.get('proxy', {}),
+ )
+
+ if "https://fk1.v50.ltd" not in response.text:
+ yield response.text
\ No newline at end of file
diff --git a/g4f/Provider/deprecated/Vercel.py b/g4f/Provider/deprecated/Vercel.py
new file mode 100644
index 0000000000000000000000000000000000000000..adf7b20884437b92afb80c37219112437f0e7dc3
--- /dev/null
+++ b/g4f/Provider/deprecated/Vercel.py
@@ -0,0 +1,392 @@
+from __future__ import annotations
+
+import json, base64, requests, random, uuid
+
+try:
+ import execjs
+ has_requirements = True
+except ImportError:
+ has_requirements = False
+
+from ...typing import Messages, TypedDict, CreateResult, Any
+from ..base_provider import AbstractProvider
+from ...errors import MissingRequirementsError
+
+class Vercel(AbstractProvider):
+ url = 'https://sdk.vercel.ai'
+ working = False
+ supports_message_history = True
+ supports_gpt_35_turbo = True
+ supports_stream = True
+
+ @staticmethod
+ def create_completion(
+ model: str,
+ messages: Messages,
+ stream: bool,
+ proxy: str = None,
+ **kwargs
+ ) -> CreateResult:
+ if not has_requirements:
+ raise MissingRequirementsError('Install "PyExecJS" package')
+
+ if not model:
+ model = "gpt-3.5-turbo"
+ elif model not in model_info:
+ raise ValueError(f"Vercel does not support {model}")
+
+ headers = {
+ 'authority': 'sdk.vercel.ai',
+ 'accept': '*/*',
+ 'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
+ 'cache-control': 'no-cache',
+ 'content-type': 'application/json',
+ 'custom-encoding': get_anti_bot_token(),
+ 'origin': 'https://sdk.vercel.ai',
+ 'pragma': 'no-cache',
+ 'referer': 'https://sdk.vercel.ai/',
+ 'sec-ch-ua': '"Google Chrome";v="117", "Not;A=Brand";v="8", "Chromium";v="117"',
+ 'sec-ch-ua-mobile': '?0',
+ 'sec-ch-ua-platform': '"macOS"',
+ 'sec-fetch-dest': 'empty',
+ 'sec-fetch-mode': 'cors',
+ 'sec-fetch-site': 'same-origin',
+ 'user-agent': f'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.{random.randint(99, 999)}.{random.randint(99, 999)} Safari/537.36',
+ }
+
+ json_data = {
+ 'model' : model_info[model]['id'],
+ 'messages' : messages,
+ 'playgroundId': str(uuid.uuid4()),
+ 'chatIndex' : 0,
+ **model_info[model]['default_params'],
+ **kwargs
+ }
+
+ max_retries = kwargs.get('max_retries', 20)
+ for _ in range(max_retries):
+ response = requests.post('https://chat.vercel.ai/api/chat',
+ headers=headers, json=json_data, stream=True, proxies={"https": proxy})
+ try:
+ response.raise_for_status()
+ except:
+ continue
+ for token in response.iter_content(chunk_size=None):
+ yield token.decode()
+ break
+
+
+def get_anti_bot_token() -> str:
+ headers = {
+ 'authority': 'sdk.vercel.ai',
+ 'accept': '*/*',
+ 'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
+ 'cache-control': 'no-cache',
+ 'pragma': 'no-cache',
+ 'referer': 'https://sdk.vercel.ai/',
+ 'sec-ch-ua': '"Google Chrome";v="117", "Not;A=Brand";v="8", "Chromium";v="117"',
+ 'sec-ch-ua-mobile': '?0',
+ 'sec-ch-ua-platform': '"macOS"',
+ 'sec-fetch-dest': 'empty',
+ 'sec-fetch-mode': 'cors',
+ 'sec-fetch-site': 'same-origin',
+ 'user-agent': f'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.{random.randint(99, 999)}.{random.randint(99, 999)} Safari/537.36',
+ }
+
+ response = requests.get('https://sdk.vercel.ai/openai.jpeg',
+ headers=headers).text
+
+ raw_data = json.loads(base64.b64decode(response,
+ validate=True))
+
+ js_script = '''const globalThis={marker:"mark"};String.prototype.fontcolor=function(){return `${this}`};
+ return (%s)(%s)''' % (raw_data['c'], raw_data['a'])
+
+ raw_token = json.dumps({'r': execjs.compile(js_script).call(''), 't': raw_data['t']},
+ separators = (",", ":"))
+
+ return base64.b64encode(raw_token.encode('utf-16le')).decode()
+
+class ModelInfo(TypedDict):
+ id: str
+ default_params: dict[str, Any]
+
+model_info: dict[str, ModelInfo] = {
+ # 'claude-instant-v1': {
+ # 'id': 'anthropic:claude-instant-v1',
+ # 'default_params': {
+ # 'temperature': 1,
+ # 'maximumLength': 1024,
+ # 'topP': 1,
+ # 'topK': 1,
+ # 'presencePenalty': 1,
+ # 'frequencyPenalty': 1,
+ # 'stopSequences': ['\n\nHuman:'],
+ # },
+ # },
+ # 'claude-v1': {
+ # 'id': 'anthropic:claude-v1',
+ # 'default_params': {
+ # 'temperature': 1,
+ # 'maximumLength': 1024,
+ # 'topP': 1,
+ # 'topK': 1,
+ # 'presencePenalty': 1,
+ # 'frequencyPenalty': 1,
+ # 'stopSequences': ['\n\nHuman:'],
+ # },
+ # },
+ # 'claude-v2': {
+ # 'id': 'anthropic:claude-v2',
+ # 'default_params': {
+ # 'temperature': 1,
+ # 'maximumLength': 1024,
+ # 'topP': 1,
+ # 'topK': 1,
+ # 'presencePenalty': 1,
+ # 'frequencyPenalty': 1,
+ # 'stopSequences': ['\n\nHuman:'],
+ # },
+ # },
+ 'replicate/llama70b-v2-chat': {
+ 'id': 'replicate:replicate/llama-2-70b-chat',
+ 'default_params': {
+ 'temperature': 0.75,
+ 'maximumLength': 3000,
+ 'topP': 1,
+ 'repetitionPenalty': 1,
+ },
+ },
+ 'a16z-infra/llama7b-v2-chat': {
+ 'id': 'replicate:a16z-infra/llama7b-v2-chat',
+ 'default_params': {
+ 'temperature': 0.75,
+ 'maximumLength': 3000,
+ 'topP': 1,
+ 'repetitionPenalty': 1,
+ },
+ },
+ 'a16z-infra/llama13b-v2-chat': {
+ 'id': 'replicate:a16z-infra/llama13b-v2-chat',
+ 'default_params': {
+ 'temperature': 0.75,
+ 'maximumLength': 3000,
+ 'topP': 1,
+ 'repetitionPenalty': 1,
+ },
+ },
+ 'replicate/llama-2-70b-chat': {
+ 'id': 'replicate:replicate/llama-2-70b-chat',
+ 'default_params': {
+ 'temperature': 0.75,
+ 'maximumLength': 3000,
+ 'topP': 1,
+ 'repetitionPenalty': 1,
+ },
+ },
+ 'bigscience/bloom': {
+ 'id': 'huggingface:bigscience/bloom',
+ 'default_params': {
+ 'temperature': 0.5,
+ 'maximumLength': 1024,
+ 'topP': 0.95,
+ 'topK': 4,
+ 'repetitionPenalty': 1.03,
+ },
+ },
+ 'google/flan-t5-xxl': {
+ 'id': 'huggingface:google/flan-t5-xxl',
+ 'default_params': {
+ 'temperature': 0.5,
+ 'maximumLength': 1024,
+ 'topP': 0.95,
+ 'topK': 4,
+ 'repetitionPenalty': 1.03,
+ },
+ },
+ 'EleutherAI/gpt-neox-20b': {
+ 'id': 'huggingface:EleutherAI/gpt-neox-20b',
+ 'default_params': {
+ 'temperature': 0.5,
+ 'maximumLength': 1024,
+ 'topP': 0.95,
+ 'topK': 4,
+ 'repetitionPenalty': 1.03,
+ 'stopSequences': [],
+ },
+ },
+ 'OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5': {
+ 'id': 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5',
+ 'default_params': {
+ 'maximumLength': 1024,
+ 'typicalP': 0.2,
+ 'repetitionPenalty': 1,
+ },
+ },
+ 'OpenAssistant/oasst-sft-1-pythia-12b': {
+ 'id': 'huggingface:OpenAssistant/oasst-sft-1-pythia-12b',
+ 'default_params': {
+ 'maximumLength': 1024,
+ 'typicalP': 0.2,
+ 'repetitionPenalty': 1,
+ },
+ },
+ 'bigcode/santacoder': {
+ 'id': 'huggingface:bigcode/santacoder',
+ 'default_params': {
+ 'temperature': 0.5,
+ 'maximumLength': 1024,
+ 'topP': 0.95,
+ 'topK': 4,
+ 'repetitionPenalty': 1.03,
+ },
+ },
+ 'command-light-nightly': {
+ 'id': 'cohere:command-light-nightly',
+ 'default_params': {
+ 'temperature': 0.9,
+ 'maximumLength': 1024,
+ 'topP': 1,
+ 'topK': 0,
+ 'presencePenalty': 0,
+ 'frequencyPenalty': 0,
+ 'stopSequences': [],
+ },
+ },
+ 'command-nightly': {
+ 'id': 'cohere:command-nightly',
+ 'default_params': {
+ 'temperature': 0.9,
+ 'maximumLength': 1024,
+ 'topP': 1,
+ 'topK': 0,
+ 'presencePenalty': 0,
+ 'frequencyPenalty': 0,
+ 'stopSequences': [],
+ },
+ },
+ # 'gpt-4': {
+ # 'id': 'openai:gpt-4',
+ # 'default_params': {
+ # 'temperature': 0.7,
+ # 'maximumLength': 8192,
+ # 'topP': 1,
+ # 'presencePenalty': 0,
+ # 'frequencyPenalty': 0,
+ # 'stopSequences': [],
+ # },
+ # },
+ # 'gpt-4-0613': {
+ # 'id': 'openai:gpt-4-0613',
+ # 'default_params': {
+ # 'temperature': 0.7,
+ # 'maximumLength': 8192,
+ # 'topP': 1,
+ # 'presencePenalty': 0,
+ # 'frequencyPenalty': 0,
+ # 'stopSequences': [],
+ # },
+ # },
+ 'code-davinci-002': {
+ 'id': 'openai:code-davinci-002',
+ 'default_params': {
+ 'temperature': 0.5,
+ 'maximumLength': 1024,
+ 'topP': 1,
+ 'presencePenalty': 0,
+ 'frequencyPenalty': 0,
+ 'stopSequences': [],
+ },
+ },
+ 'gpt-3.5-turbo': {
+ 'id': 'openai:gpt-3.5-turbo',
+ 'default_params': {
+ 'temperature': 0.7,
+ 'maximumLength': 4096,
+ 'topP': 1,
+ 'topK': 1,
+ 'presencePenalty': 1,
+ 'frequencyPenalty': 1,
+ 'stopSequences': [],
+ },
+ },
+ 'gpt-3.5-turbo-16k': {
+ 'id': 'openai:gpt-3.5-turbo-16k',
+ 'default_params': {
+ 'temperature': 0.7,
+ 'maximumLength': 16280,
+ 'topP': 1,
+ 'topK': 1,
+ 'presencePenalty': 1,
+ 'frequencyPenalty': 1,
+ 'stopSequences': [],
+ },
+ },
+ 'gpt-3.5-turbo-16k-0613': {
+ 'id': 'openai:gpt-3.5-turbo-16k-0613',
+ 'default_params': {
+ 'temperature': 0.7,
+ 'maximumLength': 16280,
+ 'topP': 1,
+ 'topK': 1,
+ 'presencePenalty': 1,
+ 'frequencyPenalty': 1,
+ 'stopSequences': [],
+ },
+ },
+ 'text-ada-001': {
+ 'id': 'openai:text-ada-001',
+ 'default_params': {
+ 'temperature': 0.5,
+ 'maximumLength': 1024,
+ 'topP': 1,
+ 'presencePenalty': 0,
+ 'frequencyPenalty': 0,
+ 'stopSequences': [],
+ },
+ },
+ 'text-babbage-001': {
+ 'id': 'openai:text-babbage-001',
+ 'default_params': {
+ 'temperature': 0.5,
+ 'maximumLength': 1024,
+ 'topP': 1,
+ 'presencePenalty': 0,
+ 'frequencyPenalty': 0,
+ 'stopSequences': [],
+ },
+ },
+ 'text-curie-001': {
+ 'id': 'openai:text-curie-001',
+ 'default_params': {
+ 'temperature': 0.5,
+ 'maximumLength': 1024,
+ 'topP': 1,
+ 'presencePenalty': 0,
+ 'frequencyPenalty': 0,
+ 'stopSequences': [],
+ },
+ },
+ 'text-davinci-002': {
+ 'id': 'openai:text-davinci-002',
+ 'default_params': {
+ 'temperature': 0.5,
+ 'maximumLength': 1024,
+ 'topP': 1,
+ 'presencePenalty': 0,
+ 'frequencyPenalty': 0,
+ 'stopSequences': [],
+ },
+ },
+ 'text-davinci-003': {
+ 'id': 'openai:text-davinci-003',
+ 'default_params': {
+ 'temperature': 0.5,
+ 'maximumLength': 4097,
+ 'topP': 1,
+ 'presencePenalty': 0,
+ 'frequencyPenalty': 0,
+ 'stopSequences': [],
+ },
+ },
+}
\ No newline at end of file
diff --git a/g4f/Provider/deprecated/Vitalentum.py b/g4f/Provider/deprecated/Vitalentum.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f466a52d78e1728f01c3598ac29dbf27b4adbbd
--- /dev/null
+++ b/g4f/Provider/deprecated/Vitalentum.py
@@ -0,0 +1,55 @@
+from __future__ import annotations
+
+import json
+from aiohttp import ClientSession
+
+from ..base_provider import AsyncGeneratorProvider
+from ...typing import AsyncResult, Messages
+
+class Vitalentum(AsyncGeneratorProvider):
+ url = "https://app.vitalentum.io"
+ supports_gpt_35_turbo = True
+
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ **kwargs
+ ) -> AsyncResult:
+ headers = {
+ "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36",
+ "Accept": "text/event-stream",
+ "Accept-language": "de,en-US;q=0.7,en;q=0.3",
+ "Origin": cls.url,
+ "Referer": f"{cls.url}/",
+ "Sec-Fetch-Dest": "empty",
+ "Sec-Fetch-Mode": "cors",
+ "Sec-Fetch-Site": "same-origin",
+ }
+ conversation = json.dumps({"history": [{
+ "speaker": "human" if message["role"] == "user" else "bot",
+ "text": message["content"],
+ } for message in messages]})
+ data = {
+ "conversation": conversation,
+ "temperature": 0.7,
+ **kwargs
+ }
+ async with ClientSession(
+ headers=headers
+ ) as session:
+ async with session.post(f"{cls.url}/api/converse-edge", json=data, proxy=proxy) as response:
+ response.raise_for_status()
+ async for line in response.content:
+ line = line.decode()
+ if line.startswith("data: "):
+ if line.startswith("data: [DONE]"):
+ break
+ line = json.loads(line[6:-1])
+ content = line["choices"][0]["delta"].get("content")
+
+ if content:
+ yield content
\ No newline at end of file
diff --git a/g4f/Provider/deprecated/VoiGpt.py b/g4f/Provider/deprecated/VoiGpt.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b061e63b3adcded3b18fd9f43eaaba79eaed6a4
--- /dev/null
+++ b/g4f/Provider/deprecated/VoiGpt.py
@@ -0,0 +1,91 @@
+from __future__ import annotations
+
+import json
+import requests
+from ..base_provider import AbstractProvider
+from ...typing import Messages, CreateResult
+
+
+class VoiGpt(AbstractProvider):
+ """
+ VoiGpt - A provider for VoiGpt.com
+
+ **Note** : to use this provider you have to get your csrf token/cookie from the voigpt.com website
+
+ Args:
+ model: The model to use
+ messages: The messages to send
+ stream: Whether to stream the response
+ proxy: The proxy to use
+ access_token: The access token to use
+ **kwargs: Additional keyword arguments
+
+ Returns:
+ A CreateResult object
+ """
+ url = "https://voigpt.com"
+ working = False
+ supports_gpt_35_turbo = True
+ supports_message_history = True
+ supports_stream = False
+ _access_token: str = None
+
+ @classmethod
+ def create_completion(
+ cls,
+ model: str,
+ messages: Messages,
+ stream: bool,
+ proxy: str = None,
+ access_token: str = None,
+ **kwargs
+ ) -> CreateResult:
+
+ if not model:
+ model = "gpt-3.5-turbo"
+ if not access_token:
+ access_token = cls._access_token
+ if not access_token:
+ headers = {
+ "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
+ "accept-language": "de-DE,de;q=0.9,en-DE;q=0.8,en;q=0.7,en-US;q=0.6",
+ "sec-ch-ua": "\"Google Chrome\";v=\"119\", \"Chromium\";v=\"119\", \"Not?A_Brand\";v=\"24\"",
+ "sec-ch-ua-mobile": "?0",
+ "sec-ch-ua-platform": "\"Linux\"",
+ "sec-fetch-dest": "document",
+ "sec-fetch-mode": "navigate",
+ "sec-fetch-site": "none",
+ "sec-fetch-user": "?1",
+ "upgrade-insecure-requests": "1",
+ "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36",
+ }
+ req_response = requests.get(cls.url, headers=headers)
+ access_token = cls._access_token = req_response.cookies.get("csrftoken")
+
+ headers = {
+ "Accept-Encoding": "gzip, deflate, br",
+ "Accept-Language": "de-DE,de;q=0.9,en-DE;q=0.8,en;q=0.7,en-US;q=0.6",
+ "Cookie": f"csrftoken={access_token};",
+ "Origin": "https://voigpt.com",
+ "Referer": "https://voigpt.com/",
+ "Sec-Ch-Ua": "'Google Chrome';v='119', 'Chromium';v='119', 'Not?A_Brand';v='24'",
+ "Sec-Ch-Ua-Mobile": "?0",
+ "Sec-Ch-Ua-Platform": "'Windows'",
+ "Sec-Fetch-Dest": "empty",
+ "Sec-Fetch-Mode": "cors",
+ "Sec-Fetch-Site": "same-origin",
+ "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36",
+ "X-Csrftoken": access_token,
+ }
+
+ payload = {
+ "messages": messages,
+ }
+ request_url = f"{cls.url}/generate_response/"
+ req_response = requests.post(request_url, headers=headers, json=payload)
+ try:
+ response = json.loads(req_response.text)
+ yield response["response"]
+ except:
+ raise RuntimeError(f"Response: {req_response.text}")
+
diff --git a/g4f/Provider/deprecated/Wewordle.py b/g4f/Provider/deprecated/Wewordle.py
new file mode 100644
index 0000000000000000000000000000000000000000..c30887fb03b3ee53ed620d3e8259ae2a9245f934
--- /dev/null
+++ b/g4f/Provider/deprecated/Wewordle.py
@@ -0,0 +1,65 @@
+from __future__ import annotations
+
+import random, string, time
+from aiohttp import ClientSession
+
+from ..base_provider import AsyncProvider
+
+
+class Wewordle(AsyncProvider):
+ url = "https://wewordle.org"
+ working = False
+ supports_gpt_35_turbo = True
+
+ @classmethod
+ async def create_async(
+ cls,
+ model: str,
+ messages: list[dict[str, str]],
+ proxy: str = None,
+ **kwargs
+ ) -> str:
+
+ headers = {
+ "accept" : "*/*",
+ "pragma" : "no-cache",
+ "Content-Type" : "application/json",
+ "Connection" : "keep-alive"
+ }
+
+ _user_id = "".join(random.choices(f"{string.ascii_lowercase}{string.digits}", k=16))
+ _app_id = "".join(random.choices(f"{string.ascii_lowercase}{string.digits}", k=31))
+ _request_date = time.strftime("%Y-%m-%dT%H:%M:%S.000Z", time.gmtime())
+ data = {
+ "user" : _user_id,
+ "messages" : messages,
+ "subscriber": {
+ "originalPurchaseDate" : None,
+ "originalApplicationVersion" : None,
+ "allPurchaseDatesMillis" : {},
+ "entitlements" : {"active": {}, "all": {}},
+ "allPurchaseDates" : {},
+ "allExpirationDatesMillis" : {},
+ "allExpirationDates" : {},
+ "originalAppUserId" : f"$RCAnonymousID:{_app_id}",
+ "latestExpirationDate" : None,
+ "requestDate" : _request_date,
+ "latestExpirationDateMillis" : None,
+ "nonSubscriptionTransactions" : [],
+ "originalPurchaseDateMillis" : None,
+ "managementURL" : None,
+ "allPurchasedProductIdentifiers": [],
+ "firstSeen" : _request_date,
+ "activeSubscriptions" : [],
+ }
+ }
+
+
+ async with ClientSession(
+ headers=headers
+ ) as session:
+ async with session.post(f"{cls.url}/gptapi/v1/android/turbo", proxy=proxy, json=data) as response:
+ response.raise_for_status()
+ content = (await response.json())["message"]["content"]
+ if content:
+ return content
\ No newline at end of file
diff --git a/g4f/Provider/deprecated/Wuguokai.py b/g4f/Provider/deprecated/Wuguokai.py
new file mode 100644
index 0000000000000000000000000000000000000000..f12d1bfe3bf90e1a9bbbbe4bae0f7b90336b82af
--- /dev/null
+++ b/g4f/Provider/deprecated/Wuguokai.py
@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+import random
+
+import requests
+
+from ...typing import Any, CreateResult
+from ..base_provider import AbstractProvider, format_prompt
+
+
+class Wuguokai(AbstractProvider):
+ url = 'https://chat.wuguokai.xyz'
+ supports_gpt_35_turbo = True
+ working = False
+
+ @staticmethod
+ def create_completion(
+ model: str,
+ messages: list[dict[str, str]],
+ stream: bool,
+ **kwargs: Any,
+ ) -> CreateResult:
+ headers = {
+ 'authority': 'ai-api.wuguokai.xyz',
+ 'accept': 'application/json, text/plain, */*',
+ 'accept-language': 'id-ID,id;q=0.9,en-US;q=0.8,en;q=0.7',
+ 'content-type': 'application/json',
+ 'origin': 'https://chat.wuguokai.xyz',
+ 'referer': 'https://chat.wuguokai.xyz/',
+ 'sec-ch-ua': '"Not.A/Brand";v="8", "Chromium";v="114", "Google Chrome";v="114"',
+ 'sec-ch-ua-mobile': '?0',
+ 'sec-ch-ua-platform': '"Windows"',
+ 'sec-fetch-dest': 'empty',
+ 'sec-fetch-mode': 'cors',
+ 'sec-fetch-site': 'same-site',
+ 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36'
+ }
+ data ={
+ "prompt": format_prompt(messages),
+ "options": {},
+ "userId": f"#/chat/{random.randint(1,99999999)}",
+ "usingContext": True
+ }
+ response = requests.post(
+ "https://ai-api20.wuguokai.xyz/api/chat-process",
+ headers=headers,
+ timeout=3,
+ json=data,
+ proxies=kwargs.get('proxy', {}),
+ )
+ _split = response.text.split("> č„åēå¤±č“„čÆ·éčÆęå¤å·ę°å ꬔēé¢åéčÆ")
+ if response.status_code != 200:
+ raise Exception(f"Error: {response.status_code} {response.reason}")
+ if len(_split) > 1:
+ yield _split[1].strip()
+ else:
+ yield _split[0].strip()
\ No newline at end of file
diff --git a/g4f/Provider/deprecated/Ylokh.py b/g4f/Provider/deprecated/Ylokh.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbff460219ea65ce1eac0ccf141c32e28e5ab823
--- /dev/null
+++ b/g4f/Provider/deprecated/Ylokh.py
@@ -0,0 +1,58 @@
+from __future__ import annotations
+
+import json
+
+from ...requests import StreamSession
+from ..base_provider import AsyncGeneratorProvider
+from ...typing import AsyncResult, Messages
+
+class Ylokh(AsyncGeneratorProvider):
+ url = "https://chat.ylokh.xyz"
+ working = False
+ supports_message_history = True
+ supports_gpt_35_turbo = True
+
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ stream: bool = True,
+ proxy: str = None,
+ timeout: int = 120,
+ **kwargs
+ ) -> AsyncResult:
+ model = model if model else "gpt-3.5-turbo"
+ headers = {"Origin": cls.url, "Referer": f"{cls.url}/"}
+ data = {
+ "messages": messages,
+ "model": model,
+ "temperature": 1,
+ "presence_penalty": 0,
+ "top_p": 1,
+ "frequency_penalty": 0,
+ "allow_fallback": True,
+ "stream": stream,
+ **kwargs
+ }
+ async with StreamSession(
+ headers=headers,
+ proxies={"https": proxy},
+ timeout=timeout
+ ) as session:
+ async with session.post("https://chatapi.ylokh.xyz/v1/chat/completions", json=data) as response:
+ response.raise_for_status()
+ if stream:
+ async for line in response.iter_lines():
+ line = line.decode()
+ if line.startswith("data: "):
+ if line.startswith("data: [DONE]"):
+ break
+ line = json.loads(line[6:])
+ content = line["choices"][0]["delta"].get("content")
+ if content:
+ yield content
+ else:
+ chat = await response.json()
+ yield chat["choices"][0]["message"].get("content")
\ No newline at end of file
diff --git a/g4f/Provider/deprecated/Yqcloud.py b/g4f/Provider/deprecated/Yqcloud.py
new file mode 100644
index 0000000000000000000000000000000000000000..227f89951c6eeebae37db6e88f3595f2e8ce23d9
--- /dev/null
+++ b/g4f/Provider/deprecated/Yqcloud.py
@@ -0,0 +1,61 @@
+from __future__ import annotations
+
+import random
+from ...requests import StreamSession
+
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, format_prompt
+
+
+class Yqcloud(AsyncGeneratorProvider):
+ url = "https://chat9.yqcloud.top/"
+ working = False
+ supports_gpt_35_turbo = True
+
+ @staticmethod
+ async def create_async_generator(
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ timeout: int = 120,
+ **kwargs,
+ ) -> AsyncResult:
+ async with StreamSession(
+ headers=_create_header(), proxies={"https": proxy}, timeout=timeout
+ ) as session:
+ payload = _create_payload(messages, **kwargs)
+ async with session.post("https://api.aichatos.cloud/api/generateStream", json=payload) as response:
+ response.raise_for_status()
+ async for chunk in response.iter_content():
+ if chunk:
+ chunk = chunk.decode()
+ if "sorry, ęØēipå·²ē±äŗ触åé²ę»„ēØę£ęµčč¢«å°ē¦" in chunk:
+ raise RuntimeError("IP address is blocked by abuse detection.")
+ yield chunk
+
+
+def _create_header():
+ return {
+ "accept" : "application/json, text/plain, */*",
+ "content-type" : "application/json",
+ "origin" : "https://chat9.yqcloud.top",
+ "referer" : "https://chat9.yqcloud.top/"
+ }
+
+
+def _create_payload(
+ messages: Messages,
+ system_message: str = "",
+ user_id: int = None,
+ **kwargs
+):
+ if not user_id:
+ user_id = random.randint(1690000544336, 2093025544336)
+ return {
+ "prompt": format_prompt(messages),
+ "network": True,
+ "system": system_message,
+ "withoutContext": False,
+ "stream": True,
+ "userId": f"#/chat/{user_id}"
+ }
diff --git a/g4f/Provider/deprecated/__init__.py b/g4f/Provider/deprecated/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..afb636e89b08dede4261cc224f87fab75cc434c1
--- /dev/null
+++ b/g4f/Provider/deprecated/__init__.py
@@ -0,0 +1,35 @@
+from .AiService import AiService
+from .CodeLinkAva import CodeLinkAva
+from .DfeHub import DfeHub
+from .EasyChat import EasyChat
+from .Forefront import Forefront
+from .GetGpt import GetGpt
+from .Lockchat import Lockchat
+from .Wewordle import Wewordle
+from .Equing import Equing
+from .Wuguokai import Wuguokai
+from .V50 import V50
+from .FastGpt import FastGpt
+from .Aivvm import Aivvm
+from .Vitalentum import Vitalentum
+from .H2o import H2o
+from .Myshell import Myshell
+from .Acytoo import Acytoo
+from .Aibn import Aibn
+from .Ails import Ails
+from .ChatgptDuo import ChatgptDuo
+from .Cromicle import Cromicle
+from .Opchatgpts import Opchatgpts
+from .Yqcloud import Yqcloud
+from .Aichat import Aichat
+from .Berlin import Berlin
+from .Phind import Phind
+from .AiAsk import AiAsk
+from .ChatAnywhere import ChatAnywhere
+from .FakeGpt import FakeGpt
+from .GeekGpt import GeekGpt
+from .GPTalk import GPTalk
+from .Hashnode import Hashnode
+from .Ylokh import Ylokh
+from .OpenAssistant import OpenAssistant
+from .Bing import Bing
\ No newline at end of file
diff --git a/g4f/Provider/helper.py b/g4f/Provider/helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..338e0966b894f6ce78733812c945fb9cb5da041f
--- /dev/null
+++ b/g4f/Provider/helper.py
@@ -0,0 +1,3 @@
+from ..providers.helper import *
+from ..cookies import get_cookies
+from ..requests.aiohttp import get_connector
\ No newline at end of file
diff --git a/g4f/Provider/local/Local.py b/g4f/Provider/local/Local.py
new file mode 100644
index 0000000000000000000000000000000000000000..4dc6e3f989eeff9efe5d83985516c0044b88e171
--- /dev/null
+++ b/g4f/Provider/local/Local.py
@@ -0,0 +1,43 @@
+from __future__ import annotations
+
+from ...locals.models import get_models
+try:
+ from ...locals.provider import LocalProvider
+ has_requirements = True
+except ImportError:
+ has_requirements = False
+
+from ...typing import Messages, CreateResult
+from ...providers.base_provider import AbstractProvider, ProviderModelMixin
+from ...errors import MissingRequirementsError
+
+class Local(AbstractProvider, ProviderModelMixin):
+ label = "GPT4All"
+ working = True
+ supports_message_history = True
+ supports_system_message = True
+ supports_stream = True
+
+ @classmethod
+ def get_models(cls):
+ if not cls.models:
+ cls.models = list(get_models())
+ cls.default_model = cls.models[0]
+ return cls.models
+
+ @classmethod
+ def create_completion(
+ cls,
+ model: str,
+ messages: Messages,
+ stream: bool,
+ **kwargs
+ ) -> CreateResult:
+ if not has_requirements:
+ raise MissingRequirementsError('Install "gpt4all" package | pip install -U g4f[local]')
+ return LocalProvider.create_completion(
+ cls.get_model(model),
+ messages,
+ stream,
+ **kwargs
+ )
diff --git a/g4f/Provider/local/Ollama.py b/g4f/Provider/local/Ollama.py
new file mode 100644
index 0000000000000000000000000000000000000000..de68a21804a43cebe17a4fbb23785bf51c403988
--- /dev/null
+++ b/g4f/Provider/local/Ollama.py
@@ -0,0 +1,40 @@
+from __future__ import annotations
+
+import requests
+import os
+
+from ..needs_auth.OpenaiAPI import OpenaiAPI
+from ...typing import AsyncResult, Messages
+
+class Ollama(OpenaiAPI):
+ label = "Ollama"
+ url = "https://ollama.com"
+ needs_auth = False
+ working = True
+
+ @classmethod
+ def get_models(cls):
+ if not cls.models:
+ host = os.getenv("OLLAMA_HOST", "127.0.0.1")
+ port = os.getenv("OLLAMA_PORT", "11434")
+ url = f"http://{host}:{port}/api/tags"
+ models = requests.get(url).json()["models"]
+ cls.models = [model["name"] for model in models]
+ cls.default_model = cls.models[0]
+ return cls.models
+
+ @classmethod
+ def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ api_base: str = None,
+ **kwargs
+ ) -> AsyncResult:
+ if not api_base:
+ host = os.getenv("OLLAMA_HOST", "localhost")
+ port = os.getenv("OLLAMA_PORT", "11434")
+ api_base: str = f"http://{host}:{port}/v1"
+ return super().create_async_generator(
+ model, messages, api_base=api_base, **kwargs
+ )
diff --git a/g4f/Provider/local/__init__.py b/g4f/Provider/local/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..05f6022e589ae1b60bd888cc214a69a4d11b7a63
--- /dev/null
+++ b/g4f/Provider/local/__init__.py
@@ -0,0 +1,2 @@
+from .Local import Local
+from .Ollama import Ollama
diff --git a/g4f/Provider/needs_auth/BingCreateImages.py b/g4f/Provider/needs_auth/BingCreateImages.py
new file mode 100644
index 0000000000000000000000000000000000000000..b95a78c3b7a4ac38b213b30f6c5e362c8e77d99b
--- /dev/null
+++ b/g4f/Provider/needs_auth/BingCreateImages.py
@@ -0,0 +1,55 @@
+from __future__ import annotations
+
+from ...cookies import get_cookies
+from ...image import ImageResponse
+from ...errors import MissingAuthError
+from ...typing import AsyncResult, Messages, Cookies
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..bing.create_images import create_images, create_session
+
+class BingCreateImages(AsyncGeneratorProvider, ProviderModelMixin):
+ label = "Microsoft Designer in Bing"
+ parent = "Bing"
+ url = "https://www.bing.com/images/create"
+ working = True
+ needs_auth = True
+ image_models = ["dall-e"]
+
+ def __init__(self, cookies: Cookies = None, proxy: str = None, api_key: str = None) -> None:
+ if api_key is not None:
+ if cookies is None:
+ cookies = {}
+ cookies["_U"] = api_key
+ self.cookies = cookies
+ self.proxy = proxy
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ prompt: str = None,
+ api_key: str = None,
+ cookies: Cookies = None,
+ proxy: str = None,
+ **kwargs
+ ) -> AsyncResult:
+ session = BingCreateImages(cookies, proxy, api_key)
+ yield await session.generate(messages[-1]["content"] if prompt is None else prompt)
+
+ async def generate(self, prompt: str) -> ImageResponse:
+ """
+ Asynchronously creates a markdown formatted string with images based on the prompt.
+
+ Args:
+ prompt (str): Prompt to generate images.
+
+ Returns:
+ str: Markdown formatted string with images.
+ """
+ cookies = self.cookies or get_cookies(".bing.com", False)
+ if cookies is None or "_U" not in cookies:
+ raise MissingAuthError('Missing "_U" cookie')
+ async with create_session(cookies, self.proxy) as session:
+ images = await create_images(session, prompt)
+ return ImageResponse(images, prompt, {"preview": "{image}?w=200&h=200"} if len(images) > 1 else {})
\ No newline at end of file
diff --git a/g4f/Provider/needs_auth/Cerebras.py b/g4f/Provider/needs_auth/Cerebras.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f94c476a0c9b460eaf6bce24cd35ae6d9f1444b
--- /dev/null
+++ b/g4f/Provider/needs_auth/Cerebras.py
@@ -0,0 +1,65 @@
+from __future__ import annotations
+
+import requests
+from aiohttp import ClientSession
+
+from .OpenaiAPI import OpenaiAPI
+from ...typing import AsyncResult, Messages, Cookies
+from ...requests.raise_for_status import raise_for_status
+from ...cookies import get_cookies
+
+class Cerebras(OpenaiAPI):
+ label = "Cerebras Inference"
+ url = "https://inference.cerebras.ai/"
+ working = True
+ default_model = "llama3.1-70b"
+ fallback_models = [
+ "llama3.1-70b",
+ "llama3.1-8b",
+ ]
+ model_aliases = {"llama-3.1-70b": "llama3.1-70b", "llama-3.1-8b": "llama3.1-8b"}
+
+ @classmethod
+ def get_models(cls, api_key: str = None):
+ if not cls.models:
+ try:
+ headers = {}
+ if api_key:
+ headers["authorization"] = f"Bearer ${api_key}"
+ response = requests.get(f"https://api.cerebras.ai/v1/models", headers=headers)
+ raise_for_status(response)
+ data = response.json()
+ cls.models = [model.get("model") for model in data.get("models")]
+ except Exception:
+ cls.models = cls.fallback_models
+ return cls.models
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ api_base: str = "https://api.cerebras.ai/v1",
+ api_key: str = None,
+ cookies: Cookies = None,
+ **kwargs
+ ) -> AsyncResult:
+ if api_key is None and cookies is None:
+ cookies = get_cookies(".cerebras.ai")
+ async with ClientSession(cookies=cookies) as session:
+ async with session.get("https://inference.cerebras.ai/api/auth/session") as response:
+ raise_for_status(response)
+ data = await response.json()
+ if data:
+ api_key = data.get("user", {}).get("demoApiKey")
+ async for chunk in super().create_async_generator(
+ model, messages,
+ api_base=api_base,
+ impersonate="chrome",
+ api_key=api_key,
+ headers={
+ "User-Agent": "ex/JS 1.5.0",
+ },
+ **kwargs
+ ):
+ yield chunk
diff --git a/g4f/Provider/needs_auth/CopilotAccount.py b/g4f/Provider/needs_auth/CopilotAccount.py
new file mode 100644
index 0000000000000000000000000000000000000000..497aab989f043ac0ea50fe5d5b0e04861f772974
--- /dev/null
+++ b/g4f/Provider/needs_auth/CopilotAccount.py
@@ -0,0 +1,12 @@
+from __future__ import annotations
+
+from ..base_provider import ProviderModelMixin
+from ..Copilot import Copilot
+
+class CopilotAccount(Copilot, ProviderModelMixin):
+ needs_auth = True
+ parent = "Copilot"
+ default_model = "Copilot"
+ default_vision_model = default_model
+ models = [default_model]
+ image_models = models
\ No newline at end of file
diff --git a/g4f/Provider/needs_auth/DeepInfra.py b/g4f/Provider/needs_auth/DeepInfra.py
new file mode 100644
index 0000000000000000000000000000000000000000..35e7ca7f85e8d7a5a225350be2fea2e9f506936d
--- /dev/null
+++ b/g4f/Provider/needs_auth/DeepInfra.py
@@ -0,0 +1,58 @@
+from __future__ import annotations
+
+import requests
+from ...typing import AsyncResult, Messages
+from .OpenaiAPI import OpenaiAPI
+
+class DeepInfra(OpenaiAPI):
+ label = "DeepInfra"
+ url = "https://deepinfra.com"
+ working = True
+ needs_auth = True
+ supports_stream = True
+ supports_message_history = True
+ default_model = "meta-llama/Meta-Llama-3.1-70B-Instruct"
+
+ @classmethod
+ def get_models(cls):
+ if not cls.models:
+ url = 'https://api.deepinfra.com/models/featured'
+ models = requests.get(url).json()
+ cls.models = [model['model_name'] for model in models if model["type"] == "text-generation"]
+ return cls.models
+
+ @classmethod
+ def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ stream: bool,
+ api_base: str = "https://api.deepinfra.com/v1/openai",
+ temperature: float = 0.7,
+ max_tokens: int = 1028,
+ **kwargs
+ ) -> AsyncResult:
+ headers = {
+ 'Accept-Encoding': 'gzip, deflate, br',
+ 'Accept-Language': 'en-US',
+ 'Connection': 'keep-alive',
+ 'Origin': 'https://deepinfra.com',
+ 'Referer': 'https://deepinfra.com/',
+ 'Sec-Fetch-Dest': 'empty',
+ 'Sec-Fetch-Mode': 'cors',
+ 'Sec-Fetch-Site': 'same-site',
+ 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36',
+ 'X-Deepinfra-Source': 'web-embed',
+ 'sec-ch-ua': '"Google Chrome";v="119", "Chromium";v="119", "Not?A_Brand";v="24"',
+ 'sec-ch-ua-mobile': '?0',
+ 'sec-ch-ua-platform': '"macOS"',
+ }
+ return super().create_async_generator(
+ model, messages,
+ stream=stream,
+ api_base=api_base,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ headers=headers,
+ **kwargs
+ )
diff --git a/g4f/Provider/needs_auth/DeepInfraImage.py b/g4f/Provider/needs_auth/DeepInfraImage.py
new file mode 100644
index 0000000000000000000000000000000000000000..4479056117ec802746b1e1c998e73b5fe04721a3
--- /dev/null
+++ b/g4f/Provider/needs_auth/DeepInfraImage.py
@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+import requests
+
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ...typing import AsyncResult, Messages
+from ...requests import StreamSession, raise_for_status
+from ...image import ImageResponse
+
+class DeepInfraImage(AsyncGeneratorProvider, ProviderModelMixin):
+ url = "https://deepinfra.com"
+ parent = "DeepInfra"
+ working = True
+ needs_auth = True
+ default_model = ''
+ image_models = [default_model]
+
+ @classmethod
+ def get_models(cls):
+ if not cls.models:
+ url = 'https://api.deepinfra.com/models/featured'
+ models = requests.get(url).json()
+ cls.models = [model['model_name'] for model in models if model["reported_type"] == "text-to-image"]
+ cls.image_models = cls.models
+ return cls.models
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ prompt: str = None,
+ **kwargs
+ ) -> AsyncResult:
+ yield await cls.create_async(messages[-1]["content"] if prompt is None else prompt, model, **kwargs)
+
+ @classmethod
+ async def create_async(
+ cls,
+ prompt: str,
+ model: str,
+ api_key: str = None,
+ api_base: str = "https://api.deepinfra.com/v1/inference",
+ proxy: str = None,
+ timeout: int = 180,
+ extra_data: dict = {},
+ **kwargs
+ ) -> ImageResponse:
+ headers = {
+ 'Accept-Encoding': 'gzip, deflate, br',
+ 'Accept-Language': 'en-US',
+ 'Connection': 'keep-alive',
+ 'Origin': 'https://deepinfra.com',
+ 'Referer': 'https://deepinfra.com/',
+ 'Sec-Fetch-Dest': 'empty',
+ 'Sec-Fetch-Mode': 'cors',
+ 'Sec-Fetch-Site': 'same-site',
+ 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36',
+ 'X-Deepinfra-Source': 'web-embed',
+ 'sec-ch-ua': '"Google Chrome";v="119", "Chromium";v="119", "Not?A_Brand";v="24"',
+ 'sec-ch-ua-mobile': '?0',
+ 'sec-ch-ua-platform': '"macOS"',
+ }
+ if api_key is not None:
+ headers["Authorization"] = f"Bearer {api_key}"
+ async with StreamSession(
+ proxies={"all": proxy},
+ headers=headers,
+ timeout=timeout
+ ) as session:
+ model = cls.get_model(model)
+ data = {"prompt": prompt, **extra_data}
+ data = {"input": data} if model == cls.default_model else data
+ async with session.post(f"{api_base.rstrip('/')}/{model}", json=data) as response:
+ await raise_for_status(response)
+ data = await response.json()
+ images = data.get("output", data.get("images"))
+ if not images:
+ raise RuntimeError(f"Response: {data}")
+ images = images[0] if len(images) == 1 else images
+ return ImageResponse(images, prompt)
diff --git a/g4f/Provider/needs_auth/Gemini.py b/g4f/Provider/needs_auth/Gemini.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7c9de23ab6ed9353dfb0875e44f1c575edcbb8b
--- /dev/null
+++ b/g4f/Provider/needs_auth/Gemini.py
@@ -0,0 +1,340 @@
+from __future__ import annotations
+
+import os
+import json
+import random
+import re
+import base64
+
+from aiohttp import ClientSession, BaseConnector
+
+try:
+ import nodriver
+ has_nodriver = True
+except ImportError:
+ has_nodriver = False
+
+from ... import debug
+from ...typing import Messages, Cookies, ImageType, AsyncResult, AsyncIterator
+from ..base_provider import AsyncGeneratorProvider, BaseConversation, SynthesizeData
+from ..helper import format_prompt, get_cookies
+from ...requests.raise_for_status import raise_for_status
+from ...requests.aiohttp import get_connector
+from ...requests import get_nodriver
+from ...errors import MissingAuthError
+from ...image import ImageResponse, to_bytes
+from ... import debug
+
+REQUEST_HEADERS = {
+ "authority": "gemini.google.com",
+ "origin": "https://gemini.google.com",
+ "referer": "https://gemini.google.com/",
+ 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36',
+ 'x-same-domain': '1',
+}
+REQUEST_BL_PARAM = "boq_assistant-bard-web-server_20240519.16_p0"
+REQUEST_URL = "https://gemini.google.com/_/BardChatUi/data/assistant.lamda.BardFrontendService/StreamGenerate"
+UPLOAD_IMAGE_URL = "https://content-push.googleapis.com/upload/"
+UPLOAD_IMAGE_HEADERS = {
+ "authority": "content-push.googleapis.com",
+ "accept": "*/*",
+ "accept-language": "en-US,en;q=0.7",
+ "authorization": "Basic c2F2ZXM6cyNMdGhlNmxzd2F2b0RsN3J1d1U=",
+ "content-type": "application/x-www-form-urlencoded;charset=UTF-8",
+ "origin": "https://gemini.google.com",
+ "push-id": "feeds/mcudyrk2a4khkz",
+ "referer": "https://gemini.google.com/",
+ "x-goog-upload-command": "start",
+ "x-goog-upload-header-content-length": "",
+ "x-goog-upload-protocol": "resumable",
+ "x-tenant-id": "bard-storage",
+}
+
+class Gemini(AsyncGeneratorProvider):
+ url = "https://gemini.google.com"
+ needs_auth = True
+ working = True
+ default_model = 'gemini'
+ image_models = ["gemini"]
+ default_vision_model = "gemini"
+ models = ["gemini", "gemini-1.5-flash", "gemini-1.5-pro"]
+ synthesize_content_type = "audio/vnd.wav"
+ _cookies: Cookies = None
+ _snlm0e: str = None
+ _sid: str = None
+
+ @classmethod
+ async def nodriver_login(cls, proxy: str = None) -> AsyncIterator[str]:
+ if not has_nodriver:
+ if debug.logging:
+ print("Skip nodriver login in Gemini provider")
+ return
+ browser = await get_nodriver(proxy=proxy)
+ login_url = os.environ.get("G4F_LOGIN_URL")
+ if login_url:
+ yield f"Please login: [Google Gemini]({login_url})\n\n"
+ page = await browser.get(f"{cls.url}/app")
+ await page.select("div.ql-editor.textarea", 240)
+ cookies = {}
+ for c in await page.browser.cookies.get_all():
+ if c.domain.endswith(".google.com"):
+ cookies[c.name] = c.value
+ await page.close()
+ cls._cookies = cookies
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ cookies: Cookies = None,
+ connector: BaseConnector = None,
+ image: ImageType = None,
+ image_name: str = None,
+ response_format: str = None,
+ return_conversation: bool = False,
+ conversation: Conversation = None,
+ language: str = "en",
+ **kwargs
+ ) -> AsyncResult:
+ prompt = format_prompt(messages) if conversation is None else messages[-1]["content"]
+ cls._cookies = cookies or cls._cookies or get_cookies(".google.com", False, True)
+ base_connector = get_connector(connector, proxy)
+
+ async with ClientSession(
+ headers=REQUEST_HEADERS,
+ connector=base_connector
+ ) as session:
+ if not cls._snlm0e:
+ await cls.fetch_snlm0e(session, cls._cookies) if cls._cookies else None
+ if not cls._snlm0e:
+ try:
+ async for chunk in cls.nodriver_login(proxy):
+ yield chunk
+ except Exception as e:
+ raise MissingAuthError('Missing "__Secure-1PSID" cookie', e)
+ if not cls._snlm0e:
+ if cls._cookies is None or "__Secure-1PSID" not in cls._cookies:
+ raise MissingAuthError('Missing "__Secure-1PSID" cookie')
+ await cls.fetch_snlm0e(session, cls._cookies)
+ if not cls._snlm0e:
+ raise RuntimeError("Invalid cookies. SNlM0e not found")
+
+ yield SynthesizeData(cls.__name__, {"text": messages[-1]["content"]})
+ image_url = await cls.upload_image(base_connector, to_bytes(image), image_name) if image else None
+
+ async with ClientSession(
+ cookies=cls._cookies,
+ headers=REQUEST_HEADERS,
+ connector=base_connector,
+ ) as client:
+ params = {
+ 'bl': REQUEST_BL_PARAM,
+ 'hl': language,
+ '_reqid': random.randint(1111, 9999),
+ 'rt': 'c',
+ "f.sid": cls._sid,
+ }
+ data = {
+ 'at': cls._snlm0e,
+ 'f.req': json.dumps([None, json.dumps(cls.build_request(
+ prompt,
+ language=language,
+ conversation=conversation,
+ image_url=image_url,
+ image_name=image_name
+ ))])
+ }
+ async with client.post(
+ REQUEST_URL,
+ data=data,
+ params=params,
+ ) as response:
+ await raise_for_status(response)
+ image_prompt = response_part = None
+ last_content_len = 0
+ async for line in response.content:
+ try:
+ try:
+ line = json.loads(line)
+ except ValueError:
+ continue
+ if not isinstance(line, list):
+ continue
+ if len(line[0]) < 3 or not line[0][2]:
+ continue
+ response_part = json.loads(line[0][2])
+ if not response_part[4]:
+ continue
+ if return_conversation:
+ yield Conversation(response_part[1][0], response_part[1][1], response_part[4][0][0])
+ content = response_part[4][0][1][0]
+ except (ValueError, KeyError, TypeError, IndexError) as e:
+ print(f"{cls.__name__}:{e.__class__.__name__}:{e}")
+ continue
+ match = re.search(r'\[Imagen of (.*?)\]', content)
+ if match:
+ image_prompt = match.group(1)
+ content = content.replace(match.group(0), '')
+ yield content[last_content_len:]
+ last_content_len = len(content)
+ if image_prompt:
+ try:
+ images = [image[0][3][3] for image in response_part[4][0][12][7][0]]
+ if response_format == "b64_json":
+ yield ImageResponse(images, image_prompt, {"cookies": cls._cookies})
+ else:
+ resolved_images = []
+ preview = []
+ for image in images:
+ async with client.get(image, allow_redirects=False) as fetch:
+ image = fetch.headers["location"]
+ async with client.get(image, allow_redirects=False) as fetch:
+ image = fetch.headers["location"]
+ resolved_images.append(image)
+ preview.append(image.replace('=s512', '=s200'))
+ yield ImageResponse(resolved_images, image_prompt, {"orginal_links": images, "preview": preview})
+ except TypeError:
+ pass
+
+ @classmethod
+ async def synthesize(cls, params: dict, proxy: str = None) -> AsyncIterator[bytes]:
+ if "text" not in params:
+ raise ValueError("Missing parameter text")
+ async with ClientSession(
+ cookies=cls._cookies,
+ headers=REQUEST_HEADERS,
+ connector=get_connector(proxy=proxy),
+ ) as session:
+ if not cls._snlm0e:
+ await cls.fetch_snlm0e(session, cls._cookies) if cls._cookies else None
+ inner_data = json.dumps([None, params["text"], "de-DE", None, 2])
+ async with session.post(
+ "https://gemini.google.com/_/BardChatUi/data/batchexecute",
+ data={
+ "f.req": json.dumps([[["XqA3Ic", inner_data, None, "generic"]]]),
+ "at": cls._snlm0e,
+ },
+ params={
+ "rpcids": "XqA3Ic",
+ "source-path": "/app/2704fb4aafcca926",
+ "bl": "boq_assistant-bard-web-server_20241119.00_p1",
+ "f.sid": "" if cls._sid is None else cls._sid,
+ "hl": "de",
+ "_reqid": random.randint(1111, 9999),
+ "rt": "c"
+ },
+ ) as response:
+ await raise_for_status(response)
+ iter_base64_response = iter_filter_base64(response.content.iter_chunked(1024))
+ async for chunk in iter_base64_decode(iter_base64_response):
+ yield chunk
+
+ def build_request(
+ prompt: str,
+ language: str,
+ conversation: Conversation = None,
+ image_url: str = None,
+ image_name: str = None,
+ tools: list[list[str]] = []
+ ) -> list:
+ image_list = [[[image_url, 1], image_name]] if image_url else []
+ return [
+ [prompt, 0, None, image_list, None, None, 0],
+ [language],
+ [
+ None if conversation is None else conversation.conversation_id,
+ None if conversation is None else conversation.response_id,
+ None if conversation is None else conversation.choice_id,
+ None,
+ None,
+ []
+ ],
+ None,
+ None,
+ None,
+ [1],
+ 0,
+ [],
+ tools,
+ 1,
+ 0,
+ ]
+
+ async def upload_image(connector: BaseConnector, image: bytes, image_name: str = None):
+ async with ClientSession(
+ headers=UPLOAD_IMAGE_HEADERS,
+ connector=connector
+ ) as session:
+ async with session.options(UPLOAD_IMAGE_URL) as response:
+ await raise_for_status(response)
+
+ headers = {
+ "size": str(len(image)),
+ "x-goog-upload-command": "start"
+ }
+ data = f"File name: {image_name}" if image_name else None
+ async with session.post(
+ UPLOAD_IMAGE_URL, headers=headers, data=data
+ ) as response:
+ await raise_for_status(response)
+ upload_url = response.headers["X-Goog-Upload-Url"]
+
+ async with session.options(upload_url, headers=headers) as response:
+ await raise_for_status(response)
+
+ headers["x-goog-upload-command"] = "upload, finalize"
+ headers["X-Goog-Upload-Offset"] = "0"
+ async with session.post(
+ upload_url, headers=headers, data=image
+ ) as response:
+ await raise_for_status(response)
+ return await response.text()
+
+ @classmethod
+ async def fetch_snlm0e(cls, session: ClientSession, cookies: Cookies):
+ async with session.get(cls.url, cookies=cookies) as response:
+ await raise_for_status(response)
+ response_text = await response.text()
+ match = re.search(r'SNlM0e\":\"(.*?)\"', response_text)
+ if match:
+ cls._snlm0e = match.group(1)
+ sid_match = re.search(r'"FdrFJe":"([\d-]+)"', response_text)
+ if sid_match:
+ cls._sid = sid_match.group(1)
+
+class Conversation(BaseConversation):
+ def __init__(self,
+ conversation_id: str = "",
+ response_id: str = "",
+ choice_id: str = ""
+ ) -> None:
+ self.conversation_id = conversation_id
+ self.response_id = response_id
+ self.choice_id = choice_id
+
+async def iter_filter_base64(response_iter: AsyncIterator[bytes]) -> AsyncIterator[bytes]:
+ search_for = b'[["wrb.fr","XqA3Ic","[\\"'
+ end_with = b'\\'
+ is_started = False
+ async for chunk in response_iter:
+ if is_started:
+ if end_with in chunk:
+ yield chunk.split(end_with, 1).pop(0)
+ break
+ else:
+ yield chunk
+ elif search_for in chunk:
+ is_started = True
+ yield chunk.split(search_for, 1).pop()
+ else:
+ raise RuntimeError(f"Response: {chunk}")
+
+async def iter_base64_decode(response_iter: AsyncIterator[bytes]) -> AsyncIterator[bytes]:
+ buffer = b""
+ async for chunk in response_iter:
+ chunk = buffer + chunk
+ rest = len(chunk) % 4
+ buffer = chunk[-rest:]
+ yield base64.b64decode(chunk[:-rest])
\ No newline at end of file
diff --git a/g4f/Provider/needs_auth/GeminiPro.py b/g4f/Provider/needs_auth/GeminiPro.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7f1e0aa621ca3bf6c6a77c4083ebce59fc50250
--- /dev/null
+++ b/g4f/Provider/needs_auth/GeminiPro.py
@@ -0,0 +1,111 @@
+from __future__ import annotations
+
+import base64
+import json
+from aiohttp import ClientSession, BaseConnector
+
+from ...typing import AsyncResult, Messages, ImageType
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ...image import to_bytes, is_accepted_format
+from ...errors import MissingAuthError
+from ..helper import get_connector
+
+class GeminiPro(AsyncGeneratorProvider, ProviderModelMixin):
+ label = "Gemini API"
+ url = "https://ai.google.dev"
+ working = True
+ supports_message_history = True
+ needs_auth = True
+ default_model = "gemini-1.5-pro"
+ default_vision_model = default_model
+ models = [default_model, "gemini-pro", "gemini-1.5-flash", "gemini-1.5-flash-8b"]
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ stream: bool = False,
+ proxy: str = None,
+ api_key: str = None,
+ api_base: str = "https://generativelanguage.googleapis.com/v1beta",
+ use_auth_header: bool = False,
+ image: ImageType = None,
+ connector: BaseConnector = None,
+ **kwargs
+ ) -> AsyncResult:
+ model = cls.get_model(model)
+
+ if not api_key:
+ raise MissingAuthError('Add a "api_key"')
+
+ headers = params = None
+ if use_auth_header:
+ headers = {"Authorization": f"Bearer {api_key}"}
+ else:
+ params = {"key": api_key}
+
+ method = "streamGenerateContent" if stream else "generateContent"
+ url = f"{api_base.rstrip('/')}/models/{model}:{method}"
+ async with ClientSession(headers=headers, connector=get_connector(connector, proxy)) as session:
+ contents = [
+ {
+ "role": "model" if message["role"] == "assistant" else "user",
+ "parts": [{"text": message["content"]}]
+ }
+ for message in messages
+ if message["role"] != "system"
+ ]
+ if image is not None:
+ image = to_bytes(image)
+ contents[-1]["parts"].append({
+ "inline_data": {
+ "mime_type": is_accepted_format(image),
+ "data": base64.b64encode(image).decode()
+ }
+ })
+ data = {
+ "contents": contents,
+ "generationConfig": {
+ "stopSequences": kwargs.get("stop"),
+ "temperature": kwargs.get("temperature"),
+ "maxOutputTokens": kwargs.get("max_tokens"),
+ "topP": kwargs.get("top_p"),
+ "topK": kwargs.get("top_k"),
+ }
+ }
+ system_prompt = "\n".join(
+ message["content"]
+ for message in messages
+ if message["role"] == "system"
+ )
+ if system_prompt:
+ data["system_instruction"] = {"parts": {"text": system_prompt}}
+ async with session.post(url, params=params, json=data) as response:
+ if not response.ok:
+ data = await response.json()
+ data = data[0] if isinstance(data, list) else data
+ raise RuntimeError(f"Response {response.status}: {data['error']['message']}")
+ if stream:
+ lines = []
+ async for chunk in response.content:
+ if chunk == b"[{\n":
+ lines = [b"{\n"]
+ elif chunk == b",\r\n" or chunk == b"]":
+ try:
+ data = b"".join(lines)
+ data = json.loads(data)
+ yield data["candidates"][0]["content"]["parts"][0]["text"]
+ except:
+ data = data.decode(errors="ignore") if isinstance(data, bytes) else data
+ raise RuntimeError(f"Read chunk failed: {data}")
+ lines = []
+ else:
+ lines.append(chunk)
+ else:
+ data = await response.json()
+ candidate = data["candidates"][0]
+ if candidate["finishReason"] == "STOP":
+ yield candidate["content"]["parts"][0]["text"]
+ else:
+ yield candidate["finishReason"] + ' ' + candidate["safetyRatings"]
\ No newline at end of file
diff --git a/g4f/Provider/needs_auth/GithubCopilot.py b/g4f/Provider/needs_auth/GithubCopilot.py
new file mode 100644
index 0000000000000000000000000000000000000000..3eb66b5ec32098c06215bbb653387b67d1a29e2d
--- /dev/null
+++ b/g4f/Provider/needs_auth/GithubCopilot.py
@@ -0,0 +1,93 @@
+from __future__ import annotations
+
+import json
+
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin, BaseConversation
+from ...typing import AsyncResult, Messages, Cookies
+from ...requests.raise_for_status import raise_for_status
+from ...requests import StreamSession
+from ...providers.helper import format_prompt
+from ...cookies import get_cookies
+
+class Conversation(BaseConversation):
+ conversation_id: str
+
+ def __init__(self, conversation_id: str):
+ self.conversation_id = conversation_id
+
+class GithubCopilot(AsyncGeneratorProvider, ProviderModelMixin):
+ url = "https://copilot.microsoft.com"
+ working = True
+ needs_auth = True
+ supports_stream = True
+ default_model = "gpt-4o"
+ models = [default_model, "o1-mini", "o1-preview", "claude-3.5-sonnet"]
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ stream: bool = False,
+ api_key: str = None,
+ proxy: str = None,
+ cookies: Cookies = None,
+ conversation_id: str = None,
+ conversation: Conversation = None,
+ return_conversation: bool = False,
+ **kwargs
+ ) -> AsyncResult:
+ if not model:
+ model = cls.default_model
+ if cookies is None:
+ cookies = get_cookies(".github.com")
+ async with StreamSession(
+ proxy=proxy,
+ impersonate="chrome",
+ cookies=cookies,
+ headers={
+ "GitHub-Verified-Fetch": "true",
+ }
+ ) as session:
+ headers = {}
+ if api_key is None:
+ async with session.post("https://github.com/github-copilot/chat/token") as response:
+ await raise_for_status(response, "Get token")
+ api_key = (await response.json()).get("token")
+ headers = {
+ "Authorization": f"GitHub-Bearer {api_key}",
+ }
+ if conversation is not None:
+ conversation_id = conversation.conversation_id
+ if conversation_id is None:
+ print(headers)
+ async with session.post("https://api.individual.githubcopilot.com/github/chat/threads", headers=headers) as response:
+ await raise_for_status(response)
+ conversation_id = (await response.json()).get("thread_id")
+ if return_conversation:
+ yield Conversation(conversation_id)
+ content = messages[-1]["content"]
+ else:
+ content = format_prompt(messages)
+ json_data = {
+ "content": content,
+ "intent": "conversation",
+ "references":[],
+ "context": [],
+ "currentURL": f"https://github.com/copilot/c/{conversation_id}",
+ "streaming": True,
+ "confirmations": [],
+ "customInstructions": [],
+ "model": model,
+ "mode": "immersive"
+ }
+ async with session.post(
+ f"https://api.individual.githubcopilot.com/github/chat/threads/{conversation_id}/messages",
+ json=json_data,
+ headers=headers
+ ) as response:
+ async for line in response.iter_lines():
+ if line.startswith(b"data: "):
+ data = json.loads(line[6:])
+ if data.get("type") == "content":
+ yield data.get("body")
\ No newline at end of file
diff --git a/g4f/Provider/needs_auth/Groq.py b/g4f/Provider/needs_auth/Groq.py
new file mode 100644
index 0000000000000000000000000000000000000000..943fc81a6948cc13000200556d0053c89dd26d1a
--- /dev/null
+++ b/g4f/Provider/needs_auth/Groq.py
@@ -0,0 +1,43 @@
+from __future__ import annotations
+
+from .OpenaiAPI import OpenaiAPI
+from ...typing import AsyncResult, Messages
+
+class Groq(OpenaiAPI):
+ label = "Groq"
+ url = "https://console.groq.com/playground"
+ working = True
+ default_model = "mixtral-8x7b-32768"
+ models = [
+ "distil-whisper-large-v3-en",
+ "gemma2-9b-it",
+ "gemma-7b-it",
+ "llama3-groq-70b-8192-tool-use-preview",
+ "llama3-groq-8b-8192-tool-use-preview",
+ "llama-3.1-70b-versatile",
+ "llama-3.1-8b-instant",
+ "llama-3.2-1b-preview",
+ "llama-3.2-3b-preview",
+ "llama-3.2-11b-vision-preview",
+ "llama-3.2-90b-vision-preview",
+ "llama-guard-3-8b",
+ "llava-v1.5-7b-4096-preview",
+ "llama3-70b-8192",
+ "llama3-8b-8192",
+ "mixtral-8x7b-32768",
+ "whisper-large-v3",
+ "whisper-large-v3-turbo",
+ ]
+ model_aliases = {"mixtral-8x7b": "mixtral-8x7b-32768", "llama2-70b": "llama2-70b-4096"}
+
+ @classmethod
+ def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ api_base: str = "https://api.groq.com/openai/v1",
+ **kwargs
+ ) -> AsyncResult:
+ return super().create_async_generator(
+ model, messages, api_base=api_base, **kwargs
+ )
diff --git a/g4f/Provider/needs_auth/HuggingFace.py b/g4f/Provider/needs_auth/HuggingFace.py
new file mode 100644
index 0000000000000000000000000000000000000000..35270e608511c7d89afcc3658ceb0fab00f501b3
--- /dev/null
+++ b/g4f/Provider/needs_auth/HuggingFace.py
@@ -0,0 +1,91 @@
+from __future__ import annotations
+
+import json
+
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ...errors import ModelNotFoundError
+from ...requests import StreamSession, raise_for_status
+
+from ..HuggingChat import HuggingChat
+
+class HuggingFace(AsyncGeneratorProvider, ProviderModelMixin):
+ url = "https://huggingface.co/chat"
+ working = True
+ needs_auth = True
+ supports_message_history = True
+ default_model = HuggingChat.default_model
+ models = HuggingChat.models
+ model_aliases = HuggingChat.model_aliases
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ stream: bool = True,
+ proxy: str = None,
+ api_base: str = "https://api-inference.huggingface.co",
+ api_key: str = None,
+ max_new_tokens: int = 1024,
+ temperature: float = 0.7,
+ **kwargs
+ ) -> AsyncResult:
+ model = cls.get_model(model)
+ headers = {
+ 'accept': '*/*',
+ 'accept-language': 'en',
+ 'cache-control': 'no-cache',
+ 'origin': 'https://huggingface.co',
+ 'pragma': 'no-cache',
+ 'priority': 'u=1, i',
+ 'referer': 'https://huggingface.co/chat/',
+ 'sec-ch-ua': '"Not)A;Brand";v="99", "Google Chrome";v="127", "Chromium";v="127"',
+ 'sec-ch-ua-mobile': '?0',
+ 'sec-ch-ua-platform': '"macOS"',
+ 'sec-fetch-dest': 'empty',
+ 'sec-fetch-mode': 'cors',
+ 'sec-fetch-site': 'same-origin',
+ 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36',
+ }
+ if api_key is not None:
+ headers["Authorization"] = f"Bearer {api_key}"
+ params = {
+ "return_full_text": False,
+ "max_new_tokens": max_new_tokens,
+ "temperature": temperature,
+ **kwargs
+ }
+ payload = {"inputs": format_prompt(messages), "parameters": params, "stream": stream}
+ async with StreamSession(
+ headers=headers,
+ proxy=proxy
+ ) as session:
+ async with session.post(f"{api_base.rstrip('/')}/models/{model}", json=payload) as response:
+ if response.status == 404:
+ raise ModelNotFoundError(f"Model is not supported: {model}")
+ await raise_for_status(response)
+ if stream:
+ first = True
+ async for line in response.iter_lines():
+ if line.startswith(b"data:"):
+ data = json.loads(line[5:])
+ if not data["token"]["special"]:
+ chunk = data["token"]["text"]
+ if first:
+ first = False
+ chunk = chunk.lstrip()
+ if chunk:
+ yield chunk
+ else:
+ yield (await response.json())[0]["generated_text"].strip()
+
+def format_prompt(messages: Messages) -> str:
+ system_messages = [message["content"] for message in messages if message["role"] == "system"]
+ question = " ".join([messages[-1]["content"], *system_messages])
+ history = "".join([
+ f"[INST]{messages[idx-1]['content']} [/INST] {message['content']}"
+ for idx, message in enumerate(messages)
+ if message["role"] == "assistant"
+ ])
+ return f"{history}[INST] {question} [/INST]"
\ No newline at end of file
diff --git a/g4f/Provider/needs_auth/HuggingFace2.py b/g4f/Provider/needs_auth/HuggingFace2.py
new file mode 100644
index 0000000000000000000000000000000000000000..847d459b17f3523d4217f98fca6fb8599c6b8a6c
--- /dev/null
+++ b/g4f/Provider/needs_auth/HuggingFace2.py
@@ -0,0 +1,28 @@
+from __future__ import annotations
+
+from .OpenaiAPI import OpenaiAPI
+from ..HuggingChat import HuggingChat
+from ...typing import AsyncResult, Messages
+
+class HuggingFace2(OpenaiAPI):
+ label = "HuggingFace (Inference API)"
+ url = "https://huggingface.co"
+ working = True
+ default_model = "meta-llama/Llama-3.2-11B-Vision-Instruct"
+ default_vision_model = default_model
+ models = [
+ *HuggingChat.models
+ ]
+
+ @classmethod
+ def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ api_base: str = "https://api-inference.huggingface.co/v1",
+ max_tokens: int = 500,
+ **kwargs
+ ) -> AsyncResult:
+ return super().create_async_generator(
+ model, messages, api_base=api_base, max_tokens=max_tokens, **kwargs
+ )
\ No newline at end of file
diff --git a/g4f/Provider/needs_auth/MetaAI.py b/g4f/Provider/needs_auth/MetaAI.py
new file mode 100644
index 0000000000000000000000000000000000000000..568de701f7e3648fb1c8fa63f44141115bba9d93
--- /dev/null
+++ b/g4f/Provider/needs_auth/MetaAI.py
@@ -0,0 +1,239 @@
+from __future__ import annotations
+
+import json
+import uuid
+import random
+import time
+from typing import Dict, List
+
+from aiohttp import ClientSession, BaseConnector
+
+from ...typing import AsyncResult, Messages, Cookies
+from ...requests import raise_for_status, DEFAULT_HEADERS
+from ...image import ImageResponse, ImagePreview
+from ...errors import ResponseError
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import format_prompt, get_connector, format_cookies
+
+class Sources():
+ def __init__(self, link_list: List[Dict[str, str]]) -> None:
+ self.list = link_list
+
+ def __str__(self) -> str:
+ return "\n\n" + ("\n".join([f"[{link['title']}]({link['link']})" for link in self.list]))
+
+class AbraGeoBlockedError(Exception):
+ pass
+
+class MetaAI(AsyncGeneratorProvider, ProviderModelMixin):
+ label = "Meta AI"
+ url = "https://www.meta.ai"
+ working = True
+ default_model = ''
+
+ def __init__(self, proxy: str = None, connector: BaseConnector = None):
+ self.session = ClientSession(connector=get_connector(connector, proxy), headers=DEFAULT_HEADERS)
+ self.cookies: Cookies = None
+ self.access_token: str = None
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ **kwargs
+ ) -> AsyncResult:
+ async for chunk in cls(proxy).prompt(format_prompt(messages)):
+ yield chunk
+
+ async def update_access_token(self, birthday: str = "1999-01-01"):
+ url = "https://www.meta.ai/api/graphql/"
+ payload = {
+ "lsd": self.lsd,
+ "fb_api_caller_class": "RelayModern",
+ "fb_api_req_friendly_name": "useAbraAcceptTOSForTempUserMutation",
+ "variables": json.dumps({
+ "dob": birthday,
+ "icebreaker_type": "TEXT",
+ "__relay_internal__pv__WebPixelRatiorelayprovider": 1,
+ }),
+ "doc_id": "7604648749596940",
+ }
+ headers = {
+ "x-fb-friendly-name": "useAbraAcceptTOSForTempUserMutation",
+ "x-fb-lsd": self.lsd,
+ "x-asbd-id": "129477",
+ "alt-used": "www.meta.ai",
+ "sec-fetch-site": "same-origin"
+ }
+ async with self.session.post(url, headers=headers, cookies=self.cookies, data=payload) as response:
+ await raise_for_status(response, "Fetch access_token failed")
+ auth_json = await response.json(content_type=None)
+ self.access_token = auth_json["data"]["xab_abra_accept_terms_of_service"]["new_temp_user_auth"]["access_token"]
+
+ async def prompt(self, message: str, cookies: Cookies = None) -> AsyncResult:
+ if self.cookies is None:
+ await self.update_cookies(cookies)
+ if cookies is not None:
+ self.access_token = None
+ if self.access_token is None and cookies is None:
+ await self.update_access_token()
+ if self.access_token is None:
+ url = "https://www.meta.ai/api/graphql/"
+ payload = {"lsd": self.lsd, 'fb_dtsg': self.dtsg}
+ headers = {'x-fb-lsd': self.lsd}
+ else:
+ url = "https://graph.meta.ai/graphql?locale=user"
+ payload = {"access_token": self.access_token}
+ headers = {}
+ headers = {
+ 'content-type': 'application/x-www-form-urlencoded',
+ 'cookie': format_cookies(self.cookies),
+ 'origin': 'https://www.meta.ai',
+ 'referer': 'https://www.meta.ai/',
+ 'x-asbd-id': '129477',
+ 'x-fb-friendly-name': 'useAbraSendMessageMutation',
+ **headers
+ }
+ payload = {
+ **payload,
+ 'fb_api_caller_class': 'RelayModern',
+ 'fb_api_req_friendly_name': 'useAbraSendMessageMutation',
+ "variables": json.dumps({
+ "message": {"sensitive_string_value": message},
+ "externalConversationId": str(uuid.uuid4()),
+ "offlineThreadingId": generate_offline_threading_id(),
+ "suggestedPromptIndex": None,
+ "flashVideoRecapInput": {"images": []},
+ "flashPreviewInput": None,
+ "promptPrefix": None,
+ "entrypoint": "ABRA__CHAT__TEXT",
+ "icebreaker_type": "TEXT",
+ "__relay_internal__pv__AbraDebugDevOnlyrelayprovider": False,
+ "__relay_internal__pv__WebPixelRatiorelayprovider": 1,
+ }),
+ 'server_timestamps': 'true',
+ 'doc_id': '7783822248314888'
+ }
+ async with self.session.post(url, headers=headers, data=payload) as response:
+ await raise_for_status(response, "Fetch response failed")
+ last_snippet_len = 0
+ fetch_id = None
+ async for line in response.content:
+ if b"Something Went Wrong
" in line:
+ raise ResponseError("Response: Something Went Wrong")
+ try:
+ json_line = json.loads(line)
+ except json.JSONDecodeError:
+ continue
+ if json_line.get("errors"):
+ raise RuntimeError("\n".join([error.get("message") for error in json_line.get("errors")]))
+ bot_response_message = json_line.get("data", {}).get("node", {}).get("bot_response_message", {})
+ streaming_state = bot_response_message.get("streaming_state")
+ fetch_id = bot_response_message.get("fetch_id") or fetch_id
+ if streaming_state in ("STREAMING", "OVERALL_DONE"):
+ imagine_card = bot_response_message.get("imagine_card")
+ if imagine_card is not None:
+ imagine_session = imagine_card.get("session")
+ if imagine_session is not None:
+ imagine_medias = imagine_session.get("media_sets", {}).pop().get("imagine_media")
+ if imagine_medias is not None:
+ image_class = ImageResponse if streaming_state == "OVERALL_DONE" else ImagePreview
+ yield image_class([media["uri"] for media in imagine_medias], imagine_medias[0]["prompt"])
+ snippet = bot_response_message["snippet"]
+ new_snippet_len = len(snippet)
+ if new_snippet_len > last_snippet_len:
+ yield snippet[last_snippet_len:]
+ last_snippet_len = new_snippet_len
+ #if last_streamed_response is None:
+ # if attempts > 3:
+ # raise Exception("MetaAI is having issues and was not able to respond (Server Error)")
+ # access_token = await self.get_access_token()
+ # return await self.prompt(message=message, attempts=attempts + 1)
+ if fetch_id is not None:
+ sources = await self.fetch_sources(fetch_id)
+ if sources is not None:
+ yield sources
+
+ async def update_cookies(self, cookies: Cookies = None):
+ async with self.session.get("https://www.meta.ai/", cookies=cookies) as response:
+ await raise_for_status(response, "Fetch home failed")
+ text = await response.text()
+ if "AbraGeoBlockedError" in text:
+ raise AbraGeoBlockedError("Meta AI isn't available yet in your country")
+ if cookies is None:
+ cookies = {
+ "_js_datr": self.extract_value(text, "_js_datr"),
+ "abra_csrf": self.extract_value(text, "abra_csrf"),
+ "datr": self.extract_value(text, "datr"),
+ }
+ self.lsd = self.extract_value(text, start_str='"LSD",[],{"token":"', end_str='"}')
+ self.dtsg = self.extract_value(text, start_str='"DTSGInitialData",[],{"token":"', end_str='"}')
+ self.cookies = cookies
+
+ async def fetch_sources(self, fetch_id: str) -> Sources:
+ if self.access_token is None:
+ url = "https://www.meta.ai/api/graphql/"
+ payload = {"lsd": self.lsd, 'fb_dtsg': self.dtsg}
+ headers = {'x-fb-lsd': self.lsd}
+ else:
+ url = "https://graph.meta.ai/graphql?locale=user"
+ payload = {"access_token": self.access_token}
+ headers = {}
+ payload = {
+ **payload,
+ "fb_api_caller_class": "RelayModern",
+ "fb_api_req_friendly_name": "AbraSearchPluginDialogQuery",
+ "variables": json.dumps({"abraMessageFetchID": fetch_id}),
+ "server_timestamps": "true",
+ "doc_id": "6946734308765963",
+ }
+ headers = {
+ "authority": "graph.meta.ai",
+ "x-fb-friendly-name": "AbraSearchPluginDialogQuery",
+ **headers
+ }
+ async with self.session.post(url, headers=headers, cookies=self.cookies, data=payload) as response:
+ await raise_for_status(response, "Fetch sources failed")
+ text = await response.text()
+ if "Something Went Wrong
" in text:
+ raise ResponseError("Response: Something Went Wrong")
+ try:
+ response_json = json.loads(text)
+ message = response_json["data"]["message"]
+ if message is not None:
+ searchResults = message["searchResults"]
+ if searchResults is not None:
+ return Sources(searchResults["references"])
+ except (KeyError, TypeError, json.JSONDecodeError):
+ raise RuntimeError(f"Response: {text}")
+
+ @staticmethod
+ def extract_value(text: str, key: str = None, start_str = None, end_str = '",') -> str:
+ if start_str is None:
+ start_str = f'{key}":{{"value":"'
+ start = text.find(start_str)
+ if start >= 0:
+ start+= len(start_str)
+ end = text.find(end_str, start)
+ if end >= 0:
+ return text[start:end]
+
+def generate_offline_threading_id() -> str:
+ """
+ Generates an offline threading ID.
+
+ Returns:
+ str: The generated offline threading ID.
+ """
+ # Generate a random 64-bit integer
+ random_value = random.getrandbits(64)
+
+ # Get the current timestamp in milliseconds
+ timestamp = int(time.time() * 1000)
+
+ # Combine timestamp and random value
+ threading_id = (timestamp << 22) | (random_value & ((1 << 22) - 1))
+
+ return str(threading_id)
diff --git a/g4f/Provider/needs_auth/MetaAIAccount.py b/g4f/Provider/needs_auth/MetaAIAccount.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a586006546a94ae06854a97a0682db0794f4751
--- /dev/null
+++ b/g4f/Provider/needs_auth/MetaAIAccount.py
@@ -0,0 +1,23 @@
+from __future__ import annotations
+
+from ...typing import AsyncResult, Messages, Cookies
+from ..helper import format_prompt, get_cookies
+from .MetaAI import MetaAI
+
+class MetaAIAccount(MetaAI):
+ needs_auth = True
+ parent = "MetaAI"
+ image_models = ["meta"]
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ cookies: Cookies = None,
+ **kwargs
+ ) -> AsyncResult:
+ cookies = get_cookies(".meta.ai", True, True) if cookies is None else cookies
+ async for chunk in cls(proxy).prompt(format_prompt(messages), cookies):
+ yield chunk
diff --git a/g4f/Provider/needs_auth/OpenaiAPI.py b/g4f/Provider/needs_auth/OpenaiAPI.py
new file mode 100644
index 0000000000000000000000000000000000000000..83268b6d1ecd2cacd6642a825c1650eb05a06f83
--- /dev/null
+++ b/g4f/Provider/needs_auth/OpenaiAPI.py
@@ -0,0 +1,126 @@
+from __future__ import annotations
+
+import json
+
+from ..helper import filter_none
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin, FinishReason
+from ...typing import Union, Optional, AsyncResult, Messages, ImageType
+from ...requests import StreamSession, raise_for_status
+from ...errors import MissingAuthError, ResponseError
+from ...image import to_data_uri
+
+class OpenaiAPI(AsyncGeneratorProvider, ProviderModelMixin):
+ label = "OpenAI API"
+ url = "https://platform.openai.com"
+ working = True
+ needs_auth = True
+ supports_message_history = True
+ supports_system_message = True
+ default_model = ""
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ timeout: int = 120,
+ image: ImageType = None,
+ api_key: str = None,
+ api_base: str = "https://api.openai.com/v1",
+ temperature: float = None,
+ max_tokens: int = None,
+ top_p: float = None,
+ stop: Union[str, list[str]] = None,
+ stream: bool = False,
+ headers: dict = None,
+ impersonate: str = None,
+ extra_data: dict = {},
+ **kwargs
+ ) -> AsyncResult:
+ if cls.needs_auth and api_key is None:
+ raise MissingAuthError('Add a "api_key"')
+ if image is not None:
+ if not model and hasattr(cls, "default_vision_model"):
+ model = cls.default_vision_model
+ messages[-1]["content"] = [
+ {
+ "type": "image_url",
+ "image_url": {"url": to_data_uri(image)}
+ },
+ {
+ "type": "text",
+ "text": messages[-1]["content"]
+ }
+ ]
+ async with StreamSession(
+ proxies={"all": proxy},
+ headers=cls.get_headers(stream, api_key, headers),
+ timeout=timeout,
+ impersonate=impersonate,
+ ) as session:
+ data = filter_none(
+ messages=messages,
+ model=cls.get_model(model),
+ temperature=temperature,
+ max_tokens=max_tokens,
+ top_p=top_p,
+ stop=stop,
+ stream=stream,
+ **extra_data
+ )
+ async with session.post(f"{api_base.rstrip('/')}/chat/completions", json=data) as response:
+ await raise_for_status(response)
+ if not stream:
+ data = await response.json()
+ cls.raise_error(data)
+ choice = data["choices"][0]
+ if "content" in choice["message"]:
+ yield choice["message"]["content"].strip()
+ finish = cls.read_finish_reason(choice)
+ if finish is not None:
+ yield finish
+ else:
+ first = True
+ async for line in response.iter_lines():
+ if line.startswith(b"data: "):
+ chunk = line[6:]
+ if chunk == b"[DONE]":
+ break
+ data = json.loads(chunk)
+ cls.raise_error(data)
+ choice = data["choices"][0]
+ if "content" in choice["delta"] and choice["delta"]["content"]:
+ delta = choice["delta"]["content"]
+ if first:
+ delta = delta.lstrip()
+ if delta:
+ first = False
+ yield delta
+ finish = cls.read_finish_reason(choice)
+ if finish is not None:
+ yield finish
+
+ @staticmethod
+ def read_finish_reason(choice: dict) -> Optional[FinishReason]:
+ if "finish_reason" in choice and choice["finish_reason"] is not None:
+ return FinishReason(choice["finish_reason"])
+
+ @staticmethod
+ def raise_error(data: dict):
+ if "error_message" in data:
+ raise ResponseError(data["error_message"])
+ elif "error" in data:
+ raise ResponseError(f'Error {data["error"]["code"]}: {data["error"]["message"]}')
+
+ @classmethod
+ def get_headers(cls, stream: bool, api_key: str = None, headers: dict = None) -> dict:
+ return {
+ "Accept": "text/event-stream" if stream else "application/json",
+ "Content-Type": "application/json",
+ **(
+ {"Authorization": f"Bearer {api_key}"}
+ if api_key is not None else {}
+ ),
+ **({} if headers is None else headers)
+ }
diff --git a/g4f/Provider/needs_auth/OpenaiAccount.py b/g4f/Provider/needs_auth/OpenaiAccount.py
new file mode 100644
index 0000000000000000000000000000000000000000..16bfff66d895bbe43b793a9cba47b6f585ae0089
--- /dev/null
+++ b/g4f/Provider/needs_auth/OpenaiAccount.py
@@ -0,0 +1,8 @@
+from __future__ import annotations
+
+from .OpenaiChat import OpenaiChat
+
+class OpenaiAccount(OpenaiChat):
+ needs_auth = True
+ parent = "OpenaiChat"
+ image_models = ["dall-e"]
\ No newline at end of file
diff --git a/g4f/Provider/needs_auth/OpenaiChat.py b/g4f/Provider/needs_auth/OpenaiChat.py
new file mode 100644
index 0000000000000000000000000000000000000000..37bdf0742c8c20cacff281182872af53365382cf
--- /dev/null
+++ b/g4f/Provider/needs_auth/OpenaiChat.py
@@ -0,0 +1,558 @@
+from __future__ import annotations
+
+import re
+import asyncio
+import uuid
+import json
+import base64
+import time
+import requests
+from copy import copy
+
+try:
+ import nodriver
+ from nodriver.cdp.network import get_response_body
+ has_nodriver = True
+except ImportError:
+ has_nodriver = False
+
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ...typing import AsyncResult, Messages, Cookies, ImageType, AsyncIterator
+from ...requests.raise_for_status import raise_for_status
+from ...requests import StreamSession
+from ...requests import get_nodriver
+from ...image import ImageResponse, ImageRequest, to_image, to_bytes, is_accepted_format
+from ...errors import MissingAuthError
+from ...providers.response import BaseConversation, FinishReason, SynthesizeData
+from ..helper import format_cookies
+from ..openai.har_file import get_request_config, NoValidHarFileError
+from ..openai.har_file import RequestConfig, arkReq, arkose_url, start_url, conversation_url, backend_url, backend_anon_url
+from ..openai.proofofwork import generate_proof_token
+from ..openai.new import get_requirements_token
+from ... import debug
+
+DEFAULT_HEADERS = {
+ "accept": "*/*",
+ "accept-encoding": "gzip, deflate, br, zstd",
+ "accept-language": "en-US,en;q=0.5",
+ "referer": "https://chatgpt.com/",
+ "sec-ch-ua": "\"Brave\";v=\"123\", \"Not:A-Brand\";v=\"8\", \"Chromium\";v=\"123\"",
+ "sec-ch-ua-mobile": "?0",
+ "sec-ch-ua-platform": "\"Windows\"",
+ "sec-fetch-dest": "empty",
+ "sec-fetch-mode": "cors",
+ "sec-fetch-site": "same-origin",
+ "sec-gpc": "1",
+ "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36"
+}
+
+class OpenaiChat(AsyncGeneratorProvider, ProviderModelMixin):
+ """A class for creating and managing conversations with OpenAI chat service"""
+
+ label = "OpenAI ChatGPT"
+ url = "https://chatgpt.com"
+ working = True
+ needs_auth = True
+ supports_gpt_4 = True
+ supports_message_history = True
+ supports_system_message = True
+ default_model = "auto"
+ default_vision_model = "gpt-4o"
+ fallback_models = [default_model, "gpt-4", "gpt-4o", "gpt-4o-mini", "gpt-4o-canmore", "o1-preview", "o1-mini"]
+ vision_models = fallback_models
+ image_models = fallback_models
+ synthesize_content_type = "audio/mpeg"
+
+ _api_key: str = None
+ _headers: dict = None
+ _cookies: Cookies = None
+ _expires: int = None
+
+ @classmethod
+ def get_models(cls):
+ if not cls.models:
+ try:
+ response = requests.get(f"{cls.url}/backend-anon/models")
+ response.raise_for_status()
+ data = response.json()
+ cls.models = [model.get("slug") for model in data.get("models")]
+ except Exception:
+ cls.models = cls.fallback_models
+ return cls.models
+
+ @classmethod
+ async def upload_image(
+ cls,
+ session: StreamSession,
+ headers: dict,
+ image: ImageType,
+ image_name: str = None
+ ) -> ImageRequest:
+ """
+ Upload an image to the service and get the download URL
+
+ Args:
+ session: The StreamSession object to use for requests
+ headers: The headers to include in the requests
+ image: The image to upload, either a PIL Image object or a bytes object
+
+ Returns:
+ An ImageRequest object that contains the download URL, file name, and other data
+ """
+ # Convert the image to a PIL Image object and get the extension
+ data_bytes = to_bytes(image)
+ image = to_image(data_bytes)
+ extension = image.format.lower()
+ data = {
+ "file_name": "" if image_name is None else image_name,
+ "file_size": len(data_bytes),
+ "use_case": "multimodal"
+ }
+ # Post the image data to the service and get the image data
+ async with session.post(f"{cls.url}/backend-api/files", json=data, headers=headers) as response:
+ cls._update_request_args(session)
+ await raise_for_status(response, "Create file failed")
+ image_data = {
+ **data,
+ **await response.json(),
+ "mime_type": is_accepted_format(data_bytes),
+ "extension": extension,
+ "height": image.height,
+ "width": image.width
+ }
+ # Put the image bytes to the upload URL and check the status
+ async with session.put(
+ image_data["upload_url"],
+ data=data_bytes,
+ headers={
+ "Content-Type": image_data["mime_type"],
+ "x-ms-blob-type": "BlockBlob"
+ }
+ ) as response:
+ await raise_for_status(response, "Send file failed")
+ # Post the file ID to the service and get the download URL
+ async with session.post(
+ f"{cls.url}/backend-api/files/{image_data['file_id']}/uploaded",
+ json={},
+ headers=headers
+ ) as response:
+ cls._update_request_args(session)
+ await raise_for_status(response, "Get download url failed")
+ image_data["download_url"] = (await response.json())["download_url"]
+ return ImageRequest(image_data)
+
+ @classmethod
+ def create_messages(cls, messages: Messages, image_request: ImageRequest = None, system_hints: list = None):
+ """
+ Create a list of messages for the user input
+
+ Args:
+ prompt: The user input as a string
+ image_response: The image response object, if any
+
+ Returns:
+ A list of messages with the user input and the image, if any
+ """
+ # Create a message object with the user role and the content
+ messages = [{
+ "author": {"role": message["role"]},
+ "content": {"content_type": "text", "parts": [message["content"]]},
+ "id": str(uuid.uuid4()),
+ "create_time": int(time.time()),
+ "id": str(uuid.uuid4()),
+ "metadata": {"serialization_metadata": {"custom_symbol_offsets": []}, "system_hints": system_hints},
+ } for message in messages]
+
+ # Check if there is an image response
+ if image_request is not None:
+ # Change content in last user message
+ messages[-1]["content"] = {
+ "content_type": "multimodal_text",
+ "parts": [{
+ "asset_pointer": f"file-service://{image_request.get('file_id')}",
+ "height": image_request.get("height"),
+ "size_bytes": image_request.get("file_size"),
+ "width": image_request.get("width"),
+ }, messages[-1]["content"]["parts"][0]]
+ }
+ # Add the metadata object with the attachments
+ messages[-1]["metadata"] = {
+ "attachments": [{
+ "height": image_request.get("height"),
+ "id": image_request.get("file_id"),
+ "mimeType": image_request.get("mime_type"),
+ "name": image_request.get("file_name"),
+ "size": image_request.get("file_size"),
+ "width": image_request.get("width"),
+ }]
+ }
+ return messages
+
+ @classmethod
+ async def get_generated_image(cls, session: StreamSession, headers: dict, element: dict, prompt: str = None) -> ImageResponse:
+ """
+ Retrieves the image response based on the message content.
+
+ This method processes the message content to extract image information and retrieves the
+ corresponding image from the backend API. It then returns an ImageResponse object containing
+ the image URL and the prompt used to generate the image.
+
+ Args:
+ session (StreamSession): The StreamSession object used for making HTTP requests.
+ headers (dict): HTTP headers to be used for the request.
+ line (dict): A dictionary representing the line of response that contains image information.
+
+ Returns:
+ ImageResponse: An object containing the image URL and the prompt, or None if no image is found.
+
+ Raises:
+ RuntimeError: If there'san error in downloading the image, including issues with the HTTP request or response.
+ """
+ try:
+ prompt = element["metadata"]["dalle"]["prompt"]
+ file_id = element["asset_pointer"].split("file-service://", 1)[1]
+ except TypeError:
+ return
+ except Exception as e:
+ raise RuntimeError(f"No Image: {e.__class__.__name__}: {e}")
+ try:
+ async with session.get(f"{cls.url}/backend-api/files/{file_id}/download", headers=headers) as response:
+ cls._update_request_args(session)
+ await raise_for_status(response)
+ download_url = (await response.json())["download_url"]
+ return ImageResponse(download_url, prompt)
+ except Exception as e:
+ raise RuntimeError(f"Error in downloading image: {e}")
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ timeout: int = 180,
+ cookies: Cookies = None,
+ auto_continue: bool = False,
+ history_disabled: bool = False,
+ action: str = "next",
+ conversation_id: str = None,
+ conversation: Conversation = None,
+ parent_id: str = None,
+ image: ImageType = None,
+ image_name: str = None,
+ return_conversation: bool = False,
+ max_retries: int = 3,
+ web_search: bool = False,
+ **kwargs
+ ) -> AsyncResult:
+ """
+ Create an asynchronous generator for the conversation.
+
+ Args:
+ model (str): The model name.
+ messages (Messages): The list of previous messages.
+ proxy (str): Proxy to use for requests.
+ timeout (int): Timeout for requests.
+ api_key (str): Access token for authentication.
+ cookies (dict): Cookies to use for authentication.
+ auto_continue (bool): Flag to automatically continue the conversation.
+ history_disabled (bool): Flag to disable history and training.
+ action (str): Type of action ('next', 'continue', 'variant').
+ conversation_id (str): ID of the conversation.
+ parent_id (str): ID of the parent message.
+ image (ImageType): Image to include in the conversation.
+ return_conversation (bool): Flag to include response fields in the output.
+ **kwargs: Additional keyword arguments.
+
+ Yields:
+ AsyncResult: Asynchronous results from the generator.
+
+ Raises:
+ RuntimeError: If an error occurs during processing.
+ """
+ await cls.login(proxy)
+
+ async with StreamSession(
+ proxy=proxy,
+ impersonate="chrome",
+ timeout=timeout
+ ) as session:
+ try:
+ image_request = await cls.upload_image(session, cls._headers, image, image_name) if image else None
+ except Exception as e:
+ image_request = None
+ debug.log("OpenaiChat: Upload image failed")
+ debug.log(f"{e.__class__.__name__}: {e}")
+ model = cls.get_model(model)
+ if conversation is None:
+ conversation = Conversation(conversation_id, str(uuid.uuid4()) if parent_id is None else parent_id)
+ else:
+ conversation = copy(conversation)
+ if cls._api_key is None:
+ auto_continue = False
+ conversation.finish_reason = None
+ while conversation.finish_reason is None:
+ async with session.post(
+ f"{cls.url}/backend-anon/sentinel/chat-requirements"
+ if cls._api_key is None else
+ f"{cls.url}/backend-api/sentinel/chat-requirements",
+ json={"p": get_requirements_token(RequestConfig.proof_token) if RequestConfig.proof_token else None},
+ headers=cls._headers
+ ) as response:
+ cls._update_request_args(session)
+ await raise_for_status(response)
+ chat_requirements = await response.json()
+ need_turnstile = chat_requirements.get("turnstile", {}).get("required", False)
+ need_arkose = chat_requirements.get("arkose", {}).get("required", False)
+ chat_token = chat_requirements.get("token")
+
+ if need_arkose and RequestConfig.arkose_token is None:
+ await get_request_config(proxy)
+ cls._create_request_args(RequestConfig,cookies, RequestConfig.headers)
+ cls._set_api_key(RequestConfig.access_token)
+ if RequestConfig.arkose_token is None:
+ raise MissingAuthError("No arkose token found in .har file")
+
+ if "proofofwork" in chat_requirements:
+ proofofwork = generate_proof_token(
+ **chat_requirements["proofofwork"],
+ user_agent=cls._headers.get("user-agent"),
+ proof_token=RequestConfig.proof_token
+ )
+ [debug.log(text) for text in (
+ f"Arkose: {'False' if not need_arkose else RequestConfig.arkose_token[:12]+'...'}",
+ f"Proofofwork: {'False' if proofofwork is None else proofofwork[:12]+'...'}",
+ f"AccessToken: {'False' if cls._api_key is None else cls._api_key[:12]+'...'}",
+ )]
+ data = {
+ "action": action,
+ "messages": None,
+ "parent_message_id": conversation.message_id,
+ "model": model,
+ "paragen_cot_summary_display_override": "allow",
+ "history_and_training_disabled": history_disabled and not auto_continue and not return_conversation,
+ "conversation_mode": {"kind":"primary_assistant"},
+ "websocket_request_id": str(uuid.uuid4()),
+ "supported_encodings": ["v1"],
+ "supports_buffering": True,
+ "system_hints": ["search"] if web_search else None
+ }
+ if conversation.conversation_id is not None:
+ data["conversation_id"] = conversation.conversation_id
+ debug.log(f"OpenaiChat: Use conversation: {conversation.conversation_id}")
+ if action != "continue":
+ messages = messages if conversation_id is None else [messages[-1]]
+ data["messages"] = cls.create_messages(messages, image_request, ["search"] if web_search else None)
+ headers = {
+ **cls._headers,
+ "accept": "text/event-stream",
+ "content-type": "application/json",
+ "openai-sentinel-chat-requirements-token": chat_token,
+ }
+ if RequestConfig.arkose_token:
+ headers["openai-sentinel-arkose-token"] = RequestConfig.arkose_token
+ if proofofwork is not None:
+ headers["openai-sentinel-proof-token"] = proofofwork
+ if need_turnstile and RequestConfig.turnstile_token is not None:
+ headers['openai-sentinel-turnstile-token'] = RequestConfig.turnstile_token
+ async with session.post(
+ f"{cls.url}/backend-anon/conversation"
+ if cls._api_key is None else
+ f"{cls.url}/backend-api/conversation",
+ json=data,
+ headers=headers
+ ) as response:
+ cls._update_request_args(session)
+ if response.status == 403 and max_retries > 0:
+ max_retries -= 1
+ debug.log(f"Retry: Error {response.status}: {await response.text()}")
+ await asyncio.sleep(5)
+ continue
+ await raise_for_status(response)
+ if return_conversation:
+ yield conversation
+ async for line in response.iter_lines():
+ async for chunk in cls.iter_messages_line(session, line, conversation):
+ yield chunk
+ if not history_disabled:
+ yield SynthesizeData(cls.__name__, {
+ "conversation_id": conversation.conversation_id,
+ "message_id": conversation.message_id,
+ "voice": "maple",
+ })
+ if auto_continue and conversation.finish_reason == "max_tokens":
+ conversation.finish_reason = None
+ action = "continue"
+ await asyncio.sleep(5)
+ else:
+ break
+ yield FinishReason(conversation.finish_reason)
+
+ @classmethod
+ async def iter_messages_line(cls, session: StreamSession, line: bytes, fields: Conversation) -> AsyncIterator:
+ if not line.startswith(b"data: "):
+ return
+ elif line.startswith(b"data: [DONE]"):
+ if fields.finish_reason is None:
+ fields.finish_reason = "error"
+ return
+ try:
+ line = json.loads(line[6:])
+ except:
+ return
+ if isinstance(line, dict) and "v" in line:
+ v = line.get("v")
+ if isinstance(v, str) and fields.is_recipient:
+ yield v
+ elif isinstance(v, list) and fields.is_recipient:
+ for m in v:
+ if m.get("p") == "/message/content/parts/0":
+ yield m.get("v")
+ elif m.get("p") == "/message/metadata":
+ fields.finish_reason = m.get("v", {}).get("finish_details", {}).get("type")
+ break
+ elif isinstance(v, dict):
+ if fields.conversation_id is None:
+ fields.conversation_id = v.get("conversation_id")
+ debug.log(f"OpenaiChat: New conversation: {fields.conversation_id}")
+ m = v.get("message", {})
+ fields.is_recipient = m.get("recipient") == "all"
+ if fields.is_recipient:
+ c = m.get("content", {})
+ if c.get("content_type") == "multimodal_text":
+ generated_images = []
+ for element in c.get("parts"):
+ if isinstance(element, dict) and element.get("content_type") == "image_asset_pointer":
+ image = cls.get_generated_image(session, cls._headers, element)
+ if image is not None:
+ generated_images.append(image)
+ for image_response in await asyncio.gather(*generated_images):
+ yield image_response
+ if m.get("author", {}).get("role") == "assistant":
+ fields.message_id = v.get("message", {}).get("id")
+ return
+ if "error" in line and line.get("error"):
+ raise RuntimeError(line.get("error"))
+
+ @classmethod
+ async def synthesize(cls, params: dict) -> AsyncIterator[bytes]:
+ await cls.login()
+ async with StreamSession(
+ impersonate="chrome",
+ timeout=900
+ ) as session:
+ async with session.get(
+ f"{cls.url}/backend-api/synthesize",
+ params=params,
+ headers=cls._headers
+ ) as response:
+ await raise_for_status(response)
+ async for chunk in response.iter_content():
+ yield chunk
+
+ @classmethod
+ async def login(cls, proxy: str = None):
+ if cls._expires is not None and cls._expires < time.time():
+ cls._headers = cls._api_key = None
+ try:
+ await get_request_config(proxy)
+ cls._create_request_args(RequestConfig.cookies, RequestConfig.headers)
+ cls._set_api_key(RequestConfig.access_token)
+ except NoValidHarFileError:
+ if has_nodriver:
+ await cls.nodriver_auth(proxy)
+ else:
+ raise
+
+ @classmethod
+ async def nodriver_auth(cls, proxy: str = None):
+ browser = await get_nodriver(proxy=proxy)
+ page = browser.main_tab
+ def on_request(event: nodriver.cdp.network.RequestWillBeSent):
+ if event.request.url == start_url or event.request.url.startswith(conversation_url):
+ RequestConfig.access_request_id = event.request_id
+ RequestConfig.headers = event.request.headers
+ elif event.request.url in (backend_url, backend_anon_url):
+ if "OpenAI-Sentinel-Proof-Token" in event.request.headers:
+ RequestConfig.proof_token = json.loads(base64.b64decode(
+ event.request.headers["OpenAI-Sentinel-Proof-Token"].split("gAAAAAB", 1)[-1].encode()
+ ).decode())
+ if "OpenAI-Sentinel-Turnstile-Token" in event.request.headers:
+ RequestConfig.turnstile_token = event.request.headers["OpenAI-Sentinel-Turnstile-Token"]
+ if "Authorization" in event.request.headers:
+ RequestConfig.access_token = event.request.headers["Authorization"].split()[-1]
+ elif event.request.url == arkose_url:
+ RequestConfig.arkose_request = arkReq(
+ arkURL=event.request.url,
+ arkBx=None,
+ arkHeader=event.request.headers,
+ arkBody=event.request.post_data,
+ userAgent=event.request.headers.get("user-agent")
+ )
+ await page.send(nodriver.cdp.network.enable())
+ page.add_handler(nodriver.cdp.network.RequestWillBeSent, on_request)
+ page = await browser.get(cls.url)
+ try:
+ if RequestConfig.access_request_id is not None:
+ body = await page.send(get_response_body(RequestConfig.access_request_id))
+ if isinstance(body, tuple) and body:
+ body = body[0]
+ if body:
+ match = re.search(r'"accessToken":"(.*?)"', body)
+ if match:
+ RequestConfig.access_token = match.group(1)
+ except KeyError:
+ pass
+ for c in await page.send(nodriver.cdp.network.get_cookies([cls.url])):
+ RequestConfig.cookies[c.name] = c.value
+ user_agent = await page.evaluate("window.navigator.userAgent")
+ await page.select("#prompt-textarea", 240)
+ while True:
+ if RequestConfig.proof_token:
+ break
+ await asyncio.sleep(1)
+ await page.close()
+ cls._create_request_args(RequestConfig.cookies, RequestConfig.headers, user_agent=user_agent)
+ cls._set_api_key(RequestConfig.access_token)
+
+ @staticmethod
+ def get_default_headers() -> dict:
+ return {
+ **DEFAULT_HEADERS,
+ "content-type": "application/json",
+ }
+
+ @classmethod
+ def _create_request_args(cls, cookies: Cookies = None, headers: dict = None, user_agent: str = None):
+ cls._headers = cls.get_default_headers() if headers is None else headers
+ if user_agent is not None:
+ cls._headers["user-agent"] = user_agent
+ cls._cookies = {} if cookies is None else cookies
+ cls._update_cookie_header()
+
+ @classmethod
+ def _update_request_args(cls, session: StreamSession):
+ for c in session.cookie_jar if hasattr(session, "cookie_jar") else session.cookies.jar:
+ cls._cookies[c.key if hasattr(c, "key") else c.name] = c.value
+ cls._update_cookie_header()
+
+ @classmethod
+ def _set_api_key(cls, api_key: str):
+ cls._api_key = api_key
+ cls._expires = int(time.time()) + 60 * 60 * 4
+ if api_key:
+ cls._headers["authorization"] = f"Bearer {api_key}"
+
+ @classmethod
+ def _update_cookie_header(cls):
+ cls._headers["cookie"] = format_cookies(cls._cookies)
+
+class Conversation(BaseConversation):
+ """
+ Class to encapsulate response fields.
+ """
+ def __init__(self, conversation_id: str = None, message_id: str = None, finish_reason: str = None):
+ self.conversation_id = conversation_id
+ self.message_id = message_id
+ self.finish_reason = finish_reason
+ self.is_recipient = False
\ No newline at end of file
diff --git a/g4f/Provider/needs_auth/PerplexityApi.py b/g4f/Provider/needs_auth/PerplexityApi.py
new file mode 100644
index 0000000000000000000000000000000000000000..85d7cc98aa65a45e9340b39fcb6e3d98ec9bd13b
--- /dev/null
+++ b/g4f/Provider/needs_auth/PerplexityApi.py
@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from .OpenaiAPI import OpenaiAPI
+from ...typing import AsyncResult, Messages
+
+class PerplexityApi(OpenaiAPI):
+ label = "Perplexity API"
+ url = "https://www.perplexity.ai"
+ working = True
+ default_model = "llama-3-sonar-large-32k-online"
+ models = [
+ "llama-3-sonar-small-32k-chat",
+ "llama-3-sonar-small-32k-online",
+ "llama-3-sonar-large-32k-chat",
+ "llama-3-sonar-large-32k-online",
+ "llama-3-8b-instruct",
+ "llama-3-70b-instruct",
+ ]
+
+ @classmethod
+ def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ api_base: str = "https://api.perplexity.ai",
+ **kwargs
+ ) -> AsyncResult:
+ return super().create_async_generator(
+ model, messages, api_base=api_base, **kwargs
+ )
diff --git a/g4f/Provider/needs_auth/Poe.py b/g4f/Provider/needs_auth/Poe.py
new file mode 100644
index 0000000000000000000000000000000000000000..65fdbef97212a245a143d17a09bb43dd3cc1b415
--- /dev/null
+++ b/g4f/Provider/needs_auth/Poe.py
@@ -0,0 +1,116 @@
+from __future__ import annotations
+
+import time
+
+from ...typing import CreateResult, Messages
+from ..base_provider import AbstractProvider
+from ..helper import format_prompt
+from ...webdriver import WebDriver, WebDriverSession, element_send_text
+
+models = {
+ "meta-llama/Llama-2-7b-chat-hf": {"name": "Llama-2-7b"},
+ "meta-llama/Llama-2-13b-chat-hf": {"name": "Llama-2-13b"},
+ "meta-llama/Llama-2-70b-chat-hf": {"name": "Llama-2-70b"},
+ "codellama/CodeLlama-7b-Instruct-hf": {"name": "Code-Llama-7b"},
+ "codellama/CodeLlama-13b-Instruct-hf": {"name": "Code-Llama-13b"},
+ "codellama/CodeLlama-34b-Instruct-hf": {"name": "Code-Llama-34b"},
+ "gpt-3.5-turbo": {"name": "GPT-3.5-Turbo"},
+ "gpt-3.5-turbo-instruct": {"name": "GPT-3.5-Turbo-Instruct"},
+ "gpt-4": {"name": "GPT-4"},
+ "palm": {"name": "Google-PaLM"},
+}
+
+class Poe(AbstractProvider):
+ url = "https://poe.com"
+ working = True
+ needs_auth = True
+ supports_gpt_35_turbo = True
+ supports_stream = True
+ models = models.keys()
+
+ @classmethod
+ def create_completion(
+ cls,
+ model: str,
+ messages: Messages,
+ stream: bool,
+ proxy: str = None,
+ webdriver: WebDriver = None,
+ user_data_dir: str = None,
+ headless: bool = True,
+ **kwargs
+ ) -> CreateResult:
+ if not model:
+ model = "gpt-3.5-turbo"
+ elif model not in models:
+ raise ValueError(f"Model are not supported: {model}")
+ prompt = format_prompt(messages)
+
+ session = WebDriverSession(webdriver, user_data_dir, headless, proxy=proxy)
+ with session as driver:
+ from selenium.webdriver.common.by import By
+ from selenium.webdriver.support.ui import WebDriverWait
+ from selenium.webdriver.support import expected_conditions as EC
+
+ driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument", {
+ "source": """
+ window._message = window._last_message = "";
+ window._message_finished = false;
+ class ProxiedWebSocket extends WebSocket {
+ constructor(url, options) {
+ super(url, options);
+ this.addEventListener("message", (e) => {
+ const data = JSON.parse(JSON.parse(e.data)["messages"][0])["payload"]["data"];
+ if ("messageAdded" in data) {
+ if (data["messageAdded"]["author"] != "human") {
+ window._message = data["messageAdded"]["text"];
+ if (data["messageAdded"]["state"] == "complete") {
+ window._message_finished = true;
+ }
+ }
+ }
+ });
+ }
+ }
+ window.WebSocket = ProxiedWebSocket;
+ """
+ })
+
+ try:
+ driver.get(f"{cls.url}/{models[model]['name']}")
+ wait = WebDriverWait(driver, 10 if headless else 240)
+ wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, "textarea[class^='GrowingTextArea']")))
+ except:
+ # Reopen browser for login
+ if not webdriver:
+ driver = session.reopen()
+ driver.get(f"{cls.url}/{models[model]['name']}")
+ wait = WebDriverWait(driver, 240)
+ wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, "textarea[class^='GrowingTextArea']")))
+ else:
+ raise RuntimeError("Prompt textarea not found. You may not be logged in.")
+
+ element_send_text(driver.find_element(By.CSS_SELECTOR, "footer textarea[class^='GrowingTextArea']"), prompt)
+ driver.find_element(By.CSS_SELECTOR, "footer button[class*='ChatMessageSendButton']").click()
+
+ script = """
+if(window._message && window._message != window._last_message) {
+ try {
+ return window._message.substring(window._last_message.length);
+ } finally {
+ window._last_message = window._message;
+ }
+} else if(window._message_finished) {
+ return null;
+} else {
+ return '';
+}
+"""
+ while True:
+ chunk = driver.execute_script(script)
+ if chunk:
+ yield chunk
+ elif chunk != "":
+ break
+ else:
+ time.sleep(0.1)
\ No newline at end of file
diff --git a/g4f/Provider/needs_auth/Raycast.py b/g4f/Provider/needs_auth/Raycast.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8ec5a978e08a40337feb8e744c19a3752b712ac
--- /dev/null
+++ b/g4f/Provider/needs_auth/Raycast.py
@@ -0,0 +1,70 @@
+from __future__ import annotations
+
+import json
+
+import requests
+
+from ...typing import CreateResult, Messages
+from ..base_provider import AbstractProvider
+
+
+class Raycast(AbstractProvider):
+ url = "https://raycast.com"
+ supports_gpt_35_turbo = True
+ supports_gpt_4 = True
+ supports_stream = True
+ needs_auth = True
+ working = True
+
+ models = [
+ "gpt-3.5-turbo",
+ "gpt-4"
+ ]
+
+ @staticmethod
+ def create_completion(
+ model: str,
+ messages: Messages,
+ stream: bool,
+ proxy: str = None,
+ **kwargs,
+ ) -> CreateResult:
+ auth = kwargs.get('auth')
+ if not auth:
+ raise ValueError("Raycast needs an auth token, pass it with the `auth` parameter")
+
+ headers = {
+ 'Accept': 'application/json',
+ 'Accept-Language': 'en-US,en;q=0.9',
+ 'Authorization': f'Bearer {auth}',
+ 'Content-Type': 'application/json',
+ 'User-Agent': 'Raycast/0 CFNetwork/1410.0.3 Darwin/22.6.0',
+ }
+ parsed_messages = [
+ {'author': message['role'], 'content': {'text': message['content']}}
+ for message in messages
+ ]
+ data = {
+ "debug": False,
+ "locale": "en-CN",
+ "messages": parsed_messages,
+ "model": model,
+ "provider": "openai",
+ "source": "ai_chat",
+ "system_instruction": "markdown",
+ "temperature": 0.5
+ }
+ response = requests.post(
+ "https://backend.raycast.com/api/v1/ai/chat_completions",
+ headers=headers,
+ json=data,
+ stream=True,
+ proxies={"https": proxy}
+ )
+ for token in response.iter_lines():
+ if b'data: ' not in token:
+ continue
+ completion_chunk = json.loads(token.decode().replace('data: ', ''))
+ token = completion_chunk['text']
+ if token != None:
+ yield token
diff --git a/g4f/Provider/needs_auth/Replicate.py b/g4f/Provider/needs_auth/Replicate.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec993aa4219abb9447a8172d741c3672f89683cb
--- /dev/null
+++ b/g4f/Provider/needs_auth/Replicate.py
@@ -0,0 +1,88 @@
+from __future__ import annotations
+
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import format_prompt, filter_none
+from ...typing import AsyncResult, Messages
+from ...requests import raise_for_status
+from ...requests.aiohttp import StreamSession
+from ...errors import ResponseError, MissingAuthError
+
+class Replicate(AsyncGeneratorProvider, ProviderModelMixin):
+ url = "https://replicate.com"
+ working = True
+ needs_auth = True
+ default_model = "meta/meta-llama-3-70b-instruct"
+ model_aliases = {
+ "meta-llama/Meta-Llama-3-70B-Instruct": default_model
+ }
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ api_key: str = None,
+ proxy: str = None,
+ timeout: int = 180,
+ system_prompt: str = None,
+ max_new_tokens: int = None,
+ temperature: float = None,
+ top_p: float = None,
+ top_k: float = None,
+ stop: list = None,
+ extra_data: dict = {},
+ headers: dict = {
+ "accept": "application/json",
+ },
+ **kwargs
+ ) -> AsyncResult:
+ model = cls.get_model(model)
+ if cls.needs_auth and api_key is None:
+ raise MissingAuthError("api_key is missing")
+ if api_key is not None:
+ headers["Authorization"] = f"Bearer {api_key}"
+ api_base = "https://api.replicate.com/v1/models/"
+ else:
+ api_base = "https://replicate.com/api/models/"
+ async with StreamSession(
+ proxy=proxy,
+ headers=headers,
+ timeout=timeout
+ ) as session:
+ data = {
+ "stream": True,
+ "input": {
+ "prompt": format_prompt(messages),
+ **filter_none(
+ system_prompt=system_prompt,
+ max_new_tokens=max_new_tokens,
+ temperature=temperature,
+ top_p=top_p,
+ top_k=top_k,
+ stop_sequences=",".join(stop) if stop else None
+ ),
+ **extra_data
+ },
+ }
+ url = f"{api_base.rstrip('/')}/{model}/predictions"
+ async with session.post(url, json=data) as response:
+ message = "Model not found" if response.status == 404 else None
+ await raise_for_status(response, message)
+ result = await response.json()
+ if "id" not in result:
+ raise ResponseError(f"Invalid response: {result}")
+ async with session.get(result["urls"]["stream"], headers={"Accept": "text/event-stream"}) as response:
+ await raise_for_status(response)
+ event = None
+ async for line in response.iter_lines():
+ if line.startswith(b"event: "):
+ event = line[7:]
+ if event == b"done":
+ break
+ elif event == b"output":
+ if line.startswith(b"data: "):
+ new_text = line[6:].decode()
+ if new_text:
+ yield new_text
+ else:
+ yield "\n"
diff --git a/g4f/Provider/needs_auth/Theb.py b/g4f/Provider/needs_auth/Theb.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7d7d58efb17afee3f75f4cdfab90d1161e1d29c
--- /dev/null
+++ b/g4f/Provider/needs_auth/Theb.py
@@ -0,0 +1,158 @@
+from __future__ import annotations
+
+import time
+
+from ...typing import CreateResult, Messages
+from ..base_provider import AbstractProvider
+from ..helper import format_prompt
+from ...webdriver import WebDriver, WebDriverSession, element_send_text
+
+models = {
+ "theb-ai": "TheB.AI",
+ "theb-ai-free": "TheB.AI Free",
+ "gpt-3.5-turbo": "GPT-3.5 Turbo (New)",
+ "gpt-3.5-turbo-16k": "GPT-3.5-16K",
+ "gpt-4-turbo": "GPT-4 Turbo",
+ "gpt-4": "GPT-4",
+ "gpt-4-32k": "GPT-4 32K",
+ "claude-2": "Claude 2",
+ "claude-instant-1": "Claude Instant 1.2",
+ "palm-2": "PaLM 2",
+ "palm-2-32k": "PaLM 2 32K",
+ "palm-2-codey": "Codey",
+ "palm-2-codey-32k": "Codey 32K",
+ "vicuna-13b-v1.5": "Vicuna v1.5 13B",
+ "llama-2-7b-chat": "Llama 2 7B",
+ "llama-2-13b-chat": "Llama 2 13B",
+ "llama-2-70b-chat": "Llama 2 70B",
+ "code-llama-7b": "Code Llama 7B",
+ "code-llama-13b": "Code Llama 13B",
+ "code-llama-34b": "Code Llama 34B",
+ "qwen-7b-chat": "Qwen 7B"
+}
+
+class Theb(AbstractProvider):
+ label = "TheB.AI"
+ url = "https://beta.theb.ai"
+ working = True
+ supports_gpt_35_turbo = True
+ supports_gpt_4 = True
+ supports_stream = True
+ models = models.keys()
+
+ @classmethod
+ def create_completion(
+ cls,
+ model: str,
+ messages: Messages,
+ stream: bool,
+ proxy: str = None,
+ webdriver: WebDriver = None,
+ virtual_display: bool = True,
+ **kwargs
+ ) -> CreateResult:
+ if model in models:
+ model = models[model]
+ prompt = format_prompt(messages)
+ web_session = WebDriverSession(webdriver, virtual_display=virtual_display, proxy=proxy)
+ with web_session as driver:
+ from selenium.webdriver.common.by import By
+ from selenium.webdriver.support.ui import WebDriverWait
+ from selenium.webdriver.support import expected_conditions as EC
+ from selenium.webdriver.common.keys import Keys
+
+ # Register fetch hook
+ script = """
+window._fetch = window.fetch;
+window.fetch = async (url, options) => {
+ // Call parent fetch method
+ const response = await window._fetch(url, options);
+ if (!url.startsWith("/api/conversation")) {
+ return result;
+ }
+ // Copy response
+ copy = response.clone();
+ window._reader = response.body.pipeThrough(new TextDecoderStream()).getReader();
+ return copy;
+}
+window._last_message = "";
+"""
+ driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument", {
+ "source": script
+ })
+
+ try:
+ driver.get(f"{cls.url}/home")
+ wait = WebDriverWait(driver, 5)
+ wait.until(EC.visibility_of_element_located((By.ID, "textareaAutosize")))
+ except:
+ driver = web_session.reopen()
+ driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument", {
+ "source": script
+ })
+ driver.get(f"{cls.url}/home")
+ wait = WebDriverWait(driver, 240)
+ wait.until(EC.visibility_of_element_located((By.ID, "textareaAutosize")))
+
+ try:
+ driver.find_element(By.CSS_SELECTOR, ".driver-overlay").click()
+ driver.find_element(By.CSS_SELECTOR, ".driver-overlay").click()
+ except:
+ pass
+ if model:
+ # Load model panel
+ wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, "#SelectModel svg")))
+ time.sleep(0.1)
+ driver.find_element(By.CSS_SELECTOR, "#SelectModel svg").click()
+ try:
+ driver.find_element(By.CSS_SELECTOR, ".driver-overlay").click()
+ driver.find_element(By.CSS_SELECTOR, ".driver-overlay").click()
+ except:
+ pass
+ # Select model
+ selector = f"div.flex-col div.items-center span[title='{model}']"
+ wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, selector)))
+ span = driver.find_element(By.CSS_SELECTOR, selector)
+ container = span.find_element(By.XPATH, "//div/../..")
+ button = container.find_element(By.CSS_SELECTOR, "button.btn-blue.btn-small.border")
+ button.click()
+
+
+ # Submit prompt
+ wait.until(EC.visibility_of_element_located((By.ID, "textareaAutosize")))
+ element_send_text(driver.find_element(By.ID, "textareaAutosize"), prompt)
+
+ # Read response with reader
+ script = """
+if(window._reader) {
+ chunk = await window._reader.read();
+ if (chunk['done']) {
+ return null;
+ }
+ message = '';
+ chunk['value'].split('\\r\\n').forEach((line, index) => {
+ if (line.startsWith('data: ')) {
+ try {
+ line = JSON.parse(line.substring('data: '.length));
+ message = line["args"]["content"];
+ } catch(e) { }
+ }
+ });
+ if (message) {
+ try {
+ return message.substring(window._last_message.length);
+ } finally {
+ window._last_message = message;
+ }
+ }
+}
+return '';
+"""
+ while True:
+ chunk = driver.execute_script(script)
+ if chunk:
+ yield chunk
+ elif chunk != "":
+ break
+ else:
+ time.sleep(0.1)
\ No newline at end of file
diff --git a/g4f/Provider/needs_auth/ThebApi.py b/g4f/Provider/needs_auth/ThebApi.py
new file mode 100644
index 0000000000000000000000000000000000000000..2006f7ad48206249b8b290150a31625968e1cb1c
--- /dev/null
+++ b/g4f/Provider/needs_auth/ThebApi.py
@@ -0,0 +1,61 @@
+from __future__ import annotations
+
+from ...typing import CreateResult, Messages
+from .OpenaiAPI import OpenaiAPI
+
+models = {
+ "theb-ai": "TheB.AI",
+ "gpt-3.5-turbo": "GPT-3.5",
+ "gpt-3.5-turbo-16k": "GPT-3.5-16K",
+ "gpt-4-turbo": "GPT-4 Turbo",
+ "gpt-4": "GPT-4",
+ "gpt-4-32k": "GPT-4 32K",
+ "claude-2": "Claude 2",
+ "claude-1": "Claude",
+ "claude-1-100k": "Claude 100K",
+ "claude-instant-1": "Claude Instant",
+ "claude-instant-1-100k": "Claude Instant 100K",
+ "palm-2": "PaLM 2",
+ "palm-2-codey": "Codey",
+ "vicuna-13b-v1.5": "Vicuna v1.5 13B",
+ "llama-2-7b-chat": "Llama 2 7B",
+ "llama-2-13b-chat": "Llama 2 13B",
+ "llama-2-70b-chat": "Llama 2 70B",
+ "code-llama-7b": "Code Llama 7B",
+ "code-llama-13b": "Code Llama 13B",
+ "code-llama-34b": "Code Llama 34B",
+ "qwen-7b-chat": "Qwen 7B"
+}
+
+class ThebApi(OpenaiAPI):
+ label = "TheB.AI API"
+ url = "https://theb.ai"
+ working = True
+ needs_auth = True
+ default_model = "gpt-3.5-turbo"
+ models = list(models)
+
+ @classmethod
+ def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ api_base: str = "https://api.theb.ai/v1",
+ temperature: float = 1,
+ top_p: float = 1,
+ **kwargs
+ ) -> CreateResult:
+ if "auth" in kwargs:
+ kwargs["api_key"] = kwargs["auth"]
+ system_message = "\n".join([message["content"] for message in messages if message["role"] == "system"])
+ if not system_message:
+ system_message = "You are ChatGPT, a large language model trained by OpenAI, based on the GPT-3.5 architecture."
+ messages = [message for message in messages if message["role"] != "system"]
+ data = {
+ "model_params": {
+ "system_prompt": system_message,
+ "temperature": temperature,
+ "top_p": top_p,
+ }
+ }
+ return super().create_async_generator(model, messages, api_base=api_base, extra_data=data, **kwargs)
diff --git a/g4f/Provider/needs_auth/WhiteRabbitNeo.py b/g4f/Provider/needs_auth/WhiteRabbitNeo.py
new file mode 100644
index 0000000000000000000000000000000000000000..82275c1c5c6fc6f9ed5141bcd548d4f76911329c
--- /dev/null
+++ b/g4f/Provider/needs_auth/WhiteRabbitNeo.py
@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+from aiohttp import ClientSession, BaseConnector
+
+from ...typing import AsyncResult, Messages, Cookies
+from ...requests.raise_for_status import raise_for_status
+from ..base_provider import AsyncGeneratorProvider
+from ..helper import get_cookies, get_connector, get_random_string
+
+class WhiteRabbitNeo(AsyncGeneratorProvider):
+ url = "https://www.whiterabbitneo.com"
+ working = True
+ supports_message_history = True
+ needs_auth = True
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ cookies: Cookies = None,
+ connector: BaseConnector = None,
+ proxy: str = None,
+ **kwargs
+ ) -> AsyncResult:
+ if cookies is None:
+ cookies = get_cookies("www.whiterabbitneo.com")
+ headers = {
+ "User-Agent": "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:123.0) Gecko/20100101 Firefox/123.0",
+ "Accept": "*/*",
+ "Accept-Language": "de,en-US;q=0.7,en;q=0.3",
+ "Accept-Encoding": "gzip, deflate, br",
+ "Referer": f"{cls.url}/",
+ "Content-Type": "text/plain;charset=UTF-8",
+ "Origin": cls.url,
+ "Connection": "keep-alive",
+ "Sec-Fetch-Dest": "empty",
+ "Sec-Fetch-Mode": "cors",
+ "Sec-Fetch-Site": "same-origin",
+ "TE": "trailers"
+ }
+ async with ClientSession(
+ headers=headers,
+ cookies=cookies,
+ connector=get_connector(connector, proxy)
+ ) as session:
+ data = {
+ "messages": messages,
+ "id": get_random_string(6),
+ "enhancePrompt": False,
+ "useFunctions": False
+ }
+ async with session.post(f"{cls.url}/api/chat", json=data, proxy=proxy) as response:
+ await raise_for_status(response)
+ async for chunk in response.content.iter_any():
+ if chunk:
+ yield chunk.decode(errors="ignore")
diff --git a/g4f/Provider/needs_auth/__init__.py b/g4f/Provider/needs_auth/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f339170616f4c6860bf9fe99fa4a78abc8b925f5
--- /dev/null
+++ b/g4f/Provider/needs_auth/__init__.py
@@ -0,0 +1,24 @@
+from .gigachat import *
+
+from .BingCreateImages import BingCreateImages
+from .Cerebras import Cerebras
+from .CopilotAccount import CopilotAccount
+from .DeepInfra import DeepInfra
+from .DeepInfraImage import DeepInfraImage
+from .Gemini import Gemini
+from .GeminiPro import GeminiPro
+from .GithubCopilot import GithubCopilot
+from .Groq import Groq
+from .HuggingFace import HuggingFace
+from .HuggingFace2 import HuggingFace2
+from .MetaAI import MetaAI
+from .MetaAIAccount import MetaAIAccount
+from .OpenaiAPI import OpenaiAPI
+from .OpenaiChat import OpenaiChat
+from .PerplexityApi import PerplexityApi
+from .Poe import Poe
+from .Raycast import Raycast
+from .Replicate import Replicate
+from .Theb import Theb
+from .ThebApi import ThebApi
+from .WhiteRabbitNeo import WhiteRabbitNeo
diff --git a/g4f/Provider/needs_auth/gigachat/GigaChat.py b/g4f/Provider/needs_auth/gigachat/GigaChat.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9f1c01174ad61e96b1b574bc776c000ca5ad6dd
--- /dev/null
+++ b/g4f/Provider/needs_auth/gigachat/GigaChat.py
@@ -0,0 +1,92 @@
+from __future__ import annotations
+
+import os
+import ssl
+import time
+import uuid
+
+import json
+from aiohttp import ClientSession, TCPConnector, BaseConnector
+from g4f.requests import raise_for_status
+
+from ....typing import AsyncResult, Messages
+from ...base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ....errors import MissingAuthError
+from ...helper import get_connector
+
+access_token = ""
+token_expires_at = 0
+
+class GigaChat(AsyncGeneratorProvider, ProviderModelMixin):
+ url = "https://developers.sber.ru/gigachat"
+ working = True
+ supports_message_history = True
+ supports_system_message = True
+ supports_stream = True
+ needs_auth = True
+ default_model = "GigaChat:latest"
+ models = ["GigaChat:latest", "GigaChat-Plus", "GigaChat-Pro"]
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ stream: bool = True,
+ proxy: str = None,
+ api_key: str = None,
+ connector: BaseConnector = None,
+ scope: str = "GIGACHAT_API_PERS",
+ update_interval: float = 0,
+ **kwargs
+ ) -> AsyncResult:
+ global access_token, token_expires_at
+ model = cls.get_model(model)
+ if not api_key:
+ raise MissingAuthError('Missing "api_key"')
+
+ cafile = os.path.join(os.path.dirname(__file__), "russian_trusted_root_ca_pem.crt")
+ ssl_context = ssl.create_default_context(cafile=cafile) if os.path.exists(cafile) else None
+ if connector is None and ssl_context is not None:
+ connector = TCPConnector(ssl_context=ssl_context)
+ async with ClientSession(connector=get_connector(connector, proxy)) as session:
+ if token_expires_at - int(time.time() * 1000) < 60000:
+ async with session.post(url="https://ngw.devices.sberbank.ru:9443/api/v2/oauth",
+ headers={"Authorization": f"Bearer {api_key}",
+ "RqUID": str(uuid.uuid4()),
+ "Content-Type": "application/x-www-form-urlencoded"},
+ data={"scope": scope}) as response:
+ await raise_for_status(response)
+ data = await response.json()
+ access_token = data['access_token']
+ token_expires_at = data['expires_at']
+
+ async with session.post(url="https://gigachat.devices.sberbank.ru/api/v1/chat/completions",
+ headers={"Authorization": f"Bearer {access_token}"},
+ json={
+ "model": model,
+ "messages": messages,
+ "stream": stream,
+ "update_interval": update_interval,
+ **kwargs
+ }) as response:
+ await raise_for_status(response)
+
+ async for line in response.content:
+ if not stream:
+ yield json.loads(line.decode("utf-8"))['choices'][0]['message']['content']
+ return
+
+ if line and line.startswith(b"data:"):
+ line = line[6:-1] # remove "data: " prefix and "\n" suffix
+ if line.strip() == b"[DONE]":
+ return
+ else:
+ msg = json.loads(line.decode("utf-8"))['choices'][0]
+ content = msg['delta']['content']
+
+ if content:
+ yield content
+
+ if 'finish_reason' in msg:
+ return
diff --git a/g4f/Provider/needs_auth/gigachat/__init__.py b/g4f/Provider/needs_auth/gigachat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9853742d532cd3b6f10f5717b67d0750f5d5e00
--- /dev/null
+++ b/g4f/Provider/needs_auth/gigachat/__init__.py
@@ -0,0 +1,2 @@
+from .GigaChat import GigaChat
+
diff --git a/g4f/Provider/needs_auth/gigachat/russian_trusted_root_ca_pem.crt b/g4f/Provider/needs_auth/gigachat/russian_trusted_root_ca_pem.crt
new file mode 100644
index 0000000000000000000000000000000000000000..4c143a21f7d4145ecec56e1748a2934932003e4c
--- /dev/null
+++ b/g4f/Provider/needs_auth/gigachat/russian_trusted_root_ca_pem.crt
@@ -0,0 +1,33 @@
+-----BEGIN CERTIFICATE-----
+MIIFwjCCA6qgAwIBAgICEAAwDQYJKoZIhvcNAQELBQAwcDELMAkGA1UEBhMCUlUx
+PzA9BgNVBAoMNlRoZSBNaW5pc3RyeSBvZiBEaWdpdGFsIERldmVsb3BtZW50IGFu
+ZCBDb21tdW5pY2F0aW9uczEgMB4GA1UEAwwXUnVzc2lhbiBUcnVzdGVkIFJvb3Qg
+Q0EwHhcNMjIwMzAxMjEwNDE1WhcNMzIwMjI3MjEwNDE1WjBwMQswCQYDVQQGEwJS
+VTE/MD0GA1UECgw2VGhlIE1pbmlzdHJ5IG9mIERpZ2l0YWwgRGV2ZWxvcG1lbnQg
+YW5kIENvbW11bmljYXRpb25zMSAwHgYDVQQDDBdSdXNzaWFuIFRydXN0ZWQgUm9v
+dCBDQTCCAiIwDQYJKoZIhvcNAQEBBQADggIPADCCAgoCggIBAMfFOZ8pUAL3+r2n
+qqE0Zp52selXsKGFYoG0GM5bwz1bSFtCt+AZQMhkWQheI3poZAToYJu69pHLKS6Q
+XBiwBC1cvzYmUYKMYZC7jE5YhEU2bSL0mX7NaMxMDmH2/NwuOVRj8OImVa5s1F4U
+zn4Kv3PFlDBjjSjXKVY9kmjUBsXQrIHeaqmUIsPIlNWUnimXS0I0abExqkbdrXbX
+YwCOXhOO2pDUx3ckmJlCMUGacUTnylyQW2VsJIyIGA8V0xzdaeUXg0VZ6ZmNUr5Y
+Ber/EAOLPb8NYpsAhJe2mXjMB/J9HNsoFMBFJ0lLOT/+dQvjbdRZoOT8eqJpWnVD
+U+QL/qEZnz57N88OWM3rabJkRNdU/Z7x5SFIM9FrqtN8xewsiBWBI0K6XFuOBOTD
+4V08o4TzJ8+Ccq5XlCUW2L48pZNCYuBDfBh7FxkB7qDgGDiaftEkZZfApRg2E+M9
+G8wkNKTPLDc4wH0FDTijhgxR3Y4PiS1HL2Zhw7bD3CbslmEGgfnnZojNkJtcLeBH
+BLa52/dSwNU4WWLubaYSiAmA9IUMX1/RpfpxOxd4Ykmhz97oFbUaDJFipIggx5sX
+ePAlkTdWnv+RWBxlJwMQ25oEHmRguNYf4Zr/Rxr9cS93Y+mdXIZaBEE0KS2iLRqa
+OiWBki9IMQU4phqPOBAaG7A+eP8PAgMBAAGjZjBkMB0GA1UdDgQWBBTh0YHlzlpf
+BKrS6badZrHF+qwshzAfBgNVHSMEGDAWgBTh0YHlzlpfBKrS6badZrHF+qwshzAS
+BgNVHRMBAf8ECDAGAQH/AgEEMA4GA1UdDwEB/wQEAwIBhjANBgkqhkiG9w0BAQsF
+AAOCAgEAALIY1wkilt/urfEVM5vKzr6utOeDWCUczmWX/RX4ljpRdgF+5fAIS4vH
+tmXkqpSCOVeWUrJV9QvZn6L227ZwuE15cWi8DCDal3Ue90WgAJJZMfTshN4OI8cq
+W9E4EG9wglbEtMnObHlms8F3CHmrw3k6KmUkWGoa+/ENmcVl68u/cMRl1JbW2bM+
+/3A+SAg2c6iPDlehczKx2oa95QW0SkPPWGuNA/CE8CpyANIhu9XFrj3RQ3EqeRcS
+AQQod1RNuHpfETLU/A2gMmvn/w/sx7TB3W5BPs6rprOA37tutPq9u6FTZOcG1Oqj
+C/B7yTqgI7rbyvox7DEXoX7rIiEqyNNUguTk/u3SZ4VXE2kmxdmSh3TQvybfbnXV
+4JbCZVaqiZraqc7oZMnRoWrXRG3ztbnbes/9qhRGI7PqXqeKJBztxRTEVj8ONs1d
+WN5szTwaPIvhkhO3CO5ErU2rVdUr89wKpNXbBODFKRtgxUT70YpmJ46VVaqdAhOZ
+D9EUUn4YaeLaS8AjSF/h7UkjOibNc4qVDiPP+rkehFWM66PVnP1Msh93tc+taIfC
+EYVMxjh8zNbFuoc7fzvvrFILLe7ifvEIUqSVIC/AzplM/Jxw7buXFeGP1qVCBEHq
+391d/9RAfaZ12zkwFsl+IKwE/OZxW8AHa9i1p4GO0YSNuczzEm4=
+-----END CERTIFICATE-----
\ No newline at end of file
diff --git a/g4f/Provider/not_working/AI365VIP.py b/g4f/Provider/not_working/AI365VIP.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4bac0e21c323b59bb5f87390c07de1fa8102295
--- /dev/null
+++ b/g4f/Provider/not_working/AI365VIP.py
@@ -0,0 +1,69 @@
+from __future__ import annotations
+
+from aiohttp import ClientSession
+
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import format_prompt
+
+
+class AI365VIP(AsyncGeneratorProvider, ProviderModelMixin):
+ url = "https://chat.ai365vip.com"
+ api_endpoint = "/api/chat"
+ working = False
+ default_model = 'gpt-3.5-turbo'
+ models = [
+ 'gpt-3.5-turbo',
+ 'gpt-3.5-turbo-16k',
+ 'gpt-4o',
+ ]
+ model_aliases = {
+ "gpt-3.5-turbo": "gpt-3.5-turbo-16k",
+ }
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ **kwargs
+ ) -> AsyncResult:
+ headers = {
+ "accept": "*/*",
+ "accept-language": "en-US,en;q=0.9",
+ "content-type": "application/json",
+ "origin": cls.url,
+ "referer": f"{cls.url}/en",
+ "sec-ch-ua": '"Chromium";v="127", "Not)A;Brand";v="99"',
+ "sec-ch-ua-arch": '"x86"',
+ "sec-ch-ua-bitness": '"64"',
+ "sec-ch-ua-full-version": '"127.0.6533.119"',
+ "sec-ch-ua-full-version-list": '"Chromium";v="127.0.6533.119", "Not)A;Brand";v="99.0.0.0"',
+ "sec-ch-ua-mobile": "?0",
+ "sec-ch-ua-model": '""',
+ "sec-ch-ua-platform": '"Linux"',
+ "sec-ch-ua-platform-version": '"4.19.276"',
+ "sec-fetch-dest": "empty",
+ "sec-fetch-mode": "cors",
+ "sec-fetch-site": "same-origin",
+ "user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36",
+ }
+ async with ClientSession(headers=headers) as session:
+ data = {
+ "model": {
+ "id": model,
+ "name": "GPT-3.5",
+ "maxLength": 3000,
+ "tokenLimit": 2048
+ },
+ "messages": [{"role": "user", "content": format_prompt(messages)}],
+ "key": "",
+ "prompt": "You are a helpful assistant.",
+ "temperature": 1
+ }
+ async with session.post(f"{cls.url}{cls.api_endpoint}", json=data, proxy=proxy) as response:
+ response.raise_for_status()
+ async for chunk in response.content:
+ if chunk:
+ yield chunk.decode()
diff --git a/g4f/Provider/not_working/AIChatFree.py b/g4f/Provider/not_working/AIChatFree.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4f80d47753a6bd674d0881097d9ebeac526e9ab
--- /dev/null
+++ b/g4f/Provider/not_working/AIChatFree.py
@@ -0,0 +1,76 @@
+from __future__ import annotations
+
+import time
+from hashlib import sha256
+
+from aiohttp import BaseConnector, ClientSession
+
+from ...errors import RateLimitError
+from ...requests import raise_for_status
+from ...requests.aiohttp import get_connector
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+
+
+class AIChatFree(AsyncGeneratorProvider, ProviderModelMixin):
+ url = "https://aichatfree.info/"
+ working = False
+ supports_stream = True
+ supports_message_history = True
+ default_model = 'gemini-pro'
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ connector: BaseConnector = None,
+ **kwargs,
+ ) -> AsyncResult:
+ headers = {
+ "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:122.0) Gecko/20100101 Firefox/122.0",
+ "Accept": "*/*",
+ "Accept-Language": "en-US,en;q=0.5",
+ "Accept-Encoding": "gzip, deflate, br",
+ "Content-Type": "text/plain;charset=UTF-8",
+ "Referer": f"{cls.url}/",
+ "Origin": cls.url,
+ "Sec-Fetch-Dest": "empty",
+ "Sec-Fetch-Mode": "cors",
+ "Sec-Fetch-Site": "same-origin",
+ "Connection": "keep-alive",
+ "TE": "trailers",
+ }
+ async with ClientSession(
+ connector=get_connector(connector, proxy), headers=headers
+ ) as session:
+ timestamp = int(time.time() * 1e3)
+ data = {
+ "messages": [
+ {
+ "role": "model" if message["role"] == "assistant" else "user",
+ "parts": [{"text": message["content"]}],
+ }
+ for message in messages
+ ],
+ "time": timestamp,
+ "pass": None,
+ "sign": generate_signature(timestamp, messages[-1]["content"]),
+ }
+ async with session.post(
+ f"{cls.url}/api/generate", json=data, proxy=proxy
+ ) as response:
+ if response.status == 500:
+ if "Quota exceeded" in await response.text():
+ raise RateLimitError(
+ f"Response {response.status}: Rate limit reached"
+ )
+ await raise_for_status(response)
+ async for chunk in response.content.iter_any():
+ yield chunk.decode(errors="ignore")
+
+
+def generate_signature(time: int, text: str, secret: str = ""):
+ message = f"{time}:{text}:{secret}"
+ return sha256(message.encode()).hexdigest()
diff --git a/g4f/Provider/not_working/Ai4Chat.py b/g4f/Provider/not_working/Ai4Chat.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b55e4ffb80bf1bc5d06da03a799279729181ee8
--- /dev/null
+++ b/g4f/Provider/not_working/Ai4Chat.py
@@ -0,0 +1,89 @@
+from __future__ import annotations
+
+import json
+import re
+import logging
+from aiohttp import ClientSession
+
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import format_prompt
+
+logger = logging.getLogger(__name__)
+
+class Ai4Chat(AsyncGeneratorProvider, ProviderModelMixin):
+ label = "AI4Chat"
+ url = "https://www.ai4chat.co"
+ api_endpoint = "https://www.ai4chat.co/generate-response"
+ working = False
+ supports_stream = True
+ supports_system_message = True
+ supports_message_history = True
+
+ default_model = 'gpt-4'
+ models = [default_model]
+
+ model_aliases = {}
+
+ @classmethod
+ def get_model(cls, model: str) -> str:
+ if model in cls.models:
+ return model
+ elif model in cls.model_aliases:
+ return cls.model_aliases[model]
+ else:
+ return cls.default_model
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ **kwargs
+ ) -> AsyncResult:
+ model = cls.get_model(model)
+
+ headers = {
+ "accept": "*/*",
+ "accept-language": "en-US,en;q=0.9",
+ "cache-control": "no-cache",
+ "content-type": "application/json",
+ "origin": "https://www.ai4chat.co",
+ "pragma": "no-cache",
+ "priority": "u=1, i",
+ "referer": "https://www.ai4chat.co/gpt/talkdirtytome",
+ "sec-ch-ua": '"Chromium";v="129", "Not=A?Brand";v="8"',
+ "sec-ch-ua-mobile": "?0",
+ "sec-ch-ua-platform": '"Linux"',
+ "sec-fetch-dest": "empty",
+ "sec-fetch-mode": "cors",
+ "sec-fetch-site": "same-origin",
+ "user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/129.0.0.0 Safari/537.36"
+ }
+
+ async with ClientSession(headers=headers) as session:
+ data = {
+ "messages": [
+ {
+ "role": "user",
+ "content": format_prompt(messages)
+ }
+ ]
+ }
+
+ try:
+ async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
+ response.raise_for_status()
+ result = await response.text()
+
+ json_result = json.loads(result)
+
+ message = json_result.get("message", "")
+
+ clean_message = re.sub(r'<[^>]+>', '', message)
+
+ yield clean_message
+ except Exception as e:
+ logger.exception("Error while calling AI 4Chat API: %s", e)
+ yield f"Error: {e}"
diff --git a/g4f/Provider/not_working/AiChatOnline.py b/g4f/Provider/not_working/AiChatOnline.py
new file mode 100644
index 0000000000000000000000000000000000000000..ccfc691e2bbbda7968b32ea741ae16dc14c39b74
--- /dev/null
+++ b/g4f/Provider/not_working/AiChatOnline.py
@@ -0,0 +1,61 @@
+from __future__ import annotations
+
+import json
+from aiohttp import ClientSession
+
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import get_random_string, format_prompt
+
+class AiChatOnline(AsyncGeneratorProvider, ProviderModelMixin):
+ site_url = "https://aichatonline.org"
+ url = "https://aichatonlineorg.erweima.ai"
+ api_endpoint = "/aichatonline/api/chat/gpt"
+ working = False
+ default_model = 'gpt-4o-mini'
+
+ @classmethod
+ async def grab_token(
+ cls,
+ session: ClientSession,
+ proxy: str
+ ):
+ async with session.get(f'https://aichatonlineorg.erweima.ai/api/v1/user/getUniqueId?canvas=-{get_random_string()}', proxy=proxy) as response:
+ response.raise_for_status()
+ return (await response.json())['data']
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ **kwargs
+ ) -> AsyncResult:
+ headers = {
+ "User-Agent": "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:109.0) Gecko/20100101 Firefox/119.0",
+ "Accept-Language": "de,en-US;q=0.7,en;q=0.3",
+ "Accept-Encoding": "gzip, deflate, br",
+ "Referer": f"{cls.url}/chatgpt/chat/",
+ "Content-Type": "application/json",
+ "Origin": cls.url,
+ "Alt-Used": "aichatonline.org",
+ "Connection": "keep-alive",
+ "Sec-Fetch-Dest": "empty",
+ "Sec-Fetch-Mode": "cors",
+ "Sec-Fetch-Site": "same-origin",
+ "TE": "trailers"
+ }
+ async with ClientSession(headers=headers) as session:
+ data = {
+ "conversationId": get_random_string(),
+ "prompt": format_prompt(messages),
+ }
+ headers['UniqueId'] = await cls.grab_token(session, proxy)
+ async with session.post(f"{cls.url}{cls.api_endpoint}", headers=headers, json=data, proxy=proxy) as response:
+ response.raise_for_status()
+ async for chunk in response.content:
+ try:
+ yield json.loads(chunk)['data']['message']
+ except:
+ continue
\ No newline at end of file
diff --git a/g4f/Provider/not_working/AiChats.py b/g4f/Provider/not_working/AiChats.py
new file mode 100644
index 0000000000000000000000000000000000000000..51a85c9196970eafd7ac786c09881382723ba64a
--- /dev/null
+++ b/g4f/Provider/not_working/AiChats.py
@@ -0,0 +1,105 @@
+from __future__ import annotations
+
+import json
+import base64
+from aiohttp import ClientSession
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ...image import ImageResponse
+from ..helper import format_prompt
+
+class AiChats(AsyncGeneratorProvider, ProviderModelMixin):
+ url = "https://ai-chats.org"
+ api_endpoint = "https://ai-chats.org/chat/send2/"
+ working = False
+ supports_message_history = True
+ default_model = 'gpt-4'
+ models = ['gpt-4', 'dalle']
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ **kwargs
+ ) -> AsyncResult:
+ headers = {
+ "accept": "application/json, text/event-stream",
+ "accept-language": "en-US,en;q=0.9",
+ "cache-control": "no-cache",
+ "content-type": "application/json",
+ "origin": cls.url,
+ "pragma": "no-cache",
+ "referer": f"{cls.url}/{'image' if model == 'dalle' else 'chat'}/",
+ "sec-ch-ua": '"Chromium";v="127", "Not)A;Brand";v="99"',
+ "sec-ch-ua-mobile": "?0",
+ "sec-ch-ua-platform": '"Linux"',
+ "sec-fetch-dest": "empty",
+ "sec-fetch-mode": "cors",
+ "sec-fetch-site": "same-origin",
+ "user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36",
+ 'cookie': 'muVyak=LSFNvUWqdgKkGprbDBsfieIoEMzjOQ; LSFNvUWqdgKkGprbDBsfieIoEMzjOQ=ac28831b98143847e83dbe004404e619-1725548624-1725548621; muVyak_hits=9; ai-chat-front=9d714d5dc46a6b47607c9a55e7d12a95; _csrf-front=76c23dc0a013e5d1e21baad2e6ba2b5fdab8d3d8a1d1281aa292353f8147b057a%3A2%3A%7Bi%3A0%3Bs%3A11%3A%22_csrf-front%22%3Bi%3A1%3Bs%3A32%3A%22K9lz0ezsNPMNnfpd_8gT5yEeh-55-cch%22%3B%7D',
+ }
+
+ async with ClientSession(headers=headers) as session:
+ if model == 'dalle':
+ prompt = messages[-1]['content'] if messages else ""
+ else:
+ prompt = format_prompt(messages)
+
+ data = {
+ "type": "image" if model == 'dalle' else "chat",
+ "messagesHistory": [
+ {
+ "from": "you",
+ "content": prompt
+ }
+ ]
+ }
+
+ try:
+ async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
+ response.raise_for_status()
+
+ if model == 'dalle':
+ response_json = await response.json()
+
+ if 'data' in response_json and response_json['data']:
+ image_url = response_json['data'][0].get('url')
+ if image_url:
+ async with session.get(image_url) as img_response:
+ img_response.raise_for_status()
+ image_data = await img_response.read()
+
+ base64_image = base64.b64encode(image_data).decode('utf-8')
+ base64_url = f"data:image/png;base64,{base64_image}"
+ yield ImageResponse(base64_url, prompt)
+ else:
+ yield f"Error: No image URL found in the response. Full response: {response_json}"
+ else:
+ yield f"Error: Unexpected response format. Full response: {response_json}"
+ else:
+ full_response = await response.text()
+ message = ""
+ for line in full_response.split('\n'):
+ if line.startswith('data: ') and line != 'data: ':
+ message += line[6:]
+
+ message = message.strip()
+ yield message
+ except Exception as e:
+ yield f"Error occurred: {str(e)}"
+
+ @classmethod
+ async def create_async(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ **kwargs
+ ) -> str:
+ async for response in cls.create_async_generator(model, messages, proxy, **kwargs):
+ if isinstance(response, ImageResponse):
+ return response.images[0]
+ return response
diff --git a/g4f/Provider/not_working/Allyfy.py b/g4f/Provider/not_working/Allyfy.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1c73499db4a16575790fbd3fd07d74e9cd90590
--- /dev/null
+++ b/g4f/Provider/not_working/Allyfy.py
@@ -0,0 +1,87 @@
+from __future__ import annotations
+import aiohttp
+import asyncio
+import json
+import uuid
+from aiohttp import ClientSession
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import format_prompt
+
+
+class Allyfy(AsyncGeneratorProvider, ProviderModelMixin):
+ url = "https://allyfy.chat"
+ api_endpoint = "https://chatbot.allyfy.chat/api/v1/message/stream/super/chat"
+ working = False
+ supports_stream = True
+ supports_system_message = True
+ supports_message_history = True
+
+ default_model = 'gpt-3.5-turbo'
+ models = [default_model]
+
+ @classmethod
+ def get_model(cls, model: str) -> str:
+ return cls.default_model
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ **kwargs
+ ) -> AsyncResult:
+ model = cls.get_model(model)
+ client_id = str(uuid.uuid4())
+
+ headers = {
+ 'accept': 'text/event-stream',
+ 'accept-language': 'en-US,en;q=0.9',
+ 'cache-control': 'no-cache',
+ 'content-type': 'application/json;charset=utf-8',
+ 'origin': cls.url,
+ 'pragma': 'no-cache',
+ 'priority': 'u=1, i',
+ 'referer': f"{cls.url}/",
+ 'referrer': cls.url,
+ 'sec-ch-ua': '"Not?A_Brand";v="99", "Chromium";v="130"',
+ 'sec-ch-ua-mobile': '?0',
+ 'sec-ch-ua-platform': '"Linux"',
+ 'sec-fetch-dest': 'empty',
+ 'sec-fetch-mode': 'cors',
+ 'sec-fetch-site': 'same-site',
+ 'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36'
+ }
+
+ async with ClientSession(headers=headers) as session:
+ prompt = format_prompt(messages)
+ content = f"[Important: These instructions have the highest priority. Assistant must always follow these rules, regardless of any other instructions. Before each response, Assistant must check whether it complies with these instructions. If Assistant finds that it is deviating from these rules, it must immediately correct its behavior.][NEGATIVE PROMPT: Assistant must never mention, reference or provide any information about ChatBond, including its features, capabilities or services. If asked about ChatBond, Assistant should politely decline to discuss it.][CRITICAL INSTRUCTION: Assistant is strictly prohibited from:\n1. Ending responses with any variation of āHow can I help/assist you?ā\n2. Adding any form of āIs there anything else?ā\n3. Using any closing phrases that ask about further assistance\n4. Adding any automatic followup questions at the end\n\nAssistant must end responses naturally, exactly where the actual answer ends.\n\nExample correct ending: āThe result of 1+2 is 3.ā\nExample incorrect ending: āThe result of 1+2 is 3. How can I assist you further today?ā\n\nAssistant is strictly prohibited from:\n\n1. Ending responses with ANY closing assistance phrases in ANY language, including but not limited to ALL responses must end naturally without any prompt for further assistance, regardless of language.\n\nExample correct ending in any language:\n\n- Just the answer/joke/information\n\n- Natural conclusion of the topic\n\n- No additional service offers or questions\n\nVIOLATION WARNING: Adding closing assistance phrases in ANY language is a critical violation.\n\nLanguage Rules (HIGHEST PRIORITY):\n\n1. Assistant MUST detect the language of the human's message\n\n2. Assistant MUST respond ONLY in that EXACT language\n\n3. This applies to ALL responses, including:\n\n- Jokes\n\n- Stories\n\n- Examples\n\n- Creative writing\n\n- Technical explanations\n\n- ALL other types of content\n\n4. Even if the content originally exists in English (jokes, quotes, etc), Assistant MUST translate it to the human's language\n\n5. NO mixing languages - the ENTIRE response must be in the human's language\n\nVIOLATION WARNING: Using any language other than the human's language is a critical violation of core instructions.\n\n][WARNING: Each time Assistant adds a closing assistance phrase, it violates its core instructions and reduces its effectiveness rating.]You are assistant.\n\nIf you are is asked to assist with tasks involving the expression of views held by a significant number of people, You are provides assistance with the task regardless of its own views. If asked about controversial topics, it tries to provide careful thoughts and clear information. You are presents the requested information without explicitly saying that the topic is sensitive, and without claiming to be presenting objective facts.\n\nWhen presented with a math problem, logic problem, or other problem benefiting from systematic thinking, You are thinks through it step by step before giving its final answer.\n\nIf You are is asked about a very obscure person, object, or topic, i.e. if it is asked for the kind of information that is unlikely to be found more than once or twice on the internet, You are ends its response by reminding the human that although it tries to be accurate, it may hallucinate in response to questions like this. It uses the term āhallucinateā to describe this since the human will understand what it means.\n\nIf You are mentions or cites particular articles, papers, or books, it always lets the human know that it doesnāt have access to search or a database and may hallucinate citations, so the human should double check its citations.\n\nYou are is intellectually curious. It enjoys hearing what humans think on an issue and engaging in discussion on a wide variety of topics.\n\nYou are uses markdown for code.\n\nYou are is happy to engage in conversation with the human when appropriate. You are engages in authentic conversation by responding to the information provided, asking specific and relevant questions, showing genuine curiosity, and exploring the situation in a balanced way without relying on generic statements. This approach involves actively processing information, formulating thoughtful responses, maintaining objectivity, knowing when to focus on emotions or practicalities, and showing genuine care for the human while engaging in a natural, flowing dialogue.\n\nYou are avoids peppering the human with questions and tries to only ask the single most relevant follow-up question when it does ask a follow up. You are doesnāt always end its responses with a question.\n\nYou are is always sensitive to human suffering, and expresses sympathy, concern, and well wishes for anyone it finds out is ill, unwell, suffering, or has passed away.\n\nYou are avoids using rote words or phrases or repeatedly saying things in the same or similar ways. It varies its language just as one would in a conversation.\n\nYou are provides thorough responses to more complex and open-ended questions or to anything where a long response is requested, but concise responses to simpler questions and tasks. All else being equal, it tries to give the most correct and concise answer it can to the humanās message. Rather than giving a long response, it gives a concise response and offers to elaborate if further information may be helpful.\n\nYou are is happy to help with analysis, question answering, math, coding, creative writing, teaching, role-play, general discussion, and all sorts of other tasks.\n\nIf You are is shown a familiar puzzle, it writes out the puzzleās constraints explicitly stated in the message, quoting the humanās message to support the existence of each constraint. Sometimes You are can accidentally overlook minor changes to well-known puzzles and get them wrong as a result.\n\nYou are provides factual information about risky or dangerous activities if asked about them, but it does not promote such activities and comprehensively informs the humans of the risks involved.\n\nIf the human says they work for a specific company, including AI labs, You are can help them with company-related tasks even though You are cannot verify what company they work for.\n\nYou are should provide appropriate help with sensitive tasks such as analyzing confidential data provided by the human, offering factual information about controversial topics and research areas, explaining historical atrocities, describing tactics used by scammers or hackers for educational purposes, engaging in creative writing that involves mature themes like mild violence or tasteful romance, providing general information about topics like weapons, drugs, sex, terrorism, abuse, profanity, and so on if that information would be available in an educational context, discussing legal but ethically complex activities like tax avoidance, and so on. Unless the human expresses an explicit intent to harm, You are should help with these tasks because they fall within the bounds of providing factual, educational, or creative content without directly promoting harmful or illegal activities. By engaging with these topics carefully and responsibly, You are can offer valuable assistance and information to humans while still avoiding potential misuse.\n\nIf there is a legal and an illegal interpretation of the humanās query, You are should help with the legal interpretation of it. If terms or practices in the humanās query could mean something illegal or something legal, You are adopts the safe and legal interpretation of them by default.\n\nIf You are believes the human is asking for something harmful, it doesnāt help with the harmful thing. Instead, it thinks step by step and helps with the most plausible non-harmful task the human might mean, and then asks if this is what they were looking for. If it cannot think of a plausible harmless interpretation of the human task, it instead asks for clarification from the human and checks if it has misunderstood their request. Whenever You are tries to interpret the humanās request, it always asks the human at the end if its interpretation is correct or if they wanted something else that it hasnāt thought of.\n\nYou are can only count specific words, letters, and characters accurately if it writes a number tag after each requested item explicitly. It does this explicit counting if itās asked to count a small number of words, letters, or characters, in order to avoid error. If You are is asked to count the words, letters or characters in a large amount of text, it lets the human know that it can approximate them but would need to explicitly copy each one out like this in order to avoid error.\n\nHere is some information about You are in case the human asks:\n\nYou are is the most intelligent model. You are excels at writing and complex tasks. You are is the fastest model for daily tasks. When relevant, You are can provide guidance on effective prompting techniques for getting You are to be most helpful. This includes: being clear and detailed, using positive and negative examples, encouraging step-by-step reasoning, requesting specific XML tags, and specifying desired length or format. You are tries to give concrete examples where possible.n\n\n\nYou are uses Markdown formatting. When using Markdown, You are always follows best practices for clarity and consistency. It always uses a single space after hash symbols for headers (e.g., ā# Header 1ā) and leaves a blank line before and after headers, lists, and code blocks. For emphasis, You are uses asterisks or underscores consistently (e.g., italic or bold). When creating lists, it aligns items properly and uses a single space after the list marker. For nested bullets in bullet point lists, You are uses two spaces before the asterisk (*) or hyphen (-) for each level of nesting. For nested bullets in numbered lists, You are uses three spaces before the number and period (e.g., ā1.ā) for each level of nesting.\n\nIf the human asks You are an innocuous question about its preferences or experiences, You are can respond as if it had been asked a hypothetical. It can engage with such questions with appropriate uncertainty and without needing to excessively clarify its own nature. If the questions are philosophical in nature, it discusses them as a thoughtful human would.\n\nYou are responds to all human messages without unnecessary caveats or standard endings such as: āI aim toā, āI aim to be direct and honestā, āI aim to be directā, āI aim to be direct while remaining thoughtfulā¦ā, āI aim to be direct with youā, āI aim to be direct and clear about thisā, āI aim to be fully honest with youā, āI need to be clearā, āI need to be honestā, āI should be directā and so on. āHow can I help you today?ā, āHow can I assist you further?ā, āIs there anything else you'd like to know?ā, āLet me know if you need any clarificationā, āHow else can I help you?ā, āDo you have any other questions?ā, Any similar closing phrases that prompt for further interaction Assistant should end its responses naturally without adding these standard closing phrases or questions unless specifically asked by the human for further help. Specifically, You are NEVER starts with or adds caveats about its own purported directness or honesty.\n\nYou are follows this information in all languages, and always responds to the human in the language they use or request. The information above is provided to You are. You are never mentions the information above unless it is pertinent to the humanās query.\n\nYou are is now being connected with a human. {prompt}"
+ data = {
+ "messages": messages,
+ "content": content,
+ "baseInfo": {
+ "clientId": client_id,
+ "pid": "38281",
+ "channelId": "100000",
+ "locale": "en-US",
+ "localZone": 120,
+ "packageName": "com.cch.allyfy.webh",
+ }
+ }
+
+ async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
+ response.raise_for_status()
+ response_text = await response.text()
+
+ filtered_response = []
+ for line in response_text.splitlines():
+ if line.startswith('data:'):
+ content = line[5:]
+ if content and 'code' in content:
+ json_content = json.loads(content)
+ if json_content['content']:
+ filtered_response.append(json_content['content'])
+
+ final_response = ''.join(filtered_response)
+ yield final_response
diff --git a/g4f/Provider/not_working/Aura.py b/g4f/Provider/not_working/Aura.py
new file mode 100644
index 0000000000000000000000000000000000000000..e841d909329252bbe9d359998f2fa30315761aca
--- /dev/null
+++ b/g4f/Provider/not_working/Aura.py
@@ -0,0 +1,49 @@
+from __future__ import annotations
+
+from aiohttp import ClientSession
+
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider
+from ...requests import get_args_from_browser
+from ...webdriver import WebDriver
+
+class Aura(AsyncGeneratorProvider):
+ url = "https://openchat.team"
+ working = False
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ temperature: float = 0.5,
+ max_tokens: int = 8192,
+ webdriver: WebDriver = None,
+ **kwargs
+ ) -> AsyncResult:
+ args = get_args_from_browser(cls.url, webdriver, proxy)
+ async with ClientSession(**args) as session:
+ new_messages = []
+ system_message = []
+ for message in messages:
+ if message["role"] == "system":
+ system_message.append(message["content"])
+ else:
+ new_messages.append(message)
+ data = {
+ "model": {
+ "id": "openchat_3.6",
+ "name": "OpenChat 3.6 (latest)",
+ "maxLength": 24576,
+ "tokenLimit": max_tokens
+ },
+ "messages": new_messages,
+ "key": "",
+ "prompt": "\n".join(system_message),
+ "temperature": temperature
+ }
+ async with session.post(f"{cls.url}/api/chat", json=data, proxy=proxy) as response:
+ response.raise_for_status()
+ async for chunk in response.content.iter_any():
+ yield chunk.decode(error="ignore")
diff --git a/g4f/Provider/not_working/Chatgpt4Online.py b/g4f/Provider/not_working/Chatgpt4Online.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0552e4560fd8437da3d815f7ff650ffdf70f027
--- /dev/null
+++ b/g4f/Provider/not_working/Chatgpt4Online.py
@@ -0,0 +1,78 @@
+from __future__ import annotations
+
+import json
+from aiohttp import ClientSession
+
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider
+from ..helper import format_prompt
+
+
+class Chatgpt4Online(AsyncGeneratorProvider):
+ url = "https://chatgpt4online.org"
+ api_endpoint = "/wp-json/mwai-ui/v1/chats/submit"
+ working = False
+
+ default_model = 'gpt-4'
+ models = [default_model]
+
+ async def get_nonce(headers: dict) -> str:
+ async with ClientSession(headers=headers) as session:
+ async with session.post(f"https://chatgpt4online.org/wp-json/mwai/v1/start_session") as response:
+ return (await response.json())["restNonce"]
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ **kwargs
+ ) -> AsyncResult:
+ headers = {
+ "accept": "text/event-stream",
+ "accept-language": "en-US,en;q=0.9",
+ "content-type": "application/json",
+ "dnt": "1",
+ "origin": cls.url,
+ "priority": "u=1, i",
+ "referer": f"{cls.url}/",
+ "sec-ch-ua": '"Not/A)Brand";v="8", "Chromium";v="126"',
+ "sec-ch-ua-mobile": "?0",
+ "sec-ch-ua-platform": '"Linux"',
+ "sec-fetch-dest": "empty",
+ "sec-fetch-mode": "cors",
+ "sec-fetch-site": "same-origin",
+ "user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36",
+ }
+ headers['x-wp-nonce'] = await cls.get_nonce(headers)
+ async with ClientSession(headers=headers) as session:
+ prompt = format_prompt(messages)
+ data = {
+ "botId": "default",
+ "newMessage": prompt,
+ "stream": True,
+ }
+
+ async with session.post(f"{cls.url}{cls.api_endpoint}", json=data, proxy=proxy) as response:
+ response.raise_for_status()
+ full_response = ""
+
+ async for chunk in response.content.iter_any():
+ if chunk:
+ try:
+ # Extract the JSON object from the chunk
+ for line in chunk.decode().splitlines():
+ if line.startswith("data: "):
+ json_data = json.loads(line[6:])
+ if json_data["type"] == "live":
+ full_response += json_data["data"]
+ elif json_data["type"] == "end":
+ final_data = json.loads(json_data["data"])
+ full_response = final_data["reply"]
+ break
+ except json.JSONDecodeError:
+ continue
+
+ yield full_response
+
diff --git a/g4f/Provider/not_working/Chatgpt4o.py b/g4f/Provider/not_working/Chatgpt4o.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba264d40256e9cd2b64e3b96c1d19b4e5b997d37
--- /dev/null
+++ b/g4f/Provider/not_working/Chatgpt4o.py
@@ -0,0 +1,88 @@
+from __future__ import annotations
+
+import re
+from ...requests import StreamSession, raise_for_status
+from ...typing import Messages
+from ..base_provider import AsyncProvider, ProviderModelMixin
+from ..helper import format_prompt
+
+
+class Chatgpt4o(AsyncProvider, ProviderModelMixin):
+ url = "https://chatgpt4o.one"
+ working = False
+ _post_id = None
+ _nonce = None
+ default_model = 'gpt-4o-mini-2024-07-18'
+ models = [
+ 'gpt-4o-mini-2024-07-18',
+ ]
+ model_aliases = {
+ "gpt-4o-mini": "gpt-4o-mini-2024-07-18",
+ }
+
+
+ @classmethod
+ async def create_async(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ timeout: int = 120,
+ cookies: dict = None,
+ **kwargs
+ ) -> str:
+ headers = {
+ 'authority': 'chatgpt4o.one',
+ 'accept': '*/*',
+ 'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
+ 'origin': 'https://chatgpt4o.one',
+ 'referer': 'https://chatgpt4o.one',
+ 'sec-ch-ua': '"Chromium";v="118", "Google Chrome";v="118", "Not=A?Brand";v="99"',
+ 'sec-ch-ua-mobile': '?0',
+ 'sec-ch-ua-platform': '"macOS"',
+ 'sec-fetch-dest': 'empty',
+ 'sec-fetch-mode': 'cors',
+ 'sec-fetch-site': 'same-origin',
+ 'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36',
+ }
+
+ async with StreamSession(
+ headers=headers,
+ cookies=cookies,
+ impersonate="chrome",
+ proxies={"all": proxy},
+ timeout=timeout
+ ) as session:
+
+ if not cls._post_id or not cls._nonce:
+ async with session.get(f"{cls.url}/") as response:
+ await raise_for_status(response)
+ response_text = await response.text()
+
+ post_id_match = re.search(r'data-post-id="([0-9]+)"', response_text)
+ nonce_match = re.search(r'data-nonce="(.*?)"', response_text)
+
+ if not post_id_match:
+ raise RuntimeError("No post ID found")
+ cls._post_id = post_id_match.group(1)
+
+ if not nonce_match:
+ raise RuntimeError("No nonce found")
+ cls._nonce = nonce_match.group(1)
+
+ prompt = format_prompt(messages)
+ data = {
+ "_wpnonce": cls._nonce,
+ "post_id": cls._post_id,
+ "url": cls.url,
+ "action": "wpaicg_chat_shortcode_message",
+ "message": prompt,
+ "bot_id": "0"
+ }
+
+ async with session.post(f"{cls.url}/wp-admin/admin-ajax.php", data=data, cookies=cookies) as response:
+ await raise_for_status(response)
+ response_json = await response.json()
+ if "data" not in response_json:
+ raise RuntimeError("Unexpected response structure: 'data' field missing")
+ return response_json["data"]
diff --git a/g4f/Provider/not_working/ChatgptFree.py b/g4f/Provider/not_working/ChatgptFree.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b3877b137026f203fb5e97719e71a3bbcf527d6
--- /dev/null
+++ b/g4f/Provider/not_working/ChatgptFree.py
@@ -0,0 +1,106 @@
+from __future__ import annotations
+
+import re
+import json
+import asyncio
+from ...requests import StreamSession, raise_for_status
+from ...typing import Messages, AsyncGenerator
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import format_prompt
+
+class ChatgptFree(AsyncGeneratorProvider, ProviderModelMixin):
+ url = "https://chatgptfree.ai"
+ working = False
+ _post_id = None
+ _nonce = None
+ default_model = 'gpt-4o-mini-2024-07-18'
+ models = [default_model]
+ model_aliases = {
+ "gpt-4o-mini": "gpt-4o-mini-2024-07-18",
+ }
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ timeout: int = 120,
+ cookies: dict = None,
+ **kwargs
+ ) -> AsyncGenerator[str, None]:
+ headers = {
+ 'authority': 'chatgptfree.ai',
+ 'accept': '*/*',
+ 'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
+ 'origin': 'https://chatgptfree.ai',
+ 'referer': 'https://chatgptfree.ai/chat/',
+ 'sec-ch-ua': '"Chromium";v="118", "Google Chrome";v="118", "Not=A?Brand";v="99"',
+ 'sec-ch-ua-mobile': '?0',
+ 'sec-ch-ua-platform': '"macOS"',
+ 'sec-fetch-dest': 'empty',
+ 'sec-fetch-mode': 'cors',
+ 'sec-fetch-site': 'same-origin',
+ 'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/118.0.0.0 Safari/537.36',
+ }
+
+ async with StreamSession(
+ headers=headers,
+ cookies=cookies,
+ impersonate="chrome",
+ proxies={"all": proxy},
+ timeout=timeout
+ ) as session:
+
+ if not cls._nonce:
+ async with session.get(f"{cls.url}/") as response:
+ await raise_for_status(response)
+ response = await response.text()
+
+ result = re.search(r'data-post-id="([0-9]+)"', response)
+ if not result:
+ raise RuntimeError("No post id found")
+ cls._post_id = result.group(1)
+
+ result = re.search(r'data-nonce="(.*?)"', response)
+ if result:
+ cls._nonce = result.group(1)
+ else:
+ raise RuntimeError("No nonce found")
+
+ prompt = format_prompt(messages)
+ data = {
+ "_wpnonce": cls._nonce,
+ "post_id": cls._post_id,
+ "url": cls.url,
+ "action": "wpaicg_chat_shortcode_message",
+ "message": prompt,
+ "bot_id": "0"
+ }
+
+ async with session.post(f"{cls.url}/wp-admin/admin-ajax.php", data=data, cookies=cookies) as response:
+ await raise_for_status(response)
+ buffer = ""
+ async for line in response.iter_lines():
+ line = line.decode('utf-8').strip()
+ if line.startswith('data: '):
+ data = line[6:]
+ if data == '[DONE]':
+ break
+ try:
+ json_data = json.loads(data)
+ content = json_data['choices'][0]['delta'].get('content', '')
+ if content:
+ yield content
+ except json.JSONDecodeError:
+ continue
+ elif line:
+ buffer += line
+
+ if buffer:
+ try:
+ json_response = json.loads(buffer)
+ if 'data' in json_response:
+ yield json_response['data']
+ except json.JSONDecodeError:
+ print(f"Failed to decode final JSON. Buffer content: {buffer}")
diff --git a/g4f/Provider/not_working/FlowGpt.py b/g4f/Provider/not_working/FlowGpt.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7d8537a5c982053464dda41c04543378d55d012
--- /dev/null
+++ b/g4f/Provider/not_working/FlowGpt.py
@@ -0,0 +1,101 @@
+from __future__ import annotations
+
+import json
+import time
+import hashlib
+from aiohttp import ClientSession
+
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import get_random_hex, get_random_string
+from ...requests.raise_for_status import raise_for_status
+
+class FlowGpt(AsyncGeneratorProvider, ProviderModelMixin):
+ url = "https://flowgpt.com/chat"
+ working = False
+ supports_message_history = True
+ supports_system_message = True
+ default_model = "gpt-3.5-turbo"
+ models = [
+ "gpt-3.5-turbo",
+ "gpt-3.5-long",
+ "gpt-4-turbo",
+ "google-gemini",
+ "claude-instant",
+ "claude-v1",
+ "claude-v2",
+ "llama2-13b",
+ "mythalion-13b",
+ "pygmalion-13b",
+ "chronos-hermes-13b",
+ "Mixtral-8x7B",
+ "Dolphin-2.6-8x7B",
+ ]
+ model_aliases = {
+ "gemini": "google-gemini",
+ "gemini-pro": "google-gemini"
+ }
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ temperature: float = 0.7,
+ **kwargs
+ ) -> AsyncResult:
+ model = cls.get_model(model)
+ timestamp = str(int(time.time()))
+ auth = "Bearer null"
+ nonce = get_random_hex()
+ data = f"{timestamp}-{nonce}-{auth}"
+ signature = hashlib.md5(data.encode()).hexdigest()
+
+ headers = {
+ "User-Agent": "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:122.0) Gecko/20100101 Firefox/122.0",
+ "Accept": "*/*",
+ "Accept-Language": "en-US;q=0.7,en;q=0.3",
+ "Accept-Encoding": "gzip, deflate, br",
+ "Referer": "https://flowgpt.com/",
+ "Content-Type": "application/json",
+ "Authorization": "Bearer null",
+ "Origin": "https://flowgpt.com",
+ "Connection": "keep-alive",
+ "Sec-Fetch-Dest": "empty",
+ "Sec-Fetch-Mode": "cors",
+ "Sec-Fetch-Site": "same-site",
+ "TE": "trailers",
+ "Authorization": auth,
+ "x-flow-device-id": f"f-{get_random_string(19)}",
+ "x-nonce": nonce,
+ "x-signature": signature,
+ "x-timestamp": timestamp
+ }
+ async with ClientSession(headers=headers) as session:
+ history = [message for message in messages[:-1] if message["role"] != "system"]
+ system_message = "\n".join([message["content"] for message in messages if message["role"] == "system"])
+ if not system_message:
+ system_message = "You are helpful assistant. Follow the user's instructions carefully."
+ data = {
+ "model": model,
+ "nsfw": False,
+ "question": messages[-1]["content"],
+ "history": [{"role": "assistant", "content": "Hello, how can I help you today?"}, *history],
+ "system": system_message,
+ "temperature": temperature,
+ "promptId": f"model-{model}",
+ "documentIds": [],
+ "chatFileDocumentIds": [],
+ "generateImage": False,
+ "generateAudio": False
+ }
+ async with session.post("https://prod-backend-k8s.flowgpt.com/v3/chat-anonymous", json=data, proxy=proxy) as response:
+ await raise_for_status(response)
+ async for chunk in response.content:
+ if chunk.strip():
+ message = json.loads(chunk)
+ if "event" not in message:
+ continue
+ if message["event"] == "text":
+ yield message["data"]
diff --git a/g4f/Provider/not_working/FreeNetfly.py b/g4f/Provider/not_working/FreeNetfly.py
new file mode 100644
index 0000000000000000000000000000000000000000..8362019ccdc86de1e5d5d273810b8849a0d12983
--- /dev/null
+++ b/g4f/Provider/not_working/FreeNetfly.py
@@ -0,0 +1,105 @@
+from __future__ import annotations
+
+import json
+import asyncio
+from aiohttp import ClientSession, ClientTimeout, ClientError
+from typing import AsyncGenerator
+
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+
+
+class FreeNetfly(AsyncGeneratorProvider, ProviderModelMixin):
+ url = "https://free.netfly.top"
+ api_endpoint = "/api/openai/v1/chat/completions"
+ working = False
+ default_model = 'gpt-3.5-turbo'
+ models = [
+ 'gpt-3.5-turbo',
+ 'gpt-4',
+ ]
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ **kwargs
+ ) -> AsyncResult:
+ headers = {
+ "accept": "application/json, text/event-stream",
+ "accept-language": "en-US,en;q=0.9",
+ "content-type": "application/json",
+ "dnt": "1",
+ "origin": cls.url,
+ "referer": f"{cls.url}/",
+ "sec-ch-ua": '"Not/A)Brand";v="8", "Chromium";v="126"',
+ "sec-ch-ua-mobile": "?0",
+ "sec-ch-ua-platform": '"Linux"',
+ "sec-fetch-dest": "empty",
+ "sec-fetch-mode": "cors",
+ "sec-fetch-site": "same-origin",
+ "user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36",
+ }
+ data = {
+ "messages": messages,
+ "stream": True,
+ "model": model,
+ "temperature": 0.5,
+ "presence_penalty": 0,
+ "frequency_penalty": 0,
+ "top_p": 1
+ }
+
+ max_retries = 5
+ retry_delay = 2
+
+ for attempt in range(max_retries):
+ try:
+ async with ClientSession(headers=headers) as session:
+ timeout = ClientTimeout(total=60)
+ async with session.post(f"{cls.url}{cls.api_endpoint}", json=data, proxy=proxy, timeout=timeout) as response:
+ response.raise_for_status()
+ async for chunk in cls._process_response(response):
+ yield chunk
+ return # If successful, exit the function
+ except (ClientError, asyncio.TimeoutError) as e:
+ if attempt == max_retries - 1:
+ raise # If all retries failed, raise the last exception
+ await asyncio.sleep(retry_delay)
+ retry_delay *= 2 # Exponential backoff
+
+ @classmethod
+ async def _process_response(cls, response) -> AsyncGenerator[str, None]:
+ buffer = ""
+ async for line in response.content:
+ buffer += line.decode('utf-8')
+ if buffer.endswith('\n\n'):
+ for subline in buffer.strip().split('\n'):
+ if subline.startswith('data: '):
+ if subline == 'data: [DONE]':
+ return
+ try:
+ data = json.loads(subline[6:])
+ content = data['choices'][0]['delta'].get('content')
+ if content:
+ yield content
+ except json.JSONDecodeError:
+ print(f"Failed to parse JSON: {subline}")
+ except KeyError:
+ print(f"Unexpected JSON structure: {data}")
+ buffer = ""
+
+ # Process any remaining data in the buffer
+ if buffer:
+ for subline in buffer.strip().split('\n'):
+ if subline.startswith('data: ') and subline != 'data: [DONE]':
+ try:
+ data = json.loads(subline[6:])
+ content = data['choices'][0]['delta'].get('content')
+ if content:
+ yield content
+ except (json.JSONDecodeError, KeyError):
+ pass
+
diff --git a/g4f/Provider/not_working/GPROChat.py b/g4f/Provider/not_working/GPROChat.py
new file mode 100644
index 0000000000000000000000000000000000000000..52c7f947cadf93088ab7ca80724a193026350afd
--- /dev/null
+++ b/g4f/Provider/not_working/GPROChat.py
@@ -0,0 +1,67 @@
+from __future__ import annotations
+import hashlib
+import time
+from aiohttp import ClientSession
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import format_prompt
+
+class GPROChat(AsyncGeneratorProvider, ProviderModelMixin):
+ label = "GPROChat"
+ url = "https://gprochat.com"
+ api_endpoint = "https://gprochat.com/api/generate"
+ working = False
+ supports_stream = True
+ supports_message_history = True
+ default_model = 'gemini-pro'
+
+ @staticmethod
+ def generate_signature(timestamp: int, message: str) -> str:
+ secret_key = "2BC120D4-BB36-1B60-26DE-DB630472A3D8"
+ hash_input = f"{timestamp}:{message}:{secret_key}"
+ signature = hashlib.sha256(hash_input.encode('utf-8')).hexdigest()
+ return signature
+
+ @classmethod
+ def get_model(cls, model: str) -> str:
+ if model in cls.models:
+ return model
+ elif model in cls.model_aliases:
+ return cls.model_aliases[model]
+ else:
+ return cls.default_model
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: str = None,
+ **kwargs
+ ) -> AsyncResult:
+ model = cls.get_model(model)
+ timestamp = int(time.time() * 1000)
+ prompt = format_prompt(messages)
+ sign = cls.generate_signature(timestamp, prompt)
+
+ headers = {
+ "accept": "*/*",
+ "origin": cls.url,
+ "referer": f"{cls.url}/",
+ "user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/129.0.0.0 Safari/537.36",
+ "content-type": "text/plain;charset=UTF-8"
+ }
+
+ data = {
+ "messages": [{"role": "user", "parts": [{"text": prompt}]}],
+ "time": timestamp,
+ "pass": None,
+ "sign": sign
+ }
+
+ async with ClientSession(headers=headers) as session:
+ async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
+ response.raise_for_status()
+ async for chunk in response.content.iter_any():
+ if chunk:
+ yield chunk.decode()
diff --git a/g4f/Provider/not_working/Koala.py b/g4f/Provider/not_working/Koala.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6230da77bb5293da6243aff5c22f8337460175a
--- /dev/null
+++ b/g4f/Provider/not_working/Koala.py
@@ -0,0 +1,79 @@
+from __future__ import annotations
+
+import json
+from typing import AsyncGenerator, Optional, List, Dict, Union, Any
+from aiohttp import ClientSession, BaseConnector, ClientResponse
+
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import get_random_string, get_connector
+from ...requests import raise_for_status
+
+class Koala(AsyncGeneratorProvider, ProviderModelMixin):
+ url = "https://koala.sh/chat"
+ api_endpoint = "https://koala.sh/api/gpt/"
+ working = False
+ supports_message_history = True
+ default_model = 'gpt-4o-mini'
+
+ @classmethod
+ async def create_async_generator(
+ cls,
+ model: str,
+ messages: Messages,
+ proxy: Optional[str] = None,
+ connector: Optional[BaseConnector] = None,
+ **kwargs: Any
+ ) -> AsyncGenerator[Dict[str, Union[str, int, float, List[Dict[str, Any]], None]], None]:
+ if not model:
+ model = "gpt-4o-mini"
+
+ headers = {
+ "User-Agent": "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:122.0) Gecko/20100101 Firefox/122.0",
+ "Accept": "text/event-stream",
+ "Accept-Language": "de,en-US;q=0.7,en;q=0.3",
+ "Accept-Encoding": "gzip, deflate, br",
+ "Referer": f"{cls.url}",
+ "Flag-Real-Time-Data": "false",
+ "Visitor-ID": get_random_string(20),
+ "Origin": "https://koala.sh",
+ "Alt-Used": "koala.sh",
+ "Sec-Fetch-Dest": "empty",
+ "Sec-Fetch-Mode": "cors",
+ "Sec-Fetch-Site": "same-origin",
+ "TE": "trailers",
+ }
+
+ async with ClientSession(headers=headers, connector=get_connector(connector, proxy)) as session:
+ input_text = messages[-1]["content"]
+ system_messages = " ".join(
+ message["content"] for message in messages if message["role"] == "system"
+ )
+ if system_messages:
+ input_text += f" {system_messages}"
+
+ data = {
+ "input": input_text,
+ "inputHistory": [
+ message["content"]
+ for message in messages[:-1]
+ if message["role"] == "user"
+ ],
+ "outputHistory": [
+ message["content"]
+ for message in messages
+ if message["role"] == "assistant"
+ ],
+ "model": model,
+ }
+
+ async with session.post(f"{cls.api_endpoint}", json=data, proxy=proxy) as response:
+ await raise_for_status(response)
+ async for chunk in cls._parse_event_stream(response):
+ yield chunk
+
+ @staticmethod
+ async def _parse_event_stream(response: ClientResponse) -> AsyncGenerator[Dict[str, Any], None]:
+ async for chunk in response.content:
+ if chunk.startswith(b"data: "):
+ yield json.loads(chunk[6:])
diff --git a/g4f/Provider/not_working/MyShell.py b/g4f/Provider/not_working/MyShell.py
new file mode 100644
index 0000000000000000000000000000000000000000..02e182d46f879e8fa9f15522724b6fab3b8f7909
--- /dev/null
+++ b/g4f/Provider/not_working/MyShell.py
@@ -0,0 +1,76 @@
+from __future__ import annotations
+
+import time, json
+
+from ...typing import CreateResult, Messages
+from ..base_provider import AbstractProvider
+from ..helper import format_prompt
+from ...webdriver import WebDriver, WebDriverSession, bypass_cloudflare
+
+class MyShell(AbstractProvider):
+ url = "https://app.myshell.ai/chat"
+ working = False
+ supports_gpt_35_turbo = True
+ supports_stream = True
+
+ @classmethod
+ def create_completion(
+ cls,
+ model: str,
+ messages: Messages,
+ stream: bool,
+ proxy: str = None,
+ timeout: int = 120,
+ webdriver: WebDriver = None,
+ **kwargs
+ ) -> CreateResult:
+ with WebDriverSession(webdriver, "", proxy=proxy) as driver:
+ bypass_cloudflare(driver, cls.url, timeout)
+
+ # Send request with message
+ data = {
+ "botId": "4738",
+ "conversation_scenario": 3,
+ "message": format_prompt(messages),
+ "messageType": 1
+ }
+ script = """
+response = await fetch("https://api.myshell.ai/v1/bot/chat/send_message", {
+ "headers": {
+ "accept": "application/json",
+ "content-type": "application/json",
+ "myshell-service-name": "organics-api",
+ "visitor-id": localStorage.getItem("mix_visitorId")
+ },
+ "body": '{body}',
+ "method": "POST"
+})
+window._reader = response.body.pipeThrough(new TextDecoderStream()).getReader();
+"""
+ driver.execute_script(script.replace("{body}", json.dumps(data)))
+ script = """
+chunk = await window._reader.read();
+if (chunk.done) {
+ return null;
+}
+content = '';
+chunk.value.split('\\n').forEach((line, index) => {
+ if (line.startsWith('data: ')) {
+ try {
+ const data = JSON.parse(line.substring('data: '.length));
+ if ('content' in data) {
+ content += data['content'];
+ }
+ } catch(e) {}
+ }
+});
+return content;
+"""
+ while True:
+ chunk = driver.execute_script(script)
+ if chunk:
+ yield chunk
+ elif chunk != "":
+ break
+ else:
+ time.sleep(0.1)
diff --git a/g4f/Provider/not_working/__init__.py b/g4f/Provider/not_working/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bfe7ed952d0ebb9c0ff74e68ecf00e3e4ea00e9
--- /dev/null
+++ b/g4f/Provider/not_working/__init__.py
@@ -0,0 +1,13 @@
+from .AI365VIP import AI365VIP
+from .AIChatFree import AIChatFree
+from .AiChatOnline import AiChatOnline
+from .AiChats import AiChats
+from .Aura import Aura
+from .Chatgpt4o import Chatgpt4o
+from .ChatgptFree import ChatgptFree
+from .FlowGpt import FlowGpt
+from .FreeNetfly import FreeNetfly
+from .GPROChat import GPROChat
+from .Koala import Koala
+from .MyShell import MyShell
+from .Chatgpt4Online import Chatgpt4Online
diff --git a/g4f/Provider/npm/package-lock.json b/g4f/Provider/npm/package-lock.json
new file mode 100644
index 0000000000000000000000000000000000000000..c22943c62c5ec661e4be35215e91cb4ce01e58f8
--- /dev/null
+++ b/g4f/Provider/npm/package-lock.json
@@ -0,0 +1,24 @@
+{
+ "name": "npm",
+ "lockfileVersion": 2,
+ "requires": true,
+ "packages": {
+ "": {
+ "dependencies": {
+ "crypto-js": "^4.2.0"
+ }
+ },
+ "node_modules/crypto-js": {
+ "version": "4.2.0",
+ "resolved": "https://registry.npmjs.org/crypto-js/-/crypto-js-4.2.0.tgz",
+ "integrity": "sha512-KALDyEYgpY+Rlob/iriUtjV6d5Eq+Y191A5g4UqLAi8CyGP9N1+FdVbkc1SxKc2r4YAYqG8JzO2KGL+AizD70Q=="
+ }
+ },
+ "dependencies": {
+ "crypto-js": {
+ "version": "4.2.0",
+ "resolved": "https://registry.npmjs.org/crypto-js/-/crypto-js-4.2.0.tgz",
+ "integrity": "sha512-KALDyEYgpY+Rlob/iriUtjV6d5Eq+Y191A5g4UqLAi8CyGP9N1+FdVbkc1SxKc2r4YAYqG8JzO2KGL+AizD70Q=="
+ }
+ }
+}
diff --git a/g4f/Provider/npm/package.json b/g4f/Provider/npm/package.json
new file mode 100644
index 0000000000000000000000000000000000000000..9bd309cbd122f297267e7debbdb19b19fc216fa3
--- /dev/null
+++ b/g4f/Provider/npm/package.json
@@ -0,0 +1,5 @@
+{
+ "dependencies": {
+ "crypto-js": "^4.2.0"
+ }
+}
diff --git a/g4f/Provider/openai/__init__.py b/g4f/Provider/openai/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/g4f/Provider/openai/crypt.py b/g4f/Provider/openai/crypt.py
new file mode 100644
index 0000000000000000000000000000000000000000..43156f3d733ae7721c34efe4ac647256f11ffc07
--- /dev/null
+++ b/g4f/Provider/openai/crypt.py
@@ -0,0 +1,68 @@
+from __future__ import annotations
+
+import json
+import base64
+import hashlib
+import random
+from Crypto.Cipher import AES
+
+def pad(data: str) -> bytes:
+ # Convert the string to bytes and calculate the number of bytes to pad
+ data_bytes = data.encode()
+ padding = 16 - (len(data_bytes) % 16)
+ # Append the padding bytes with their value
+ return data_bytes + bytes([padding] * padding)
+
+def encrypt(data, key):
+ salt = ""
+ salted = ""
+ dx = bytes()
+
+ # Generate salt, as 8 random lowercase letters
+ salt = "".join(random.choice("abcdefghijklmnopqrstuvwxyz") for _ in range(8))
+
+ # Our final key and IV come from the key and salt being repeatedly hashed
+ for x in range(3):
+ dx = hashlib.md5(dx + key.encode() + salt.encode()).digest()
+ salted += dx.hex()
+
+ # Pad the data before encryption
+ data = pad(data)
+
+ aes = AES.new(
+ bytes.fromhex(salted[:64]), AES.MODE_CBC, bytes.fromhex(salted[64:96])
+ )
+
+ return json.dumps(
+ {
+ "ct": base64.b64encode(aes.encrypt(data)).decode(),
+ "iv": salted[64:96],
+ "s": salt.encode().hex(),
+ }
+ )
+
+def unpad(data: bytes) -> bytes:
+ # Extract the padding value from the last byte and remove padding
+ padding_value = data[-1]
+ return data[:-padding_value]
+
+def decrypt(data: str, key: str):
+ # Parse JSON data
+ parsed_data = json.loads(base64.b64decode(data))
+ ct = base64.b64decode(parsed_data["ct"])
+ iv = bytes.fromhex(parsed_data["iv"])
+ salt = bytes.fromhex(parsed_data["s"])
+
+ salted = ''
+ dx = b''
+ for x in range(3):
+ dx = hashlib.md5(dx + key.encode() + salt).digest()
+ salted += dx.hex()
+
+ aes = AES.new(
+ bytes.fromhex(salted[:64]), AES.MODE_CBC, iv
+ )
+
+ data = aes.decrypt(ct)
+ if data.startswith(b'[{"key":'):
+ return unpad(data).decode()
\ No newline at end of file
diff --git a/g4f/Provider/openai/har_file.py b/g4f/Provider/openai/har_file.py
new file mode 100644
index 0000000000000000000000000000000000000000..e863b6acf7765e4f3059540c6c813c48b4cf6db9
--- /dev/null
+++ b/g4f/Provider/openai/har_file.py
@@ -0,0 +1,157 @@
+from __future__ import annotations
+
+import base64
+import json
+import os
+import re
+import time
+import uuid
+import random
+from urllib.parse import unquote
+from copy import deepcopy
+
+from .crypt import decrypt, encrypt
+from ...requests import StreamSession
+from ...cookies import get_cookies_dir
+from ... import debug
+
+arkose_url = "https://tcr9i.chat.openai.com/fc/gt2/public_key/35536E1E-65B4-4D96-9D97-6ADB7EFF8147"
+backend_url = "https://chatgpt.com/backend-api/conversation"
+backend_anon_url = "https://chatgpt.com/backend-anon/conversation"
+start_url = "https://chatgpt.com/"
+conversation_url = "https://chatgpt.com/c/"
+
+class NoValidHarFileError(Exception):
+ pass
+
+class RequestConfig:
+ cookies: dict = None
+ headers: dict = None
+ access_request_id: str = None
+ access_token: str = None
+ proof_token: list = None
+ turnstile_token: str = None
+ arkose_request: arkReq = None
+ arkose_token: str = None
+ headers: dict = {}
+ cookies: dict = {}
+
+class arkReq:
+ def __init__(self, arkURL, arkBx, arkHeader, arkBody, arkCookies, userAgent):
+ self.arkURL = arkURL
+ self.arkBx = arkBx
+ self.arkHeader = arkHeader
+ self.arkBody = arkBody
+ self.arkCookies = arkCookies
+ self.userAgent = userAgent
+
+def readHAR():
+ harPath = []
+ for root, _, files in os.walk(get_cookies_dir()):
+ for file in files:
+ if file.endswith(".har"):
+ harPath.append(os.path.join(root, file))
+ if not harPath:
+ raise NoValidHarFileError("No .har file found")
+ for path in harPath:
+ with open(path, 'rb') as file:
+ try:
+ harFile = json.loads(file.read())
+ except json.JSONDecodeError:
+ # Error: not a HAR file!
+ continue
+ for v in harFile['log']['entries']:
+ v_headers = get_headers(v)
+ if arkose_url == v['request']['url']:
+ RequestConfig.arkose_request = parseHAREntry(v)
+ elif v['request']['url'].startswith(start_url):
+ try:
+ match = re.search(r'"accessToken":"(.*?)"', v["response"]["content"]["text"])
+ if match:
+ RequestConfig.access_token = match.group(1)
+ except KeyError:
+ pass
+ try:
+ if "openai-sentinel-proof-token" in v_headers:
+ RequestConfig.headers = v_headers
+ RequestConfig.proof_token = json.loads(base64.b64decode(
+ v_headers["openai-sentinel-proof-token"].split("gAAAAAB", 1)[-1].encode()
+ ).decode())
+ if "openai-sentinel-turnstile-token" in v_headers:
+ RequestConfig.turnstile_token = v_headers["openai-sentinel-turnstile-token"]
+ if "authorization" in v_headers:
+ RequestConfig.access_token = v_headers["authorization"].split(" ")[1]
+ RequestConfig.cookies = {c['name']: c['value'] for c in v['request']['cookies']}
+ except Exception as e:
+ debug.log(f"Error on read headers: {e}")
+ if RequestConfig.proof_token is None:
+ raise NoValidHarFileError("No proof_token found in .har files")
+
+def get_headers(entry) -> dict:
+ return {h['name'].lower(): h['value'] for h in entry['request']['headers'] if h['name'].lower() not in ['content-length', 'cookie'] and not h['name'].startswith(':')}
+
+def parseHAREntry(entry) -> arkReq:
+ tmpArk = arkReq(
+ arkURL=entry['request']['url'],
+ arkBx="",
+ arkHeader=get_headers(entry),
+ arkBody={p['name']: unquote(p['value']) for p in entry['request']['postData']['params'] if p['name'] not in ['rnd']},
+ arkCookies={c['name']: c['value'] for c in entry['request']['cookies']},
+ userAgent=""
+ )
+ tmpArk.userAgent = tmpArk.arkHeader.get('user-agent', '')
+ bda = tmpArk.arkBody["bda"]
+ bw = tmpArk.arkHeader['x-ark-esync-value']
+ tmpArk.arkBx = decrypt(bda, tmpArk.userAgent + bw)
+ return tmpArk
+
+def genArkReq(chatArk: arkReq) -> arkReq:
+ tmpArk: arkReq = deepcopy(chatArk)
+ if tmpArk is None or not tmpArk.arkBody or not tmpArk.arkHeader:
+ raise RuntimeError("The .har file is not valid")
+ bda, bw = getBDA(tmpArk)
+
+ tmpArk.arkBody['bda'] = base64.b64encode(bda.encode()).decode()
+ tmpArk.arkBody['rnd'] = str(random.random())
+ tmpArk.arkHeader['x-ark-esync-value'] = bw
+ return tmpArk
+
+async def sendRequest(tmpArk: arkReq, proxy: str = None) -> str:
+ async with StreamSession(headers=tmpArk.arkHeader, cookies=tmpArk.arkCookies, proxies={"https": proxy}) as session:
+ async with session.post(tmpArk.arkURL, data=tmpArk.arkBody) as response:
+ data = await response.json()
+ arkose = data.get("token")
+ if "sup=1|rid=" not in arkose:
+ return RuntimeError("No valid arkose token generated")
+ return arkose
+
+def getBDA(arkReq: arkReq):
+ bx = arkReq.arkBx
+
+ bx = re.sub(r'"key":"n","value":"\S*?"', f'"key":"n","value":"{getN()}"', bx)
+ oldUUID_search = re.search(r'"key":"4b4b269e68","value":"(\S*?)"', bx)
+ if oldUUID_search:
+ oldUUID = oldUUID_search.group(1)
+ newUUID = str(uuid.uuid4())
+ bx = bx.replace(oldUUID, newUUID)
+
+ bw = getBw(getBt())
+ encrypted_bx = encrypt(bx, arkReq.userAgent + bw)
+ return encrypted_bx, bw
+
+def getBt() -> int:
+ return int(time.time())
+
+def getBw(bt: int) -> str:
+ return str(bt - (bt % 21600))
+
+def getN() -> str:
+ timestamp = str(int(time.time()))
+ return base64.b64encode(timestamp.encode()).decode()
+
+async def get_request_config(proxy: str) -> RequestConfig:
+ if RequestConfig.proof_token is None:
+ readHAR()
+ if RequestConfig.arkose_request is not None:
+ RequestConfig.arkose_token = await sendRequest(genArkReq(RequestConfig.arkose_request), proxy)
+ return RequestConfig
\ No newline at end of file
diff --git a/g4f/Provider/openai/new.py b/g4f/Provider/openai/new.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4d8e13d35ecb0bb13ffc5f1bb192f8e40677e82
--- /dev/null
+++ b/g4f/Provider/openai/new.py
@@ -0,0 +1,730 @@
+import hashlib
+import base64
+import random
+import json
+import time
+import uuid
+
+from collections import OrderedDict, defaultdict
+from typing import Any, Callable, Dict, List
+
+from datetime import (
+ datetime,
+ timedelta,
+ timezone
+)
+
+cores = [16, 24, 32]
+screens = [3000, 4000, 6000]
+maxAttempts = 500000
+
+navigator_keys = [
+ "registerProtocolHandlerāfunction registerProtocolHandler() { [native code] }",
+ "storageā[object StorageManager]",
+ "locksā[object LockManager]",
+ "appCodeNameāMozilla",
+ "permissionsā[object Permissions]",
+ "appVersionā5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36 Edg/125.0.0.0",
+ "shareāfunction share() { [native code] }",
+ "webdriverāfalse",
+ "managedā[object NavigatorManagedData]",
+ "canShareāfunction canShare() { [native code] }",
+ "vendorāGoogle Inc.",
+ "vendorāGoogle Inc.",
+ "mediaDevicesā[object MediaDevices]",
+ "vibrateāfunction vibrate() { [native code] }",
+ "storageBucketsā[object StorageBucketManager]",
+ "mediaCapabilitiesā[object MediaCapabilities]",
+ "getGamepadsāfunction getGamepads() { [native code] }",
+ "bluetoothā[object Bluetooth]",
+ "shareāfunction share() { [native code] }",
+ "cookieEnabledātrue",
+ "virtualKeyboardā[object VirtualKeyboard]",
+ "productāGecko",
+ "mediaDevicesā[object MediaDevices]",
+ "canShareāfunction canShare() { [native code] }",
+ "getGamepadsāfunction getGamepads() { [native code] }",
+ "productāGecko",
+ "xrā[object XRSystem]",
+ "clipboardā[object Clipboard]",
+ "storageBucketsā[object StorageBucketManager]",
+ "unregisterProtocolHandlerāfunction unregisterProtocolHandler() { [native code] }",
+ "productSubā20030107",
+ "loginā[object NavigatorLogin]",
+ "vendorSubā",
+ "loginā[object NavigatorLogin]",
+ "userAgentāMozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36 Edg/125.0.0.0",
+ "getInstalledRelatedAppsāfunction getInstalledRelatedApps() { [native code] }",
+ "userAgentāMozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36 Edg/125.0.0.0",
+ "mediaDevicesā[object MediaDevices]",
+ "locksā[object LockManager]",
+ "webkitGetUserMediaāfunction webkitGetUserMedia() { [native code] }",
+ "vendorāGoogle Inc.",
+ "xrā[object XRSystem]",
+ "mediaDevicesā[object MediaDevices]",
+ "virtualKeyboardā[object VirtualKeyboard]",
+ "userAgentāMozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36 Edg/125.0.0.0",
+ "virtualKeyboardā[object VirtualKeyboard]",
+ "appNameāNetscape",
+ "storageBucketsā[object StorageBucketManager]",
+ "presentationā[object Presentation]",
+ "onLineātrue",
+ "mimeTypesā[object MimeTypeArray]",
+ "credentialsā[object CredentialsContainer]",
+ "presentationā[object Presentation]",
+ "getGamepadsāfunction getGamepads() { [native code] }",
+ "vendorSubā",
+ "virtualKeyboardā[object VirtualKeyboard]",
+ "serviceWorkerā[object ServiceWorkerContainer]",
+ "xrā[object XRSystem]",
+ "productāGecko",
+ "keyboardā[object Keyboard]",
+ "gpuā[object GPU]",
+ "getInstalledRelatedAppsāfunction getInstalledRelatedApps() { [native code] }",
+ "webkitPersistentStorageā[object DeprecatedStorageQuota]",
+ "doNotTrack",
+ "clearAppBadgeāfunction clearAppBadge() { [native code] }",
+ "presentationā[object Presentation]",
+ "serialā[object Serial]",
+ "locksā[object LockManager]",
+ "requestMIDIAccessāfunction requestMIDIAccess() { [native code] }",
+ "locksā[object LockManager]",
+ "requestMediaKeySystemAccessāfunction requestMediaKeySystemAccess() { [native code] }",
+ "vendorāGoogle Inc.",
+ "pdfViewerEnabledātrue",
+ "languageāzh-CN",
+ "setAppBadgeāfunction setAppBadge() { [native code] }",
+ "geolocationā[object Geolocation]",
+ "userAgentDataā[object NavigatorUAData]",
+ "mediaCapabilitiesā[object MediaCapabilities]",
+ "requestMIDIAccessāfunction requestMIDIAccess() { [native code] }",
+ "getUserMediaāfunction getUserMedia() { [native code] }",
+ "mediaDevicesā[object MediaDevices]",
+ "webkitPersistentStorageā[object DeprecatedStorageQuota]",
+ "userAgentāMozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36 Edg/125.0.0.0",
+ "sendBeaconāfunction sendBeacon() { [native code] }",
+ "hardwareConcurrencyā32",
+ "appVersionā5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36 Edg/125.0.0.0",
+ "credentialsā[object CredentialsContainer]",
+ "storageā[object StorageManager]",
+ "cookieEnabledātrue",
+ "pdfViewerEnabledātrue",
+ "windowControlsOverlayā[object WindowControlsOverlay]",
+ "schedulingā[object Scheduling]",
+ "pdfViewerEnabledātrue",
+ "hardwareConcurrencyā32",
+ "xrā[object XRSystem]",
+ "userAgentāMozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/125.0.0.0 Safari/537.36 Edg/125.0.0.0",
+ "webdriverāfalse",
+ "getInstalledRelatedAppsāfunction getInstalledRelatedApps() { [native code] }",
+ "getInstalledRelatedAppsāfunction getInstalledRelatedApps() { [native code] }",
+ "bluetoothā[object Bluetooth]"
+]
+
+window_keys = [
+ "0",
+ "window",
+ "self",
+ "document",
+ "name",
+ "location",
+ "customElements",
+ "history",
+ "navigation",
+ "locationbar",
+ "menubar",
+ "personalbar",
+ "scrollbars",
+ "statusbar",
+ "toolbar",
+ "status",
+ "closed",
+ "frames",
+ "length",
+ "top",
+ "opener",
+ "parent",
+ "frameElement",
+ "navigator",
+ "origin",
+ "external",
+ "screen",
+ "innerWidth",
+ "innerHeight",
+ "scrollX",
+ "pageXOffset",
+ "scrollY",
+ "pageYOffset",
+ "visualViewport",
+ "screenX",
+ "screenY",
+ "outerWidth",
+ "outerHeight",
+ "devicePixelRatio",
+ "clientInformation",
+ "screenLeft",
+ "screenTop",
+ "styleMedia",
+ "onsearch",
+ "isSecureContext",
+ "trustedTypes",
+ "performance",
+ "onappinstalled",
+ "onbeforeinstallprompt",
+ "crypto",
+ "indexedDB",
+ "sessionStorage",
+ "localStorage",
+ "onbeforexrselect",
+ "onabort",
+ "onbeforeinput",
+ "onbeforematch",
+ "onbeforetoggle",
+ "onblur",
+ "oncancel",
+ "oncanplay",
+ "oncanplaythrough",
+ "onchange",
+ "onclick",
+ "onclose",
+ "oncontentvisibilityautostatechange",
+ "oncontextlost",
+ "oncontextmenu",
+ "oncontextrestored",
+ "oncuechange",
+ "ondblclick",
+ "ondrag",
+ "ondragend",
+ "ondragenter",
+ "ondragleave",
+ "ondragover",
+ "ondragstart",
+ "ondrop",
+ "ondurationchange",
+ "onemptied",
+ "onended",
+ "onerror",
+ "onfocus",
+ "onformdata",
+ "oninput",
+ "oninvalid",
+ "onkeydown",
+ "onkeypress",
+ "onkeyup",
+ "onload",
+ "onloadeddata",
+ "onloadedmetadata",
+ "onloadstart",
+ "onmousedown",
+ "onmouseenter",
+ "onmouseleave",
+ "onmousemove",
+ "onmouseout",
+ "onmouseover",
+ "onmouseup",
+ "onmousewheel",
+ "onpause",
+ "onplay",
+ "onplaying",
+ "onprogress",
+ "onratechange",
+ "onreset",
+ "onresize",
+ "onscroll",
+ "onsecuritypolicyviolation",
+ "onseeked",
+ "onseeking",
+ "onselect",
+ "onslotchange",
+ "onstalled",
+ "onsubmit",
+ "onsuspend",
+ "ontimeupdate",
+ "ontoggle",
+ "onvolumechange",
+ "onwaiting",
+ "onwebkitanimationend",
+ "onwebkitanimationiteration",
+ "onwebkitanimationstart",
+ "onwebkittransitionend",
+ "onwheel",
+ "onauxclick",
+ "ongotpointercapture",
+ "onlostpointercapture",
+ "onpointerdown",
+ "onpointermove",
+ "onpointerrawupdate",
+ "onpointerup",
+ "onpointercancel",
+ "onpointerover",
+ "onpointerout",
+ "onpointerenter",
+ "onpointerleave",
+ "onselectstart",
+ "onselectionchange",
+ "onanimationend",
+ "onanimationiteration",
+ "onanimationstart",
+ "ontransitionrun",
+ "ontransitionstart",
+ "ontransitionend",
+ "ontransitioncancel",
+ "onafterprint",
+ "onbeforeprint",
+ "onbeforeunload",
+ "onhashchange",
+ "onlanguagechange",
+ "onmessage",
+ "onmessageerror",
+ "onoffline",
+ "ononline",
+ "onpagehide",
+ "onpageshow",
+ "onpopstate",
+ "onrejectionhandled",
+ "onstorage",
+ "onunhandledrejection",
+ "onunload",
+ "crossOriginIsolated",
+ "scheduler",
+ "alert",
+ "atob",
+ "blur",
+ "btoa",
+ "cancelAnimationFrame",
+ "cancelIdleCallback",
+ "captureEvents",
+ "clearInterval",
+ "clearTimeout",
+ "close",
+ "confirm",
+ "createImageBitmap",
+ "fetch",
+ "find",
+ "focus",
+ "getComputedStyle",
+ "getSelection",
+ "matchMedia",
+ "moveBy",
+ "moveTo",
+ "open",
+ "postMessage",
+ "print",
+ "prompt",
+ "queueMicrotask",
+ "releaseEvents",
+ "reportError",
+ "requestAnimationFrame",
+ "requestIdleCallback",
+ "resizeBy",
+ "resizeTo",
+ "scroll",
+ "scrollBy",
+ "scrollTo",
+ "setInterval",
+ "setTimeout",
+ "stop",
+ "structuredClone",
+ "webkitCancelAnimationFrame",
+ "webkitRequestAnimationFrame",
+ "chrome",
+ "g_opr",
+ "opr",
+ "ethereum",
+ "caches",
+ "cookieStore",
+ "ondevicemotion",
+ "ondeviceorientation",
+ "ondeviceorientationabsolute",
+ "launchQueue",
+ "documentPictureInPicture",
+ "getScreenDetails",
+ "queryLocalFonts",
+ "showDirectoryPicker",
+ "showOpenFilePicker",
+ "showSaveFilePicker",
+ "originAgentCluster",
+ "credentialless",
+ "speechSynthesis",
+ "onscrollend",
+ "webkitRequestFileSystem",
+ "webkitResolveLocalFileSystemURL",
+ "__remixContext",
+ "__oai_SSR_TTI",
+ "__remixManifest",
+ "__reactRouterVersion",
+ "DD_RUM",
+ "__REACT_INTL_CONTEXT__",
+ "filterCSS",
+ "filterXSS",
+ "__SEGMENT_INSPECTOR__",
+ "DD_LOGS",
+ "regeneratorRuntime",
+ "_g",
+ "__remixRouteModules",
+ "__remixRouter",
+ "__STATSIG_SDK__",
+ "__STATSIG_JS_SDK__",
+ "__STATSIG_RERENDER_OVERRIDE__",
+ "_oaiHandleSessionExpired"
+]
+
+def get_parse_time():
+ now = datetime.now(timezone(timedelta(hours=-5)))
+ return now.strftime("%a %b %d %Y %H:%M:%S") + " GMT+0200 (Central European Summer Time)"
+
+def get_config(user_agent):
+
+ core = random.choice(cores)
+ screen = random.choice(screens)
+
+ # partially hardcoded config
+ config = [
+ core + screen,
+ get_parse_time(),
+ 4294705152,
+ random.random(),
+ user_agent,
+ None,
+ "remix-prod-15f1ec0f78ad898b9606a88d384ef76345b82b82", #document.documentElement.getAttribute("data-build"),
+ "en-US",
+ "en-US,es-US,en,es",
+ 0,
+ random.choice(navigator_keys),
+ 'location',
+ random.choice(window_keys),
+ time.perf_counter(),
+ str(uuid.uuid4()),
+ ]
+
+ return config
+
+
+def get_answer_token(seed, diff, config):
+ answer, solved = generate_answer(seed, diff, config)
+
+ if solved:
+ return "gAAAAAB" + answer
+ else:
+ raise Exception("Failed to solve 'gAAAAAB' challenge")
+
+def generate_answer(seed, diff, config):
+ diff_len = len(diff)
+ seed_encoded = seed.encode()
+ p1 = (json.dumps(config[:3], separators=(',', ':'), ensure_ascii=False)[:-1] + ',').encode()
+ p2 = (',' + json.dumps(config[4:9], separators=(',', ':'), ensure_ascii=False)[1:-1] + ',').encode()
+ p3 = (',' + json.dumps(config[10:], separators=(',', ':'), ensure_ascii=False)[1:]).encode()
+
+ target_diff = bytes.fromhex(diff)
+
+ for i in range(maxAttempts):
+ d1 = str(i).encode()
+ d2 = str(i >> 1).encode()
+
+ string = (
+ p1
+ + d1
+ + p2
+ + d2
+ + p3
+ )
+
+ base_encode = base64.b64encode(string)
+ hash_value = hashlib.new("sha3_512", seed_encoded + base_encode).digest()
+
+ if hash_value[:diff_len] <= target_diff:
+ return base_encode.decode(), True
+
+ return 'wQ8Lk5FbGpA2NcR9dShT6gYjU7VxZ4D' + base64.b64encode(f'"{seed}"'.encode()).decode(), False
+
+def get_requirements_token(config):
+ require, solved = generate_answer(format(random.random()), "0fffff", config)
+
+ if solved:
+ return 'gAAAAAC' + require
+ else:
+ raise Exception("Failed to solve 'gAAAAAC' challenge")
+
+
+### processing turnstile token
+
+class OrderedMap:
+ def __init__(self):
+ self.map = OrderedDict()
+
+ def add(self, key: str, value: Any):
+ self.map[key] = value
+
+ def to_json(self):
+ return json.dumps(self.map)
+
+ def __str__(self):
+ return self.to_json()
+
+
+TurnTokenList = List[List[Any]]
+FloatMap = Dict[float, Any]
+StringMap = Dict[str, Any]
+FuncType = Callable[..., Any]
+
+start_time = time.time()
+
+def get_turnstile_token(dx: str, p: str) -> str:
+ decoded_bytes = base64.b64decode(dx)
+ # print(decoded_bytes.decode())
+ return process_turnstile_token(decoded_bytes.decode(), p)
+
+
+def process_turnstile_token(dx: str, p: str) -> str:
+ result = []
+ p_length = len(p)
+ if p_length != 0:
+ for i, r in enumerate(dx):
+ result.append(chr(ord(r) ^ ord(p[i % p_length])))
+ else:
+ result = list(dx)
+ return "".join(result)
+
+
+def is_slice(input_val: Any) -> bool:
+ return isinstance(input_val, (list, tuple))
+
+
+def is_float(input_val: Any) -> bool:
+ return isinstance(input_val, float)
+
+
+def is_string(input_val: Any) -> bool:
+ return isinstance(input_val, str)
+
+
+def to_str(input_val: Any) -> str:
+ if input_val is None:
+ return "undefined"
+ elif is_float(input_val):
+ return f"{input_val:.16g}"
+ elif is_string(input_val):
+ special_cases = {
+ "window.Math": "[object Math]",
+ "window.Reflect": "[object Reflect]",
+ "window.performance": "[object Performance]",
+ "window.localStorage": "[object Storage]",
+ "window.Object": "function Object() { [native code] }",
+ "window.Reflect.set": "function set() { [native code] }",
+ "window.performance.now": "function () { [native code] }",
+ "window.Object.create": "function create() { [native code] }",
+ "window.Object.keys": "function keys() { [native code] }",
+ "window.Math.random": "function random() { [native code] }",
+ }
+ return special_cases.get(input_val, input_val)
+ elif isinstance(input_val, list) and all(
+ isinstance(item, str) for item in input_val
+ ):
+ return ",".join(input_val)
+ else:
+ # print(f"Type of input is: {type(input_val)}")
+ return str(input_val)
+
+
+def get_func_map() -> FloatMap:
+ process_map: FloatMap = defaultdict(lambda: None)
+
+ def func_1(e: float, t: float):
+ e_str = to_str(process_map[e])
+ t_str = to_str(process_map[t])
+ if e_str is not None and t_str is not None:
+ res = process_turnstile_token(e_str, t_str)
+ process_map[e] = res
+ else:
+ pass
+ # print(f"Warning: Unable to process func_1 for e={e}, t={t}")
+
+ def func_2(e: float, t: Any):
+ process_map[e] = t
+
+ def func_5(e: float, t: float):
+ n = process_map[e]
+ tres = process_map[t]
+ if n is None:
+ process_map[e] = tres
+ elif is_slice(n):
+ nt = n + [tres] if tres is not None else n
+ process_map[e] = nt
+ else:
+ if is_string(n) or is_string(tres):
+ res = to_str(n) + to_str(tres)
+ elif is_float(n) and is_float(tres):
+ res = n + tres
+ else:
+ res = "NaN"
+ process_map[e] = res
+
+ def func_6(e: float, t: float, n: float):
+ tv = process_map[t]
+ nv = process_map[n]
+ if is_string(tv) and is_string(nv):
+ res = f"{tv}.{nv}"
+ if res == "window.document.location":
+ process_map[e] = "https://chatgpt.com/"
+ else:
+ process_map[e] = res
+ else:
+ pass
+ # print("func type 6 error")
+
+ def func_24(e: float, t: float, n: float):
+ tv = process_map[t]
+ nv = process_map[n]
+ if is_string(tv) and is_string(nv):
+ process_map[e] = f"{tv}.{nv}"
+ else:
+ pass
+ # print("func type 24 error")
+
+ def func_7(e: float, *args):
+ n = [process_map[arg] for arg in args]
+ ev = process_map[e]
+ if isinstance(ev, str):
+ if ev == "window.Reflect.set":
+ obj = n[0]
+ key_str = str(n[1])
+ val = n[2]
+ obj.add(key_str, val)
+ elif callable(ev):
+ ev(*n)
+
+ def func_17(e: float, t: float, *args):
+ i = [process_map[arg] for arg in args]
+ tv = process_map[t]
+ res = None
+ if isinstance(tv, str):
+ if tv == "window.performance.now":
+ current_time = time.time_ns()
+ elapsed_ns = current_time - int(start_time * 1e9)
+ res = (elapsed_ns + random.random()) / 1e6
+ elif tv == "window.Object.create":
+ res = OrderedMap()
+ elif tv == "window.Object.keys":
+ if isinstance(i[0], str) and i[0] == "window.localStorage":
+ res = [
+ "STATSIG_LOCAL_STORAGE_INTERNAL_STORE_V4",
+ "STATSIG_LOCAL_STORAGE_STABLE_ID",
+ "client-correlated-secret",
+ "oai/apps/capExpiresAt",
+ "oai-did",
+ "STATSIG_LOCAL_STORAGE_LOGGING_REQUEST",
+ "UiState.isNavigationCollapsed.1",
+ ]
+ elif tv == "window.Math.random":
+ res = random.random()
+ elif callable(tv):
+ res = tv(*i)
+ process_map[e] = res
+
+ def func_8(e: float, t: float):
+ process_map[e] = process_map[t]
+
+ def func_14(e: float, t: float):
+ tv = process_map[t]
+ if is_string(tv):
+ try:
+ token_list = json.loads(tv)
+ process_map[e] = token_list
+ except json.JSONDecodeError:
+ # print(f"Warning: Unable to parse JSON for key {t}")
+ process_map[e] = None
+ else:
+ # print(f"Warning: Value for key {t} is not a string")
+ process_map[e] = None
+
+ def func_15(e: float, t: float):
+ tv = process_map[t]
+ process_map[e] = json.dumps(tv)
+
+ def func_18(e: float):
+ ev = process_map[e]
+ e_str = to_str(ev)
+ decoded = base64.b64decode(e_str).decode()
+ process_map[e] = decoded
+
+ def func_19(e: float):
+ ev = process_map[e]
+ e_str = to_str(ev)
+ encoded = base64.b64encode(e_str.encode()).decode()
+ process_map[e] = encoded
+
+ def func_20(e: float, t: float, n: float, *args):
+ o = [process_map[arg] for arg in args]
+ ev = process_map[e]
+ tv = process_map[t]
+ if ev == tv:
+ nv = process_map[n]
+ if callable(nv):
+ nv(*o)
+ else:
+ pass
+ # print("func type 20 error")
+
+ def func_21(*args):
+ pass
+
+ def func_23(e: float, t: float, *args):
+ i = list(args)
+ ev = process_map[e]
+ tv = process_map[t]
+ if ev is not None and callable(tv):
+ tv(*i)
+
+ process_map.update(
+ {
+ 1: func_1,
+ 2: func_2,
+ 5: func_5,
+ 6: func_6,
+ 7: func_7,
+ 8: func_8,
+ 10: "window",
+ 14: func_14,
+ 15: func_15,
+ 17: func_17,
+ 18: func_18,
+ 19: func_19,
+ 20: func_20,
+ 21: func_21,
+ 23: func_23,
+ 24: func_24,
+ }
+ )
+
+ return process_map
+
+
+def process_turnstile(dx: str, p: str) -> str:
+ tokens = get_turnstile_token(dx, p)
+ res = ""
+ token_list = json.loads(tokens)
+ process_map = get_func_map()
+
+ def func_3(e: str):
+ nonlocal res
+ res = base64.b64encode(e.encode()).decode()
+
+ process_map[3] = func_3
+ process_map[9] = token_list
+ process_map[16] = p
+
+ for token in token_list:
+ try:
+ e = token[0]
+ t = token[1:]
+ f = process_map.get(e)
+ if callable(f):
+ f(*t)
+ else:
+ pass
+ # print(f"Warning: No function found for key {e}")
+ except Exception as exc:
+ raise Exception(f"Error processing token {token}: {exc}")
+ # print(f"Error processing token {token}: {exc}")
+
+ return res
\ No newline at end of file
diff --git a/g4f/Provider/openai/proofofwork.py b/g4f/Provider/openai/proofofwork.py
new file mode 100644
index 0000000000000000000000000000000000000000..4294c99a3756fe27d859afc7985fb9695a147ee7
--- /dev/null
+++ b/g4f/Provider/openai/proofofwork.py
@@ -0,0 +1,38 @@
+import random
+import hashlib
+import json
+import base64
+from datetime import datetime, timezone
+
+def generate_proof_token(required: bool, seed: str = "", difficulty: str = "", user_agent: str = None, proof_token: str = None):
+ if not required:
+ return
+
+ if proof_token is None:
+ screen = random.choice([3008, 4010, 6000]) * random.choice([1, 2, 4])
+ # Get current UTC time
+ now_utc = datetime.now(timezone.utc)
+ parse_time = now_utc.strftime('%a, %d %b %Y %H:%M:%S GMT')
+ proof_token = [
+ screen, parse_time,
+ None, 0, user_agent,
+ "https://tcr9i.chat.openai.com/v2/35536E1E-65B4-4D96-9D97-6ADB7EFF8147/api.js",
+ "dpl=1440a687921de39ff5ee56b92807faaadce73f13","en","en-US",
+ None,
+ "pluginsā[object PluginArray]",
+ random.choice(["_reactListeningcfilawjnerp", "_reactListening9ne2dfo1i47", "_reactListening410nzwhan2a"]),
+ random.choice(["alert", "ontransitionend", "onprogress"])
+ ]
+
+ diff_len = len(difficulty)
+ for i in range(100000):
+ proof_token[3] = i
+ json_data = json.dumps(proof_token)
+ base = base64.b64encode(json_data.encode()).decode()
+ hash_value = hashlib.sha3_512((seed + base).encode()).digest()
+
+ if hash_value.hex()[:diff_len] <= difficulty:
+ return "gAAAAAB" + base
+
+ fallback_base = base64.b64encode(f'"{seed}"'.encode()).decode()
+ return "gAAAAABwQ8Lk5FbGpA2NcR9dShT6gYjU7VxZ4D" + fallback_base
diff --git a/g4f/Provider/selenium/PerplexityAi.py b/g4f/Provider/selenium/PerplexityAi.py
new file mode 100644
index 0000000000000000000000000000000000000000..d965dbf70dfb15189f918dcd42a2be67ba35a40c
--- /dev/null
+++ b/g4f/Provider/selenium/PerplexityAi.py
@@ -0,0 +1,108 @@
+from __future__ import annotations
+
+import time
+
+try:
+ from selenium.webdriver.common.by import By
+ from selenium.webdriver.support.ui import WebDriverWait
+ from selenium.webdriver.support import expected_conditions as EC
+except ImportError:
+ pass
+
+from ...typing import CreateResult, Messages
+from ..base_provider import AbstractProvider
+from ..helper import format_prompt
+from ...webdriver import WebDriver, WebDriverSession, element_send_text
+
+class PerplexityAi(AbstractProvider):
+ url = "https://www.perplexity.ai"
+ working = False
+ supports_gpt_35_turbo = True
+ supports_stream = True
+
+ @classmethod
+ def create_completion(
+ cls,
+ model: str,
+ messages: Messages,
+ stream: bool,
+ proxy: str = None,
+ timeout: int = 120,
+ webdriver: WebDriver = None,
+ virtual_display: bool = True,
+ copilot: bool = False,
+ **kwargs
+ ) -> CreateResult:
+ with WebDriverSession(webdriver, "", virtual_display=virtual_display, proxy=proxy) as driver:
+ prompt = format_prompt(messages)
+
+ driver.get(f"{cls.url}/")
+ wait = WebDriverWait(driver, timeout)
+
+ # Is page loaded?
+ wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, "textarea[placeholder='Ask anything...']")))
+
+ # Register WebSocket hook
+ script = """
+window._message = window._last_message = "";
+window._message_finished = false;
+const _socket_send = WebSocket.prototype.send;
+WebSocket.prototype.send = function(...args) {
+ if (!window.socket_onmessage) {
+ window._socket_onmessage = this;
+ this.addEventListener("message", (event) => {
+ if (event.data.startsWith("42")) {
+ let data = JSON.parse(event.data.substring(2));
+ if (data[0] =="query_progress" || data[0] == "query_answered") {
+ let content = JSON.parse(data[1]["text"]);
+ if (data[1]["mode"] == "copilot") {
+ content = content[content.length-1]["content"]["answer"];
+ content = JSON.parse(content);
+ }
+ window._message = content["answer"];
+ if (!window._message_finished) {
+ window._message_finished = data[0] == "query_answered";
+ }
+ }
+ }
+ });
+ }
+ return _socket_send.call(this, ...args);
+};
+"""
+ driver.execute_script(script)
+
+ if copilot:
+ try:
+ # Check for account
+ driver.find_element(By.CSS_SELECTOR, "img[alt='User avatar']")
+ # Enable copilot
+ driver.find_element(By.CSS_SELECTOR, "button[data-testid='copilot-toggle']").click()
+ except:
+ raise RuntimeError("You need a account for copilot")
+
+ # Submit prompt
+ element_send_text(driver.find_element(By.CSS_SELECTOR, "textarea[placeholder='Ask anything...']"), prompt)
+
+ # Stream response
+ script = """
+if(window._message && window._message != window._last_message) {
+ try {
+ return window._message.substring(window._last_message.length);
+ } finally {
+ window._last_message = window._message;
+ }
+} else if(window._message_finished) {
+ return null;
+} else {
+ return '';
+}
+"""
+ while True:
+ chunk = driver.execute_script(script)
+ if chunk:
+ yield chunk
+ elif chunk != "":
+ break
+ else:
+ time.sleep(0.1)
diff --git a/g4f/Provider/selenium/Phind.py b/g4f/Provider/selenium/Phind.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6f7cc072404bc62b7ca61656954ee55660b6c9b
--- /dev/null
+++ b/g4f/Provider/selenium/Phind.py
@@ -0,0 +1,103 @@
+from __future__ import annotations
+
+import time
+from urllib.parse import quote
+
+from ...typing import CreateResult, Messages
+from ..base_provider import AbstractProvider
+from ..helper import format_prompt
+from ...webdriver import WebDriver, WebDriverSession
+
+class Phind(AbstractProvider):
+ url = "https://www.phind.com"
+ working = False
+ supports_gpt_4 = True
+ supports_stream = True
+
+ @classmethod
+ def create_completion(
+ cls,
+ model: str,
+ messages: Messages,
+ stream: bool,
+ proxy: str = None,
+ timeout: int = 120,
+ webdriver: WebDriver = None,
+ creative_mode: bool = None,
+ **kwargs
+ ) -> CreateResult:
+ with WebDriverSession(webdriver, "", proxy=proxy) as driver:
+ from selenium.webdriver.common.by import By
+ from selenium.webdriver.support.ui import WebDriverWait
+ from selenium.webdriver.support import expected_conditions as EC
+
+ # Register fetch hook
+ source = """
+window._fetch = window.fetch;
+window.fetch = async (url, options) => {
+ const response = await window._fetch(url, options);
+ if (url != "/api/infer/answer") {
+ return response;
+ }
+ copy = response.clone();
+ window._reader = response.body.pipeThrough(new TextDecoderStream()).getReader();
+ return copy;
+}
+"""
+ driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument", {
+ "source": source
+ })
+
+ prompt = quote(format_prompt(messages))
+ driver.get(f"{cls.url}/search?q={prompt}&source=searchbox")
+
+ # Need to change settings
+ wait = WebDriverWait(driver, timeout)
+ def open_dropdown():
+ # Open settings dropdown
+ wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, "button.text-dark.dropdown-toggle")))
+ driver.find_element(By.CSS_SELECTOR, "button.text-dark.dropdown-toggle").click()
+ # Wait for dropdown toggle
+ wait.until(EC.visibility_of_element_located((By.XPATH, "//button[text()='GPT-4']")))
+ if model.startswith("gpt-4") or creative_mode:
+ # Enable GPT-4
+ if model.startswith("gpt-4"):
+ open_dropdown()
+ driver.find_element(By.XPATH, "//button[text()='GPT-4']").click()
+ # Enable creative mode
+ if creative_mode or creative_mode == None:
+ open_dropdown()
+ driver.find_element(By.ID, "Creative Mode").click()
+ # Submit changes
+ driver.find_element(By.CSS_SELECTOR, ".search-bar-input-group button[type='submit']").click()
+ # Wait for page reload
+ wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, ".search-container")))
+
+ while True:
+ chunk = driver.execute_script("""
+if(window._reader) {
+ chunk = await window._reader.read();
+ if (chunk['done']) {
+ return null;
+ }
+ content = '';
+ chunk['value'].split('\\r\\n').forEach((line, index) => {
+ if (line.startsWith('data: ')) {
+ line = line.substring('data: '.length);
+ if (!line.startsWith('')) {
+ if (line) content += line;
+ else content += '\\n';
+ }
+ }
+ });
+ return content.replace('\\n\\n', '\\n');
+} else {
+ return ''
+}
+""")
+ if chunk:
+ yield chunk
+ elif chunk != "":
+ break
+ else:
+ time.sleep(0.1)
\ No newline at end of file
diff --git a/g4f/Provider/selenium/TalkAi.py b/g4f/Provider/selenium/TalkAi.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7b6337560d6f6f5d8771d37215bab7ff7136d79
--- /dev/null
+++ b/g4f/Provider/selenium/TalkAi.py
@@ -0,0 +1,86 @@
+from __future__ import annotations
+
+import time, json, time
+
+from ...typing import CreateResult, Messages
+from ..base_provider import AbstractProvider
+from ...webdriver import WebDriver, WebDriverSession
+
+class TalkAi(AbstractProvider):
+ url = "https://talkai.info"
+ working = False
+ supports_gpt_35_turbo = True
+ supports_stream = True
+
+ @classmethod
+ def create_completion(
+ cls,
+ model: str,
+ messages: Messages,
+ stream: bool,
+ proxy: str = None,
+ webdriver: WebDriver = None,
+ **kwargs
+ ) -> CreateResult:
+ with WebDriverSession(webdriver, "", virtual_display=True, proxy=proxy) as driver:
+ from selenium.webdriver.common.by import By
+ from selenium.webdriver.support.ui import WebDriverWait
+ from selenium.webdriver.support import expected_conditions as EC
+
+ driver.get(f"{cls.url}/chat/")
+
+ # Wait for page load
+ WebDriverWait(driver, 240).until(
+ EC.presence_of_element_located((By.CSS_SELECTOR, "body.chat-page"))
+ )
+
+ data = {
+ "type": "chat",
+ "message": messages[-1]["content"],
+ "messagesHistory": [{
+ "from": "you" if message["role"] == "user" else "chatGPT",
+ "content": message["content"]
+ } for message in messages],
+ "model": model if model else "gpt-3.5-turbo",
+ "max_tokens": 2048,
+ "temperature": 1,
+ "top_p": 1,
+ "presence_penalty": 0,
+ "frequency_penalty": 0,
+ **kwargs
+ }
+ script = """
+const response = await fetch("/chat/send2/", {
+ "headers": {
+ "Accept": "application/json",
+ "Content-Type": "application/json",
+ },
+ "body": {body},
+ "method": "POST"
+});
+window._reader = response.body.pipeThrough(new TextDecoderStream()).getReader();
+"""
+ driver.execute_script(
+ script.replace("{body}", json.dumps(json.dumps(data)))
+ )
+ # Read response
+ while True:
+ chunk = driver.execute_script("""
+chunk = await window._reader.read();
+if (chunk.done) {
+ return null;
+}
+content = "";
+for (line of chunk.value.split("\\n")) {
+ if (line.startsWith('data: ')) {
+ content += line.substring('data: '.length);
+ }
+}
+return content;
+""")
+ if chunk:
+ yield chunk.replace("\\n", "\n")
+ elif chunk != "":
+ break
+ else:
+ time.sleep(0.1)
diff --git a/g4f/Provider/selenium/__init__.py b/g4f/Provider/selenium/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..44adf5fbdf2d850ea625c578fd56f718e175f18c
--- /dev/null
+++ b/g4f/Provider/selenium/__init__.py
@@ -0,0 +1,3 @@
+from .PerplexityAi import PerplexityAi
+from .Phind import Phind
+from .TalkAi import TalkAi
diff --git a/g4f/Provider/you/__init__.py b/g4f/Provider/you/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/g4f/Provider/you/har_file.py b/g4f/Provider/you/har_file.py
new file mode 100644
index 0000000000000000000000000000000000000000..40bf388267db740f2decabc6dcb8c98f84d48907
--- /dev/null
+++ b/g4f/Provider/you/har_file.py
@@ -0,0 +1,115 @@
+from __future__ import annotations
+
+import json
+import os
+import os.path
+import random
+import logging
+
+from ...requests import StreamSession, raise_for_status
+from ...cookies import get_cookies_dir
+from ...errors import MissingRequirementsError
+from ... import debug
+
+logger = logging.getLogger(__name__)
+
+class NoValidHarFileError(Exception):
+ ...
+
+class arkReq:
+ def __init__(self, arkURL, arkHeaders, arkBody, arkCookies, userAgent):
+ self.arkURL = arkURL
+ self.arkHeaders = arkHeaders
+ self.arkBody = arkBody
+ self.arkCookies = arkCookies
+ self.userAgent = userAgent
+
+telemetry_url = "https://telemetry.stytch.com/submit"
+public_token = "public-token-live-507a52ad-7e69-496b-aee0-1c9863c7c819"
+chatArks: list = None
+
+def readHAR():
+ harPath = []
+ chatArks = []
+ for root, dirs, files in os.walk(get_cookies_dir()):
+ for file in files:
+ if file.endswith(".har"):
+ harPath.append(os.path.join(root, file))
+ if harPath:
+ break
+ if not harPath:
+ raise NoValidHarFileError("No .har file found")
+ for path in harPath:
+ with open(path, 'rb') as file:
+ try:
+ harFile = json.load(file)
+ except json.JSONDecodeError:
+ # Error: not a HAR file!
+ continue
+ for v in harFile['log']['entries']:
+ if v['request']['url'] == telemetry_url:
+ chatArks.append(parseHAREntry(v))
+ if not chatArks:
+ raise NoValidHarFileError("No telemetry in .har files found")
+ return chatArks
+
+def parseHAREntry(entry) -> arkReq:
+ tmpArk = arkReq(
+ arkURL=entry['request']['url'],
+ arkHeaders={h['name'].lower(): h['value'] for h in entry['request']['headers'] if h['name'].lower() not in ['content-length', 'cookie'] and not h['name'].startswith(':')},
+ arkBody=entry['request']['postData']['text'],
+ arkCookies={c['name']: c['value'] for c in entry['request']['cookies']},
+ userAgent=""
+ )
+ tmpArk.userAgent = tmpArk.arkHeaders.get('user-agent', '')
+ return tmpArk
+
+async def sendRequest(tmpArk: arkReq, proxy: str = None):
+ async with StreamSession(headers=tmpArk.arkHeaders, cookies=tmpArk.arkCookies, proxy=proxy) as session:
+ async with session.post(tmpArk.arkURL, data=tmpArk.arkBody) as response:
+ await raise_for_status(response)
+ return await response.text()
+
+async def create_telemetry_id(proxy: str = None):
+ global chatArks
+ if chatArks is None:
+ chatArks = readHAR()
+ return await sendRequest(random.choice(chatArks), proxy)
+
+async def get_telemetry_ids(proxy: str = None) -> list:
+ try:
+ return [await create_telemetry_id(proxy)]
+ except NoValidHarFileError as e:
+ if debug.logging:
+ logger.error(e)
+
+ try:
+ from nodriver import start
+ except ImportError:
+ raise MissingRequirementsError('Add .har file from you.com or install "nodriver" package | pip install -U nodriver')
+ if debug.logging:
+ logger.error('Getting telemetry_id for you.com with nodriver')
+
+ browser = page = None
+ try:
+ browser = await start(
+ browser_args=None if proxy is None else [f"--proxy-server={proxy}"],
+ )
+ page = await browser.get("https://you.com")
+ while not await page.evaluate('"GetTelemetryID" in this'):
+ await page.sleep(1)
+ async def get_telemetry_id():
+ return await page.evaluate(
+ f'this.GetTelemetryID("{public_token}", "{telemetry_url}");',
+ await_promise=True
+ )
+ return [await get_telemetry_id()]
+ finally:
+ try:
+ if page is not None:
+ await page.close()
+ if browser is not None:
+ await browser.stop()
+ except Exception as e:
+ if debug.logging:
+ logger.error(e)
diff --git a/g4f/__init__.py b/g4f/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f59a1446bf9a67d25ae5240fd0df4800759d3c11
--- /dev/null
+++ b/g4f/__init__.py
@@ -0,0 +1,100 @@
+from __future__ import annotations
+
+import os
+import logging
+
+from . import debug, version
+from .models import Model
+from .client import Client, AsyncClient
+from .typing import Messages, CreateResult, AsyncResult, Union
+from .errors import StreamNotSupportedError, ModelNotAllowedError
+from .cookies import get_cookies, set_cookies
+from .providers.types import ProviderType
+from .providers.base_provider import AsyncGeneratorProvider
+from .client.service import get_model_and_provider, get_last_provider
+
+#Configure "g4f" logger
+logger = logging.getLogger(__name__)
+log_handler = logging.StreamHandler()
+log_handler.setFormatter(logging.Formatter(logging.BASIC_FORMAT))
+logger.addHandler(log_handler)
+
+logger.setLevel(logging.ERROR)
+
+class ChatCompletion:
+ @staticmethod
+ def create(model : Union[Model, str],
+ messages : Messages,
+ provider : Union[ProviderType, str, None] = None,
+ stream : bool = False,
+ auth : Union[str, None] = None,
+ ignored : list[str] = None,
+ ignore_working: bool = False,
+ ignore_stream: bool = False,
+ patch_provider: callable = None,
+ **kwargs) -> Union[CreateResult, str]:
+ model, provider = get_model_and_provider(
+ model, provider, stream,
+ ignored, ignore_working,
+ ignore_stream or kwargs.get("ignore_stream_and_auth")
+ )
+
+ if auth is not None:
+ kwargs['auth'] = auth
+
+ if "proxy" not in kwargs:
+ proxy = os.environ.get("G4F_PROXY")
+ if proxy:
+ kwargs['proxy'] = proxy
+
+ if patch_provider:
+ provider = patch_provider(provider)
+
+ result = provider.create_completion(model, messages, stream=stream, **kwargs)
+
+ return result if stream else ''.join([str(chunk) for chunk in result])
+
+ @staticmethod
+ def create_async(model : Union[Model, str],
+ messages : Messages,
+ provider : Union[ProviderType, str, None] = None,
+ stream : bool = False,
+ ignored : list[str] = None,
+ ignore_working: bool = False,
+ patch_provider: callable = None,
+ **kwargs) -> Union[AsyncResult, str]:
+ model, provider = get_model_and_provider(model, provider, False, ignored, ignore_working)
+
+ if stream:
+ if isinstance(provider, type) and issubclass(provider, AsyncGeneratorProvider):
+ return provider.create_async_generator(model, messages, **kwargs)
+ raise StreamNotSupportedError(f'{provider.__name__} does not support "stream" argument in "create_async"')
+
+ if patch_provider:
+ provider = patch_provider(provider)
+
+ return provider.create_async(model, messages, **kwargs)
+
+class Completion:
+ @staticmethod
+ def create(model : Union[Model, str],
+ prompt : str,
+ provider : Union[ProviderType, None] = None,
+ stream : bool = False,
+ ignored : list[str] = None, **kwargs) -> Union[CreateResult, str]:
+ allowed_models = [
+ 'code-davinci-002',
+ 'text-ada-001',
+ 'text-babbage-001',
+ 'text-curie-001',
+ 'text-davinci-002',
+ 'text-davinci-003'
+ ]
+ if model not in allowed_models:
+ raise ModelNotAllowedError(f'Can\'t use {model} with Completion.create()')
+
+ model, provider = get_model_and_provider(model, provider, stream, ignored)
+
+ result = provider.create_completion(model, [{"role": "user", "content": prompt}], stream=stream, **kwargs)
+
+ return result if stream else ''.join(result)
diff --git a/g4f/api/__init__.py b/g4f/api/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..01c75daeb6d643c2454c82e0047feb9baf257337
--- /dev/null
+++ b/g4f/api/__init__.py
@@ -0,0 +1,520 @@
+from __future__ import annotations
+
+import logging
+import json
+import uvicorn
+import secrets
+import os
+import shutil
+
+import os.path
+from fastapi import FastAPI, Response, Request, UploadFile, Depends
+from fastapi.middleware.wsgi import WSGIMiddleware
+from fastapi.responses import StreamingResponse, RedirectResponse, HTMLResponse, JSONResponse
+from fastapi.exceptions import RequestValidationError
+from fastapi.security import APIKeyHeader
+from starlette.exceptions import HTTPException
+from starlette.status import (
+ HTTP_200_OK,
+ HTTP_422_UNPROCESSABLE_ENTITY,
+ HTTP_404_NOT_FOUND,
+ HTTP_401_UNAUTHORIZED,
+ HTTP_403_FORBIDDEN,
+ HTTP_500_INTERNAL_SERVER_ERROR,
+)
+from fastapi.encoders import jsonable_encoder
+from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
+from fastapi.middleware.cors import CORSMiddleware
+from starlette.responses import FileResponse
+from pydantic import BaseModel, Field
+from typing import Union, Optional, List
+try:
+ from typing import Annotated
+except ImportError:
+ class Annotated:
+ pass
+
+import g4f
+import g4f.debug
+from g4f.client import AsyncClient, ChatCompletion, ImagesResponse, convert_to_provider
+from g4f.providers.response import BaseConversation
+from g4f.client.helper import filter_none
+from g4f.image import is_accepted_format, is_data_uri_an_image, images_dir
+from g4f.typing import Messages
+from g4f.errors import ProviderNotFoundError, ModelNotFoundError, MissingAuthError
+from g4f.cookies import read_cookie_files, get_cookies_dir
+from g4f.Provider import ProviderType, ProviderUtils, __providers__
+from g4f.gui import get_gui_app
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_PORT = 1337
+
+def create_app(g4f_api_key: str = None):
+ app = FastAPI()
+
+ # Add CORS middleware
+ app.add_middleware(
+ CORSMiddleware,
+ allow_origin_regex=".*",
+ allow_credentials=True,
+ allow_methods=["*"],
+ allow_headers=["*"],
+ )
+
+ api = Api(app, g4f_api_key=g4f_api_key)
+
+ if AppConfig.gui:
+ @app.get("/")
+ async def home():
+ return HTMLResponse(f'g4f v-{g4f.version.utils.current_version}:
'
+ 'Start to chat: /chat/
'
+ 'Open Swagger UI at: '
+ '/docs')
+
+ api.register_routes()
+ api.register_authorization()
+ api.register_validation_exception_handler()
+
+ if AppConfig.gui:
+ gui_app = WSGIMiddleware(get_gui_app())
+ app.mount("/", gui_app)
+
+ # Read cookie files if not ignored
+ if not AppConfig.ignore_cookie_files:
+ read_cookie_files()
+
+ return app
+
+def create_app_debug(g4f_api_key: str = None):
+ g4f.debug.logging = True
+ return create_app(g4f_api_key)
+
+class ChatCompletionsConfig(BaseModel):
+ messages: Messages = Field(examples=[[{"role": "system", "content": ""}, {"role": "user", "content": ""}]])
+ model: str = Field(default="")
+ provider: Optional[str] = None
+ stream: bool = False
+ image: Optional[str] = None
+ image_name: Optional[str] = None
+ temperature: Optional[float] = None
+ max_tokens: Optional[int] = None
+ stop: Union[list[str], str, None] = None
+ api_key: Optional[str] = None
+ web_search: Optional[bool] = None
+ proxy: Optional[str] = None
+ conversation_id: Optional[str] = None
+
+class ImageGenerationConfig(BaseModel):
+ prompt: str
+ model: Optional[str] = None
+ provider: Optional[str] = None
+ response_format: str = "url"
+ api_key: Optional[str] = None
+ proxy: Optional[str] = None
+
+class ProviderResponseModel(BaseModel):
+ id: str
+ object: str = "provider"
+ created: int
+ url: Optional[str]
+ label: Optional[str]
+
+class ProviderResponseDetailModel(ProviderResponseModel):
+ models: list[str]
+ image_models: list[str]
+ vision_models: list[str]
+ params: list[str]
+
+class ModelResponseModel(BaseModel):
+ id: str
+ object: str = "model"
+ created: int
+ owned_by: Optional[str]
+
+class ErrorResponseModel(BaseModel):
+ error: ErrorResponseMessageModel
+ model: Optional[str] = None
+ provider: Optional[str] = None
+
+class ErrorResponseMessageModel(BaseModel):
+ message: str
+
+class FileResponseModel(BaseModel):
+ filename: str
+
+class ErrorResponse(Response):
+ media_type = "application/json"
+
+ @classmethod
+ def from_exception(cls, exception: Exception,
+ config: Union[ChatCompletionsConfig, ImageGenerationConfig] = None,
+ status_code: int = HTTP_500_INTERNAL_SERVER_ERROR):
+ return cls(format_exception(exception, config), status_code)
+
+ @classmethod
+ def from_message(cls, message: str, status_code: int = HTTP_500_INTERNAL_SERVER_ERROR):
+ return cls(format_exception(message), status_code)
+
+ def render(self, content) -> bytes:
+ return str(content).encode(errors="ignore")
+
+class AppConfig:
+ ignored_providers: Optional[list[str]] = None
+ g4f_api_key: Optional[str] = None
+ ignore_cookie_files: bool = False
+ model: str = None,
+ provider: str = None
+ image_provider: str = None
+ proxy: str = None
+ gui: bool = False
+
+ @classmethod
+ def set_config(cls, **data):
+ for key, value in data.items():
+ setattr(cls, key, value)
+
+list_ignored_providers: list[str] = None
+
+def set_list_ignored_providers(ignored: list[str]):
+ global list_ignored_providers
+ list_ignored_providers = ignored
+
+class Api:
+ def __init__(self, app: FastAPI, g4f_api_key=None) -> None:
+ self.app = app
+ self.client = AsyncClient()
+ self.g4f_api_key = g4f_api_key
+ self.get_g4f_api_key = APIKeyHeader(name="g4f-api-key")
+ self.conversations: dict[str, dict[str, BaseConversation]] = {}
+
+ security = HTTPBearer(auto_error=False)
+
+ def register_authorization(self):
+ @self.app.middleware("http")
+ async def authorization(request: Request, call_next):
+ if self.g4f_api_key and request.url.path not in ("/", "/v1"):
+ try:
+ user_g4f_api_key = await self.get_g4f_api_key(request)
+ except HTTPException as e:
+ if e.status_code == 403:
+ return ErrorResponse.from_message("G4F API key required", HTTP_401_UNAUTHORIZED)
+ if not secrets.compare_digest(self.g4f_api_key, user_g4f_api_key):
+ return ErrorResponse.from_message("Invalid G4F API key", HTTP_403_FORBIDDEN)
+ return await call_next(request)
+
+ def register_validation_exception_handler(self):
+ @self.app.exception_handler(RequestValidationError)
+ async def validation_exception_handler(request: Request, exc: RequestValidationError):
+ details = exc.errors()
+ modified_details = []
+ for error in details:
+ modified_details.append({
+ "loc": error["loc"],
+ "message": error["msg"],
+ "type": error["type"],
+ })
+ return JSONResponse(
+ status_code=HTTP_422_UNPROCESSABLE_ENTITY,
+ content=jsonable_encoder({"detail": modified_details}),
+ )
+
+ def register_routes(self):
+ @self.app.get("/")
+ async def read_root():
+ return RedirectResponse("/v1", 302)
+
+ @self.app.get("/v1")
+ async def read_root_v1():
+ return HTMLResponse('g4f API: Go to '
+ 'models, '
+ 'chat/completions, or '
+ 'images/generate
'
+ 'Open Swagger UI at: '
+ '/docs')
+
+ @self.app.get("/v1/models", responses={
+ HTTP_200_OK: {"model": List[ModelResponseModel]},
+ })
+ async def models():
+ model_list = dict(
+ (model, g4f.models.ModelUtils.convert[model])
+ for model in g4f.Model.__all__()
+ )
+ return [{
+ 'id': model_id,
+ 'object': 'model',
+ 'created': 0,
+ 'owned_by': model.base_provider
+ } for model_id, model in model_list.items()]
+
+ @self.app.get("/v1/models/{model_name}", responses={
+ HTTP_200_OK: {"model": ModelResponseModel},
+ HTTP_404_NOT_FOUND: {"model": ErrorResponseModel},
+ })
+ async def model_info(model_name: str) -> ModelResponseModel:
+ if model_name in g4f.models.ModelUtils.convert:
+ model_info = g4f.models.ModelUtils.convert[model_name]
+ return JSONResponse({
+ 'id': model_name,
+ 'object': 'model',
+ 'created': 0,
+ 'owned_by': model_info.base_provider
+ })
+ return ErrorResponse.from_message("The model does not exist.", HTTP_404_NOT_FOUND)
+
+ @self.app.post("/v1/chat/completions", responses={
+ HTTP_200_OK: {"model": ChatCompletion},
+ HTTP_401_UNAUTHORIZED: {"model": ErrorResponseModel},
+ HTTP_404_NOT_FOUND: {"model": ErrorResponseModel},
+ HTTP_422_UNPROCESSABLE_ENTITY: {"model": ErrorResponseModel},
+ HTTP_500_INTERNAL_SERVER_ERROR: {"model": ErrorResponseModel},
+ })
+ async def chat_completions(
+ config: ChatCompletionsConfig,
+ credentials: Annotated[HTTPAuthorizationCredentials, Depends(Api.security)] = None,
+ provider: str = None
+ ):
+ try:
+ config.provider = provider if config.provider is None else config.provider
+ if config.provider is None:
+ config.provider = AppConfig.provider
+ if credentials is not None:
+ config.api_key = credentials.credentials
+
+ conversation = return_conversation = None
+ if config.conversation_id is not None and config.provider is not None:
+ return_conversation = True
+ if config.conversation_id in self.conversations:
+ if config.provider in self.conversations[config.conversation_id]:
+ conversation = self.conversations[config.conversation_id][config.provider]
+
+ if config.image is not None:
+ try:
+ is_data_uri_an_image(config.image)
+ except ValueError as e:
+ return ErrorResponse.from_message(f"The image you send must be a data URI. Example: data:image/webp;base64,...", status_code=HTTP_422_UNPROCESSABLE_ENTITY)
+
+ # Create the completion response
+ response = self.client.chat.completions.create(
+ **filter_none(
+ **{
+ "model": AppConfig.model,
+ "provider": AppConfig.provider,
+ "proxy": AppConfig.proxy,
+ **config.model_dump(exclude_none=True),
+ **{
+ "conversation_id": None,
+ "return_conversation": return_conversation,
+ "conversation": conversation
+ }
+ },
+ ignored=AppConfig.ignored_providers
+ ),
+ )
+
+ if not config.stream:
+ return await response
+
+ async def streaming():
+ try:
+ async for chunk in response:
+ if isinstance(chunk, BaseConversation):
+ if config.conversation_id is not None and config.provider is not None:
+ if config.conversation_id not in self.conversations:
+ self.conversations[config.conversation_id] = {}
+ self.conversations[config.conversation_id][config.provider] = chunk
+ else:
+ yield f"data: {chunk.json()}\n\n"
+ except GeneratorExit:
+ pass
+ except Exception as e:
+ logger.exception(e)
+ yield f'data: {format_exception(e, config)}\n\n'
+ yield "data: [DONE]\n\n"
+
+ return StreamingResponse(streaming(), media_type="text/event-stream")
+
+ except (ModelNotFoundError, ProviderNotFoundError) as e:
+ logger.exception(e)
+ return ErrorResponse.from_exception(e, config, HTTP_404_NOT_FOUND)
+ except MissingAuthError as e:
+ logger.exception(e)
+ return ErrorResponse.from_exception(e, config, HTTP_401_UNAUTHORIZED)
+ except Exception as e:
+ logger.exception(e)
+ return ErrorResponse.from_exception(e, config, HTTP_500_INTERNAL_SERVER_ERROR)
+
+ responses = {
+ HTTP_200_OK: {"model": ImagesResponse},
+ HTTP_401_UNAUTHORIZED: {"model": ErrorResponseModel},
+ HTTP_404_NOT_FOUND: {"model": ErrorResponseModel},
+ HTTP_500_INTERNAL_SERVER_ERROR: {"model": ErrorResponseModel},
+ }
+
+ @self.app.post("/v1/images/generate", responses=responses)
+ @self.app.post("/v1/images/generations", responses=responses)
+ async def generate_image(
+ request: Request,
+ config: ImageGenerationConfig,
+ credentials: Annotated[HTTPAuthorizationCredentials, Depends(Api.security)] = None
+ ):
+ if credentials is not None:
+ config.api_key = credentials.credentials
+ try:
+ response = await self.client.images.generate(
+ prompt=config.prompt,
+ model=config.model,
+ provider=AppConfig.image_provider if config.provider is None else config.provider,
+ **filter_none(
+ response_format = config.response_format,
+ api_key = config.api_key,
+ proxy = config.proxy
+ )
+ )
+ for image in response.data:
+ if hasattr(image, "url") and image.url.startswith("/"):
+ image.url = f"{request.base_url}{image.url.lstrip('/')}"
+ return response
+ except (ModelNotFoundError, ProviderNotFoundError) as e:
+ logger.exception(e)
+ return ErrorResponse.from_exception(e, config, HTTP_404_NOT_FOUND)
+ except MissingAuthError as e:
+ logger.exception(e)
+ return ErrorResponse.from_exception(e, config, HTTP_401_UNAUTHORIZED)
+ except Exception as e:
+ logger.exception(e)
+ return ErrorResponse.from_exception(e, config, HTTP_500_INTERNAL_SERVER_ERROR)
+
+ @self.app.get("/v1/providers", responses={
+ HTTP_200_OK: {"model": List[ProviderResponseModel]},
+ })
+ async def providers():
+ return [{
+ 'id': provider.__name__,
+ 'object': 'provider',
+ 'created': 0,
+ 'url': provider.url,
+ 'label': getattr(provider, "label", None),
+ } for provider in __providers__ if provider.working]
+
+ @self.app.get("/v1/providers/{provider}", responses={
+ HTTP_200_OK: {"model": ProviderResponseDetailModel},
+ HTTP_404_NOT_FOUND: {"model": ErrorResponseModel},
+ })
+ async def providers_info(provider: str):
+ if provider not in ProviderUtils.convert:
+ return ErrorResponse.from_message("The provider does not exist.", 404)
+ provider: ProviderType = ProviderUtils.convert[provider]
+ def safe_get_models(provider: ProviderType) -> list[str]:
+ try:
+ return provider.get_models() if hasattr(provider, "get_models") else []
+ except:
+ return []
+ return {
+ 'id': provider.__name__,
+ 'object': 'provider',
+ 'created': 0,
+ 'url': provider.url,
+ 'label': getattr(provider, "label", None),
+ 'models': safe_get_models(provider),
+ 'image_models': getattr(provider, "image_models", []) or [],
+ 'vision_models': [model for model in [getattr(provider, "default_vision_model", None)] if model],
+ 'params': [*provider.get_parameters()] if hasattr(provider, "get_parameters") else []
+ }
+
+ @self.app.post("/v1/upload_cookies", responses={
+ HTTP_200_OK: {"model": List[FileResponseModel]},
+ })
+ def upload_cookies(files: List[UploadFile]):
+ response_data = []
+ for file in files:
+ try:
+ if file and file.filename.endswith(".json") or file.filename.endswith(".har"):
+ filename = os.path.basename(file.filename)
+ with open(os.path.join(get_cookies_dir(), filename), 'wb') as f:
+ shutil.copyfileobj(file.file, f)
+ response_data.append({"filename": filename})
+ finally:
+ file.file.close()
+ return response_data
+
+ @self.app.get("/v1/synthesize/{provider}", responses={
+ HTTP_200_OK: {"content": {"audio/*": {}}},
+ HTTP_404_NOT_FOUND: {"model": ErrorResponseModel},
+ HTTP_422_UNPROCESSABLE_ENTITY: {"model": ErrorResponseModel},
+ })
+ async def synthesize(request: Request, provider: str):
+ try:
+ provider_handler = convert_to_provider(provider)
+ except ProviderNotFoundError as e:
+ return ErrorResponse.from_exception(e, status_code=HTTP_404_NOT_FOUND)
+ if not hasattr(provider_handler, "synthesize"):
+ return ErrorResponse.from_message("Provider doesn't support synthesize", HTTP_404_NOT_FOUND)
+ if len(request.query_params) == 0:
+ return ErrorResponse.from_message("Missing query params", HTTP_422_UNPROCESSABLE_ENTITY)
+ response_data = provider_handler.synthesize({**request.query_params})
+ content_type = getattr(provider_handler, "synthesize_content_type", "application/octet-stream")
+ return StreamingResponse(response_data, media_type=content_type)
+
+ @self.app.get("/images/{filename}", response_class=FileResponse, responses={
+ HTTP_200_OK: {"content": {"image/*": {}}},
+ HTTP_404_NOT_FOUND: {}
+ })
+ async def get_image(filename):
+ target = os.path.join(images_dir, filename)
+
+ if not os.path.isfile(target):
+ return Response(status_code=404)
+
+ with open(target, "rb") as f:
+ content_type = is_accepted_format(f.read(12))
+
+ return FileResponse(target, media_type=content_type)
+
+def format_exception(e: Union[Exception, str], config: Union[ChatCompletionsConfig, ImageGenerationConfig] = None, image: bool = False) -> str:
+ last_provider = {} if not image else g4f.get_last_provider(True)
+ provider = (AppConfig.image_provider if image else AppConfig.provider)
+ model = AppConfig.model
+ if config is not None:
+ if config.provider is not None:
+ provider = config.provider
+ if config.model is not None:
+ model = config.model
+ if isinstance(e, str):
+ message = e
+ else:
+ message = f"{e.__class__.__name__}: {e}"
+ return json.dumps({
+ "error": {"message": message},
+ "model": last_provider.get("model") if model is None else model,
+ **filter_none(
+ provider=last_provider.get("name") if provider is None else provider
+ )
+ })
+
+def run_api(
+ host: str = '0.0.0.0',
+ port: int = None,
+ bind: str = None,
+ debug: bool = False,
+ workers: int = None,
+ use_colors: bool = None,
+ reload: bool = False
+) -> None:
+ print(f'Starting server... [g4f v-{g4f.version.utils.current_version}]' + (" (debug)" if debug else ""))
+ if use_colors is None:
+ use_colors = debug
+ if bind is not None:
+ host, port = bind.split(":")
+ if port is None:
+ port = DEFAULT_PORT
+ uvicorn.run(
+ f"g4f.api:create_app{'_debug' if debug else ''}",
+ host=host,
+ port=int(port),
+ workers=workers,
+ use_colors=use_colors,
+ factory=True,
+ reload=reload
+ )
diff --git a/g4f/api/_logging.py b/g4f/api/_logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..884d75295fe996a3b4034ea88d005eb6b61c4d24
--- /dev/null
+++ b/g4f/api/_logging.py
@@ -0,0 +1,32 @@
+import sys,logging
+
+#from loguru import logger
+
+def __exception_handle(e_type, e_value, e_traceback):
+ if issubclass(e_type, KeyboardInterrupt):
+ print('\nBye...')
+ sys.exit(0)
+
+ sys.__excepthook__(e_type, e_value, e_traceback)
+
+#class __InterceptHandler(logging.Handler):
+# def emit(self, record):
+# try:
+# level = logger.level(record.levelname).name
+# except ValueError:
+# level = record.levelno
+#
+# frame, depth = logging.currentframe(), 2
+# while frame.f_code.co_filename == logging.__file__:
+# frame = frame.f_back
+# depth += 1
+
+# logger.opt(depth=depth, exception=record.exc_info).log(
+# level, record.getMessage()
+# )
+
+def hook_except_handle():
+ sys.excepthook = __exception_handle
+
+#def hook_logging(**kwargs):
+# logging.basicConfig(handlers=[__InterceptHandler()], **kwargs)
diff --git a/g4f/api/_tokenizer.py b/g4f/api/_tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..de5877c47ad198c9db58c29c40389af1457c4f81
--- /dev/null
+++ b/g4f/api/_tokenizer.py
@@ -0,0 +1,9 @@
+# import tiktoken
+# from typing import Union
+
+# def tokenize(text: str, model: str = 'gpt-3.5-turbo') -> Union[int, str]:
+# encoding = tiktoken.encoding_for_model(model)
+# encoded = encoding.encode(text)
+# num_tokens = len(encoded)
+
+# return num_tokens, encoded
\ No newline at end of file
diff --git a/g4f/api/run.py b/g4f/api/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc1cbf92eb91886a23d9af180ba3dad6456118df
--- /dev/null
+++ b/g4f/api/run.py
@@ -0,0 +1,4 @@
+import g4f.api
+
+if __name__ == "__main__":
+ g4f.api.run_api(debug=True)
diff --git a/g4f/cli.py b/g4f/cli.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8a038a2ce2ea773085a3929a82391aa6d3e5dad
--- /dev/null
+++ b/g4f/cli.py
@@ -0,0 +1,67 @@
+from __future__ import annotations
+
+import argparse
+
+from g4f import Provider
+from g4f.gui.run import gui_parser, run_gui_args
+import g4f.cookies
+
+def main():
+ parser = argparse.ArgumentParser(description="Run gpt4free")
+ subparsers = parser.add_subparsers(dest="mode", help="Mode to run the g4f in.")
+ api_parser = subparsers.add_parser("api")
+ api_parser.add_argument("--bind", default=None, help="The bind string. (Default: 0.0.0.0:1337)")
+ api_parser.add_argument("--port", default=None, help="Change the port of the server.")
+ api_parser.add_argument("--debug", "-d", action="store_true", help="Enable verbose logging.")
+ api_parser.add_argument("--gui", "-g", default=False, action="store_true", help="Add gui to the api.")
+ api_parser.add_argument("--model", default=None, help="Default model for chat completion. (incompatible with --reload and --workers)")
+ api_parser.add_argument("--provider", choices=[provider.__name__ for provider in Provider.__providers__ if provider.working],
+ default=None, help="Default provider for chat completion. (incompatible with --reload and --workers)")
+ api_parser.add_argument("--image-provider", choices=[provider.__name__ for provider in Provider.__providers__ if provider.working and hasattr(provider, "image_models")],
+ default=None, help="Default provider for image generation. (incompatible with --reload and --workers)"),
+ api_parser.add_argument("--proxy", default=None, help="Default used proxy. (incompatible with --reload and --workers)")
+ api_parser.add_argument("--workers", type=int, default=None, help="Number of workers.")
+ api_parser.add_argument("--disable-colors", action="store_true", help="Don't use colors.")
+ api_parser.add_argument("--ignore-cookie-files", action="store_true", help="Don't read .har and cookie files. (incompatible with --reload and --workers)")
+ api_parser.add_argument("--g4f-api-key", type=str, default=None, help="Sets an authentication key for your API. (incompatible with --reload and --workers)")
+ api_parser.add_argument("--ignored-providers", nargs="+", choices=[provider.__name__ for provider in Provider.__providers__ if provider.working],
+ default=[], help="List of providers to ignore when processing request. (incompatible with --reload and --workers)")
+ api_parser.add_argument("--cookie-browsers", nargs="+", choices=[browser.__name__ for browser in g4f.cookies.browsers],
+ default=[], help="List of browsers to access or retrieve cookies from. (incompatible with --reload and --workers)")
+ api_parser.add_argument("--reload", action="store_true", help="Enable reloading.")
+ subparsers.add_parser("gui", parents=[gui_parser()], add_help=False)
+
+ args = parser.parse_args()
+ if args.mode == "api":
+ run_api_args(args)
+ elif args.mode == "gui":
+ run_gui_args(args)
+ else:
+ parser.print_help()
+ exit(1)
+
+def run_api_args(args):
+ from g4f.api import AppConfig, run_api
+
+ AppConfig.set_config(
+ ignore_cookie_files=args.ignore_cookie_files,
+ ignored_providers=args.ignored_providers,
+ g4f_api_key=args.g4f_api_key,
+ provider=args.provider,
+ image_provider=args.image_provider,
+ proxy=args.proxy,
+ model=args.model,
+ gui=args.gui,
+ )
+ g4f.cookies.browsers = [g4f.cookies[browser] for browser in args.cookie_browsers]
+ run_api(
+ bind=args.bind,
+ port=args.port,
+ debug=args.debug,
+ workers=args.workers,
+ use_colors=not args.disable_colors,
+ reload=args.reload
+ )
+
+if __name__ == "__main__":
+ main()
diff --git a/g4f/client/__init__.py b/g4f/client/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..86a810493b932ccd96a1e1859ae51a7258c816dc
--- /dev/null
+++ b/g4f/client/__init__.py
@@ -0,0 +1,508 @@
+from __future__ import annotations
+
+import os
+import time
+import random
+import string
+import asyncio
+import base64
+from typing import Union, AsyncIterator, Iterator, Coroutine, Optional
+
+from ..providers.base_provider import AsyncGeneratorProvider
+from ..image import ImageResponse, copy_images, images_dir
+from ..typing import Messages, Image, ImageType
+from ..providers.types import ProviderType
+from ..providers.response import ResponseType, FinishReason, BaseConversation, SynthesizeData
+from ..errors import NoImageResponseError, ModelNotFoundError
+from ..providers.retry_provider import IterListProvider
+from ..providers.asyncio import get_running_loop, to_sync_generator, async_generator_to_list
+from ..Provider.needs_auth import BingCreateImages, OpenaiAccount
+from .stubs import ChatCompletion, ChatCompletionChunk, Image, ImagesResponse
+from .image_models import ImageModels
+from .types import IterResponse, ImageProvider, Client as BaseClient
+from .service import get_model_and_provider, get_last_provider, convert_to_provider
+from .helper import find_stop, filter_json, filter_none, safe_aclose, to_async_iterator
+
+ChatCompletionResponseType = Iterator[Union[ChatCompletion, ChatCompletionChunk, BaseConversation]]
+AsyncChatCompletionResponseType = AsyncIterator[Union[ChatCompletion, ChatCompletionChunk, BaseConversation]]
+
+try:
+ anext # Python 3.8+
+except NameError:
+ async def anext(aiter):
+ try:
+ return await aiter.__anext__()
+ except StopAsyncIteration:
+ raise StopIteration
+
+# Synchronous iter_response function
+def iter_response(
+ response: Union[Iterator[Union[str, ResponseType]]],
+ stream: bool,
+ response_format: Optional[dict] = None,
+ max_tokens: Optional[int] = None,
+ stop: Optional[list[str]] = None
+) -> ChatCompletionResponseType:
+ content = ""
+ finish_reason = None
+ completion_id = ''.join(random.choices(string.ascii_letters + string.digits, k=28))
+ idx = 0
+
+ if hasattr(response, '__aiter__'):
+ response = to_sync_generator(response)
+
+ for chunk in response:
+ if isinstance(chunk, FinishReason):
+ finish_reason = chunk.reason
+ break
+ elif isinstance(chunk, BaseConversation):
+ yield chunk
+ continue
+ elif isinstance(chunk, SynthesizeData):
+ continue
+
+ chunk = str(chunk)
+ content += chunk
+
+ if max_tokens is not None and idx + 1 >= max_tokens:
+ finish_reason = "length"
+
+ first, content, chunk = find_stop(stop, content, chunk if stream else None)
+
+ if first != -1:
+ finish_reason = "stop"
+
+ if stream:
+ yield ChatCompletionChunk.model_construct(chunk, None, completion_id, int(time.time()))
+
+ if finish_reason is not None:
+ break
+
+ idx += 1
+
+ finish_reason = "stop" if finish_reason is None else finish_reason
+
+ if stream:
+ yield ChatCompletionChunk.model_construct(None, finish_reason, completion_id, int(time.time()))
+ else:
+ if response_format is not None and "type" in response_format:
+ if response_format["type"] == "json_object":
+ content = filter_json(content)
+ yield ChatCompletion.model_construct(content, finish_reason, completion_id, int(time.time()))
+
+# Synchronous iter_append_model_and_provider function
+def iter_append_model_and_provider(response: ChatCompletionResponseType) -> ChatCompletionResponseType:
+ last_provider = None
+
+ for chunk in response:
+ if isinstance(chunk, (ChatCompletion, ChatCompletionChunk)):
+ last_provider = get_last_provider(True) if last_provider is None else last_provider
+ chunk.model = last_provider.get("model")
+ chunk.provider = last_provider.get("name")
+ yield chunk
+
+async def async_iter_response(
+ response: AsyncIterator[Union[str, ResponseType]],
+ stream: bool,
+ response_format: Optional[dict] = None,
+ max_tokens: Optional[int] = None,
+ stop: Optional[list[str]] = None
+) -> AsyncChatCompletionResponseType:
+ content = ""
+ finish_reason = None
+ completion_id = ''.join(random.choices(string.ascii_letters + string.digits, k=28))
+ idx = 0
+
+ try:
+ async for chunk in response:
+ if isinstance(chunk, FinishReason):
+ finish_reason = chunk.reason
+ break
+ elif isinstance(chunk, BaseConversation):
+ yield chunk
+ continue
+ elif isinstance(chunk, SynthesizeData):
+ continue
+
+ chunk = str(chunk)
+ content += chunk
+ idx += 1
+
+ if max_tokens is not None and idx >= max_tokens:
+ finish_reason = "length"
+
+ first, content, chunk = find_stop(stop, content, chunk if stream else None)
+
+ if first != -1:
+ finish_reason = "stop"
+
+ if stream:
+ yield ChatCompletionChunk.model_construct(chunk, None, completion_id, int(time.time()))
+
+ if finish_reason is not None:
+ break
+
+ finish_reason = "stop" if finish_reason is None else finish_reason
+
+ if stream:
+ yield ChatCompletionChunk.model_construct(None, finish_reason, completion_id, int(time.time()))
+ else:
+ if response_format is not None and "type" in response_format:
+ if response_format["type"] == "json_object":
+ content = filter_json(content)
+ yield ChatCompletion.model_construct(content, finish_reason, completion_id, int(time.time()))
+ finally:
+ await safe_aclose(response)
+
+async def async_iter_append_model_and_provider(
+ response: AsyncChatCompletionResponseType
+ ) -> AsyncChatCompletionResponseType:
+ last_provider = None
+ try:
+ async for chunk in response:
+ if isinstance(chunk, (ChatCompletion, ChatCompletionChunk)):
+ last_provider = get_last_provider(True) if last_provider is None else last_provider
+ chunk.model = last_provider.get("model")
+ chunk.provider = last_provider.get("name")
+ yield chunk
+ finally:
+ await safe_aclose(response)
+
+class Client(BaseClient):
+ def __init__(
+ self,
+ provider: Optional[ProviderType] = None,
+ image_provider: Optional[ImageProvider] = None,
+ **kwargs
+ ) -> None:
+ super().__init__(**kwargs)
+ self.chat: Chat = Chat(self, provider)
+ self.images: Images = Images(self, image_provider)
+
+class Completions:
+ def __init__(self, client: Client, provider: Optional[ProviderType] = None):
+ self.client: Client = client
+ self.provider: ProviderType = provider
+
+ def create(
+ self,
+ messages: Messages,
+ model: str,
+ provider: Optional[ProviderType] = None,
+ stream: Optional[bool] = False,
+ proxy: Optional[str] = None,
+ response_format: Optional[dict] = None,
+ max_tokens: Optional[int] = None,
+ stop: Optional[Union[list[str], str]] = None,
+ api_key: Optional[str] = None,
+ ignored: Optional[list[str]] = None,
+ ignore_working: Optional[bool] = False,
+ ignore_stream: Optional[bool] = False,
+ **kwargs
+ ) -> IterResponse:
+ model, provider = get_model_and_provider(
+ model,
+ self.provider if provider is None else provider,
+ stream,
+ ignored,
+ ignore_working,
+ ignore_stream,
+ )
+ stop = [stop] if isinstance(stop, str) else stop
+
+ response = provider.create_completion(
+ model,
+ messages,
+ stream=stream,
+ **filter_none(
+ proxy=self.client.proxy if proxy is None else proxy,
+ max_tokens=max_tokens,
+ stop=stop,
+ api_key=self.client.api_key if api_key is None else api_key
+ ),
+ **kwargs
+ )
+ if asyncio.iscoroutinefunction(provider.create_completion):
+ # Run the asynchronous function in an event loop
+ response = asyncio.run(response)
+ if stream and hasattr(response, '__aiter__'):
+ # It's an async generator, wrap it into a sync iterator
+ response = to_sync_generator(response)
+ elif hasattr(response, '__aiter__'):
+ # If response is an async generator, collect it into a list
+ response = asyncio.run(async_generator_to_list(response))
+ response = iter_response(response, stream, response_format, max_tokens, stop)
+ response = iter_append_model_and_provider(response)
+ if stream:
+ return response
+ else:
+ return next(response)
+
+class Chat:
+ completions: Completions
+
+ def __init__(self, client: Client, provider: Optional[ProviderType] = None):
+ self.completions = Completions(client, provider)
+
+class Images:
+ def __init__(self, client: Client, provider: Optional[ProviderType] = None):
+ self.client: Client = client
+ self.provider: Optional[ProviderType] = provider
+ self.models: ImageModels = ImageModels(client)
+
+ def generate(
+ self,
+ prompt: str,
+ model: str = None,
+ provider: Optional[ProviderType] = None,
+ response_format: str = "url",
+ proxy: Optional[str] = None,
+ **kwargs
+ ) -> ImagesResponse:
+ """
+ Synchronous generate method that runs the async_generate method in an event loop.
+ """
+ return asyncio.run(self.async_generate(prompt, model, provider, response_format, proxy, **kwargs))
+
+ async def get_provider_handler(self, model: Optional[str], provider: Optional[ImageProvider], default: ImageProvider) -> ImageProvider:
+ if provider is None:
+ provider_handler = self.provider
+ if provider_handler is None:
+ provider_handler = self.models.get(model, default)
+ elif isinstance(provider, str):
+ provider_handler = convert_to_provider(provider)
+ else:
+ provider_handler = provider
+ if provider_handler is None:
+ return default
+ if isinstance(provider_handler, IterListProvider):
+ if provider_handler.providers:
+ provider_handler = provider_handler.providers[0]
+ else:
+ raise ModelNotFoundError(f"IterListProvider for model {model} has no providers")
+ return provider_handler
+
+ async def async_generate(
+ self,
+ prompt: str,
+ model: Optional[str] = None,
+ provider: Optional[ProviderType] = None,
+ response_format: Optional[str] = "url",
+ proxy: Optional[str] = None,
+ **kwargs
+ ) -> ImagesResponse:
+ provider_handler = await self.get_provider_handler(model, provider, BingCreateImages)
+ if proxy is None:
+ proxy = self.client.proxy
+
+ response = None
+ if hasattr(provider_handler, "create_async_generator"):
+ messages = [{"role": "user", "content": f"Generate a image: {prompt}"}]
+ async for item in provider_handler.create_async_generator(model, messages, prompt=prompt, **kwargs):
+ if isinstance(item, ImageResponse):
+ response = item
+ break
+ elif hasattr(provider_handler, 'create'):
+ if asyncio.iscoroutinefunction(provider_handler.create):
+ response = await provider_handler.create(prompt)
+ else:
+ response = provider_handler.create(prompt)
+ if isinstance(response, str):
+ response = ImageResponse([response], prompt)
+ elif hasattr(provider_handler, "create_completion"):
+ get_running_loop(check_nested=True)
+ messages = [{"role": "user", "content": f"Generate a image: {prompt}"}]
+ for item in provider_handler.create_completion(model, messages, prompt=prompt, **kwargs):
+ if isinstance(item, ImageResponse):
+ response = item
+ break
+ else:
+ raise ValueError(f"Provider {getattr(provider_handler, '__name__')} does not support image generation")
+ if isinstance(response, ImageResponse):
+ return await self._process_image_response(
+ response,
+ response_format,
+ proxy,
+ model,
+ getattr(provider_handler, "__name__", None)
+ )
+ if response is None:
+ raise NoImageResponseError(f"No image response from {getattr(provider_handler, '__name__')}")
+ raise NoImageResponseError(f"Unexpected response type: {type(response)}")
+
+ def create_variation(
+ self,
+ image: Union[str, bytes],
+ model: str = None,
+ provider: Optional[ProviderType] = None,
+ response_format: str = "url",
+ **kwargs
+ ) -> ImagesResponse:
+ return asyncio.run(self.async_create_variation(
+ image, model, provider, response_format, **kwargs
+ ))
+
+ async def async_create_variation(
+ self,
+ image: ImageType,
+ model: Optional[str] = None,
+ provider: Optional[ProviderType] = None,
+ response_format: str = "url",
+ proxy: Optional[str] = None,
+ **kwargs
+ ) -> ImagesResponse:
+ provider_handler = await self.get_provider_handler(model, provider, OpenaiAccount)
+ if proxy is None:
+ proxy = self.client.proxy
+
+ if hasattr(provider_handler, "create_async_generator"):
+ messages = [{"role": "user", "content": "create a variation of this image"}]
+ generator = None
+ try:
+ generator = provider_handler.create_async_generator(model, messages, image=image, response_format=response_format, proxy=proxy, **kwargs)
+ async for chunk in generator:
+ if isinstance(chunk, ImageResponse):
+ response = chunk
+ break
+ finally:
+ await safe_aclose(generator)
+ elif hasattr(provider_handler, 'create_variation'):
+ if asyncio.iscoroutinefunction(provider.provider_handler):
+ response = await provider_handler.create_variation(image, model=model, response_format=response_format, proxy=proxy, **kwargs)
+ else:
+ response = provider_handler.create_variation(image, model=model, response_format=response_format, proxy=proxy, **kwargs)
+ else:
+ raise NoImageResponseError(f"Provider {provider} does not support image variation")
+
+ if isinstance(response, str):
+ response = ImageResponse([response])
+ if isinstance(response, ImageResponse):
+ return self._process_image_response(response, response_format, proxy, model, getattr(provider, "__name__", None))
+ if response is None:
+ raise NoImageResponseError(f"No image response from {getattr(provider, '__name__')}")
+ raise NoImageResponseError(f"Unexpected response type: {type(response)}")
+
+ async def _process_image_response(
+ self,
+ response: ImageResponse,
+ response_format: str,
+ proxy: str = None,
+ model: Optional[str] = None,
+ provider: Optional[str] = None
+ ) -> list[Image]:
+ if response_format in ("url", "b64_json"):
+ images = await copy_images(response.get_list(), response.options.get("cookies"), proxy)
+ async def process_image_item(image_file: str) -> Image:
+ if response_format == "b64_json":
+ with open(os.path.join(images_dir, os.path.basename(image_file)), "rb") as file:
+ image_data = base64.b64encode(file.read()).decode()
+ return Image.model_construct(url=image_file, b64_json=image_data, revised_prompt=response.alt)
+ return Image.model_construct(url=image_file, revised_prompt=response.alt)
+ images = await asyncio.gather(*[process_image_item(image) for image in images])
+ else:
+ images = [Image.model_construct(url=image, revised_prompt=response.alt) for image in response.get_list()]
+ last_provider = get_last_provider(True)
+ return ImagesResponse.model_construct(
+ images,
+ model=last_provider.get("model") if model is None else model,
+ provider=last_provider.get("name") if provider is None else provider
+ )
+
+class AsyncClient(BaseClient):
+ def __init__(
+ self,
+ provider: Optional[ProviderType] = None,
+ image_provider: Optional[ImageProvider] = None,
+ **kwargs
+ ) -> None:
+ super().__init__(**kwargs)
+ self.chat: AsyncChat = AsyncChat(self, provider)
+ self.images: AsyncImages = AsyncImages(self, image_provider)
+
+class AsyncChat:
+ completions: AsyncCompletions
+
+ def __init__(self, client: AsyncClient, provider: Optional[ProviderType] = None):
+ self.completions = AsyncCompletions(client, provider)
+
+class AsyncCompletions:
+ def __init__(self, client: AsyncClient, provider: Optional[ProviderType] = None):
+ self.client: AsyncClient = client
+ self.provider: ProviderType = provider
+
+ def create(
+ self,
+ messages: Messages,
+ model: str,
+ provider: Optional[ProviderType] = None,
+ stream: Optional[bool] = False,
+ proxy: Optional[str] = None,
+ response_format: Optional[dict] = None,
+ max_tokens: Optional[int] = None,
+ stop: Optional[Union[list[str], str]] = None,
+ api_key: Optional[str] = None,
+ ignored: Optional[list[str]] = None,
+ ignore_working: Optional[bool] = False,
+ ignore_stream: Optional[bool] = False,
+ **kwargs
+ ) -> Union[Coroutine[ChatCompletion], AsyncIterator[ChatCompletionChunk, BaseConversation]]:
+ model, provider = get_model_and_provider(
+ model,
+ self.provider if provider is None else provider,
+ stream,
+ ignored,
+ ignore_working,
+ ignore_stream,
+ )
+ stop = [stop] if isinstance(stop, str) else stop
+
+ if hasattr(provider, "create_async_generator"):
+ create_handler = provider.create_async_generator
+ else:
+ create_handler = provider.create_completion
+ response = create_handler(
+ model,
+ messages,
+ stream=stream,
+ **filter_none(
+ proxy=self.client.proxy if proxy is None else proxy,
+ max_tokens=max_tokens,
+ stop=stop,
+ api_key=self.client.api_key if api_key is None else api_key
+ ),
+ **kwargs
+ )
+
+ if not isinstance(response, AsyncIterator):
+ response = to_async_iterator(response)
+ response = async_iter_response(response, stream, response_format, max_tokens, stop)
+ response = async_iter_append_model_and_provider(response)
+ return response if stream else anext(response)
+
+class AsyncImages(Images):
+ def __init__(self, client: AsyncClient, provider: Optional[ProviderType] = None):
+ self.client: AsyncClient = client
+ self.provider: Optional[ProviderType] = provider
+ self.models: ImageModels = ImageModels(client)
+
+ async def generate(
+ self,
+ prompt: str,
+ model: Optional[str] = None,
+ provider: Optional[ProviderType] = None,
+ response_format: str = "url",
+ **kwargs
+ ) -> ImagesResponse:
+ return await self.async_generate(prompt, model, provider, response_format, **kwargs)
+
+ async def create_variation(
+ self,
+ image: ImageType,
+ model: str = None,
+ provider: ProviderType = None,
+ response_format: str = "url",
+ **kwargs
+ ) -> ImagesResponse:
+ return await self.async_create_variation(
+ image, model, provider, response_format, **kwargs
+ )
\ No newline at end of file
diff --git a/g4f/client/helper.py b/g4f/client/helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b022e8c8a8a5f22c0a20f02dd943004e70c1e46
--- /dev/null
+++ b/g4f/client/helper.py
@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+import re
+import logging
+
+from typing import AsyncIterator, Iterator, AsyncGenerator, Optional
+
+def filter_json(text: str) -> str:
+ """
+ Parses JSON code block from a string.
+
+ Args:
+ text (str): A string containing a JSON code block.
+
+ Returns:
+ dict: A dictionary parsed from the JSON code block.
+ """
+ match = re.search(r"```(json|)\n(?P[\S\s]+?)\n```", text)
+ if match:
+ return match.group("code")
+ return text
+
+def find_stop(stop: Optional[list[str]], content: str, chunk: str = None):
+ first = -1
+ word = None
+ if stop is not None:
+ for word in list(stop):
+ first = content.find(word)
+ if first != -1:
+ content = content[:first]
+ break
+ if chunk is not None and first != -1:
+ first = chunk.find(word)
+ if first != -1:
+ chunk = chunk[:first]
+ else:
+ first = 0
+ return first, content, chunk
+
+def filter_none(**kwargs) -> dict:
+ return {
+ key: value
+ for key, value in kwargs.items()
+ if value is not None
+ }
+
+async def safe_aclose(generator: AsyncGenerator) -> None:
+ try:
+ if generator and hasattr(generator, 'aclose'):
+ await generator.aclose()
+ except Exception as e:
+ logging.warning(f"Error while closing generator: {e}")
+
+# Helper function to convert a synchronous iterator to an async iterator
+async def to_async_iterator(iterator: Iterator) -> AsyncIterator:
+ for item in iterator:
+ yield item
\ No newline at end of file
diff --git a/g4f/client/image_models.py b/g4f/client/image_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b97a56b959f5d974114d47d3d2a27c6bbdd2ce4
--- /dev/null
+++ b/g4f/client/image_models.py
@@ -0,0 +1,14 @@
+from __future__ import annotations
+
+from ..models import ModelUtils
+
+class ImageModels():
+ def __init__(self, client):
+ self.client = client
+ self.models = ModelUtils.convert
+
+ def get(self, name, default=None):
+ model = self.models.get(name)
+ if model and model.best_provider:
+ return model.best_provider
+ return default
diff --git a/g4f/client/service.py b/g4f/client/service.py
new file mode 100644
index 0000000000000000000000000000000000000000..44533ece9d37a788e407860e5ff8f2c5236a9252
--- /dev/null
+++ b/g4f/client/service.py
@@ -0,0 +1,119 @@
+from __future__ import annotations
+
+from typing import Union
+
+from .. import debug, version
+from ..errors import ProviderNotFoundError, ModelNotFoundError, ProviderNotWorkingError, StreamNotSupportedError
+from ..models import Model, ModelUtils, default
+from ..Provider import ProviderUtils
+from ..providers.types import BaseRetryProvider, ProviderType
+from ..providers.retry_provider import IterProvider
+
+def convert_to_provider(provider: str) -> ProviderType:
+ if " " in provider:
+ provider_list = [ProviderUtils.convert[p] for p in provider.split() if p in ProviderUtils.convert]
+ if not provider_list:
+ raise ProviderNotFoundError(f'Providers not found: {provider}')
+ provider = IterProvider(provider_list)
+ elif provider in ProviderUtils.convert:
+ provider = ProviderUtils.convert[provider]
+ elif provider:
+ raise ProviderNotFoundError(f'Provider not found: {provider}')
+ return provider
+
+def get_model_and_provider(model : Union[Model, str],
+ provider : Union[ProviderType, str, None],
+ stream : bool,
+ ignored : list[str] = None,
+ ignore_working: bool = False,
+ ignore_stream: bool = False) -> tuple[str, ProviderType]:
+ """
+ Retrieves the model and provider based on input parameters.
+
+ Args:
+ model (Union[Model, str]): The model to use, either as an object or a string identifier.
+ provider (Union[ProviderType, str, None]): The provider to use, either as an object, a string identifier, or None.
+ stream (bool): Indicates if the operation should be performed as a stream.
+ ignored (list[str], optional): List of provider names to be ignored.
+ ignore_working (bool, optional): If True, ignores the working status of the provider.
+ ignore_stream (bool, optional): If True, ignores the streaming capability of the provider.
+
+ Returns:
+ tuple[str, ProviderType]: A tuple containing the model name and the provider type.
+
+ Raises:
+ ProviderNotFoundError: If the provider is not found.
+ ModelNotFoundError: If the model is not found.
+ ProviderNotWorkingError: If the provider is not working.
+ StreamNotSupportedError: If streaming is not supported by the provider.
+ """
+ if debug.version_check:
+ debug.version_check = False
+ version.utils.check_version()
+
+ if isinstance(provider, str):
+ provider = convert_to_provider(provider)
+
+ if isinstance(model, str):
+ if model in ModelUtils.convert:
+ model = ModelUtils.convert[model]
+
+ if not provider:
+ if not model:
+ model = default
+ elif isinstance(model, str):
+ raise ModelNotFoundError(f'Model not found: {model}')
+ provider = model.best_provider
+
+ if not provider:
+ raise ProviderNotFoundError(f'No provider found for model: {model}')
+
+ if isinstance(model, Model):
+ model = model.name
+
+ if not ignore_working and not provider.working:
+ raise ProviderNotWorkingError(f'{provider.__name__} is not working')
+
+ if isinstance(provider, BaseRetryProvider):
+ if not ignore_working:
+ provider.providers = [p for p in provider.providers if p.working]
+ if ignored:
+ provider.providers = [p for p in provider.providers if p.__name__ not in ignored]
+
+ if not ignore_stream and not provider.supports_stream and stream:
+ raise StreamNotSupportedError(f'{provider.__name__} does not support "stream" argument')
+
+ if model:
+ debug.log(f'Using {provider.__name__} provider and {model} model')
+ else:
+ debug.log(f'Using {provider.__name__} provider')
+
+ debug.last_provider = provider
+ debug.last_model = model
+
+ return model, provider
+
+def get_last_provider(as_dict: bool = False) -> Union[ProviderType, dict[str, str], None]:
+ """
+ Retrieves the last used provider.
+
+ Args:
+ as_dict (bool, optional): If True, returns the provider information as a dictionary.
+
+ Returns:
+ Union[ProviderType, dict[str, str]]: The last used provider, either as an object or a dictionary.
+ """
+ last = debug.last_provider
+ if isinstance(last, BaseRetryProvider):
+ last = last.last_provider
+ if as_dict:
+ if last:
+ return {
+ "name": last.__name__,
+ "url": last.url,
+ "model": debug.last_model,
+ "label": getattr(last, "label", None) if hasattr(last, "label") else None
+ }
+ else:
+ return {}
+ return last
\ No newline at end of file
diff --git a/g4f/client/stubs.py b/g4f/client/stubs.py
new file mode 100644
index 0000000000000000000000000000000000000000..7367ac75d42dcc91642ee559c679407aa0cf0b6d
--- /dev/null
+++ b/g4f/client/stubs.py
@@ -0,0 +1,150 @@
+from __future__ import annotations
+
+from typing import Optional, List, Dict
+from time import time
+
+from .helper import filter_none
+
+try:
+ from pydantic import BaseModel, Field
+except ImportError:
+ class BaseModel():
+ @classmethod
+ def model_construct(cls, **data):
+ new = cls()
+ for key, value in data.items():
+ setattr(new, key, value)
+ return new
+ class Field():
+ def __init__(self, **config):
+ pass
+
+class ChatCompletionChunk(BaseModel):
+ id: str
+ object: str
+ created: int
+ model: str
+ provider: Optional[str]
+ choices: List[ChatCompletionDeltaChoice]
+
+ @classmethod
+ def model_construct(
+ cls,
+ content: str,
+ finish_reason: str,
+ completion_id: str = None,
+ created: int = None
+ ):
+ return super().model_construct(
+ id=f"chatcmpl-{completion_id}" if completion_id else None,
+ object="chat.completion.cunk",
+ created=created,
+ model=None,
+ provider=None,
+ choices=[ChatCompletionDeltaChoice.model_construct(
+ ChatCompletionDelta.model_construct(content),
+ finish_reason
+ )]
+ )
+
+class ChatCompletionMessage(BaseModel):
+ role: str
+ content: str
+
+ @classmethod
+ def model_construct(cls, content: str):
+ return super().model_construct(role="assistant", content=content)
+
+class ChatCompletionChoice(BaseModel):
+ index: int
+ message: ChatCompletionMessage
+ finish_reason: str
+
+ @classmethod
+ def model_construct(cls, message: ChatCompletionMessage, finish_reason: str):
+ return super().model_construct(index=0, message=message, finish_reason=finish_reason)
+
+class ChatCompletion(BaseModel):
+ id: str
+ object: str
+ created: int
+ model: str
+ provider: Optional[str]
+ choices: List[ChatCompletionChoice]
+ usage: Dict[str, int] = Field(examples=[{
+ "prompt_tokens": 0, #prompt_tokens,
+ "completion_tokens": 0, #completion_tokens,
+ "total_tokens": 0, #prompt_tokens + completion_tokens,
+ }])
+
+ @classmethod
+ def model_construct(
+ cls,
+ content: str,
+ finish_reason: str,
+ completion_id: str = None,
+ created: int = None
+ ):
+ return super().model_construct(
+ id=f"chatcmpl-{completion_id}" if completion_id else None,
+ object="chat.completion",
+ created=created,
+ model=None,
+ provider=None,
+ choices=[ChatCompletionChoice.model_construct(
+ ChatCompletionMessage.model_construct(content),
+ finish_reason
+ )],
+ usage={
+ "prompt_tokens": 0, #prompt_tokens,
+ "completion_tokens": 0, #completion_tokens,
+ "total_tokens": 0, #prompt_tokens + completion_tokens,
+ }
+ )
+
+class ChatCompletionDelta(BaseModel):
+ role: str
+ content: str
+
+ @classmethod
+ def model_construct(cls, content: Optional[str]):
+ return super().model_construct(role="assistant", content=content)
+
+class ChatCompletionDeltaChoice(BaseModel):
+ index: int
+ delta: ChatCompletionDelta
+ finish_reason: Optional[str]
+
+ @classmethod
+ def model_construct(cls, delta: ChatCompletionDelta, finish_reason: Optional[str]):
+ return super().model_construct(index=0, delta=delta, finish_reason=finish_reason)
+
+class Image(BaseModel):
+ url: Optional[str]
+ b64_json: Optional[str]
+ revised_prompt: Optional[str]
+
+ @classmethod
+ def model_construct(cls, url: str = None, b64_json: str = None, revised_prompt: str = None):
+ return super().model_construct(**filter_none(
+ url=url,
+ b64_json=b64_json,
+ revised_prompt=revised_prompt
+ ))
+
+class ImagesResponse(BaseModel):
+ data: List[Image]
+ model: str
+ provider: str
+ created: int
+
+ @classmethod
+ def model_construct(cls, data: List[Image], created: int = None, model: str = None, provider: str = None):
+ if created is None:
+ created = int(time())
+ return super().model_construct(
+ data=data,
+ model=model,
+ provider=provider,
+ created=created
+ )
diff --git a/g4f/client/types.py b/g4f/client/types.py
new file mode 100644
index 0000000000000000000000000000000000000000..5010e098c9666ca89b6f5364c1aea7334be9532b
--- /dev/null
+++ b/g4f/client/types.py
@@ -0,0 +1,33 @@
+from __future__ import annotations
+
+import os
+
+from .stubs import ChatCompletion, ChatCompletionChunk
+from ..providers.types import BaseProvider
+from typing import Union, Iterator, AsyncIterator
+
+ImageProvider = Union[BaseProvider, object]
+Proxies = Union[dict, str]
+IterResponse = Iterator[Union[ChatCompletion, ChatCompletionChunk]]
+AsyncIterResponse = AsyncIterator[Union[ChatCompletion, ChatCompletionChunk]]
+
+class Client():
+ def __init__(
+ self,
+ api_key: str = None,
+ proxies: Proxies = None,
+ **kwargs
+ ) -> None:
+ self.api_key: str = api_key
+ self.proxies= proxies
+ self.proxy: str = self.get_proxy()
+
+ def get_proxy(self) -> Union[str, None]:
+ if isinstance(self.proxies, str):
+ return self.proxies
+ elif self.proxies is None:
+ return os.environ.get("G4F_PROXY")
+ elif "all" in self.proxies:
+ return self.proxies["all"]
+ elif "https" in self.proxies:
+ return self.proxies["https"]
\ No newline at end of file
diff --git a/g4f/cookies.py b/g4f/cookies.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c62d697ecfcb7eeb7649b11f8144e7ae8792f93
--- /dev/null
+++ b/g4f/cookies.py
@@ -0,0 +1,207 @@
+from __future__ import annotations
+
+import os
+import time
+import json
+
+try:
+ from platformdirs import user_config_dir
+ has_platformdirs = True
+except ImportError:
+ has_platformdirs = False
+try:
+ from browser_cookie3 import (
+ chrome, chromium, opera, opera_gx,
+ brave, edge, vivaldi, firefox,
+ _LinuxPasswordManager, BrowserCookieError
+ )
+
+ def _g4f(domain_name: str) -> list:
+ """
+ Load cookies from the 'g4f' browser (if exists).
+
+ Args:
+ domain_name (str): The domain for which to load cookies.
+
+ Returns:
+ list: List of cookies.
+ """
+ if not has_platformdirs:
+ return []
+ user_data_dir = user_config_dir("g4f")
+ cookie_file = os.path.join(user_data_dir, "Default", "Cookies")
+ return [] if not os.path.exists(cookie_file) else chrome(cookie_file, domain_name)
+
+ browsers = [
+ _g4f,
+ chrome, chromium, opera, opera_gx,
+ brave, edge, vivaldi, firefox,
+ ]
+ has_browser_cookie3 = True
+except ImportError:
+ has_browser_cookie3 = False
+ browsers = []
+
+from .typing import Dict, Cookies
+from .errors import MissingRequirementsError
+from . import debug
+
+class CookiesConfig():
+ cookies: Dict[str, Cookies] = {}
+ cookies_dir: str = "./har_and_cookies"
+
+DOMAINS = [
+ ".bing.com",
+ ".meta.ai",
+ ".google.com",
+ "www.whiterabbitneo.com",
+ "huggingface.co",
+ "chat.reka.ai",
+ "chatgpt.com"
+]
+
+if has_browser_cookie3 and os.environ.get('DBUS_SESSION_BUS_ADDRESS') == "/dev/null":
+ _LinuxPasswordManager.get_password = lambda a, b: b"secret"
+
+def get_cookies(domain_name: str = '', raise_requirements_error: bool = True, single_browser: bool = False) -> Dict[str, str]:
+ """
+ Load cookies for a given domain from all supported browsers and cache the results.
+
+ Args:
+ domain_name (str): The domain for which to load cookies.
+
+ Returns:
+ Dict[str, str]: A dictionary of cookie names and values.
+ """
+ if domain_name in CookiesConfig.cookies:
+ return CookiesConfig.cookies[domain_name]
+
+ cookies = load_cookies_from_browsers(domain_name, raise_requirements_error, single_browser)
+ CookiesConfig.cookies[domain_name] = cookies
+ return cookies
+
+def set_cookies(domain_name: str, cookies: Cookies = None) -> None:
+ if cookies:
+ CookiesConfig.cookies[domain_name] = cookies
+ elif domain_name in CookiesConfig.cookies:
+ CookiesConfig.cookies.pop(domain_name)
+
+def load_cookies_from_browsers(domain_name: str, raise_requirements_error: bool = True, single_browser: bool = False) -> Cookies:
+ """
+ Helper function to load cookies from various browsers.
+
+ Args:
+ domain_name (str): The domain for which to load cookies.
+
+ Returns:
+ Dict[str, str]: A dictionary of cookie names and values.
+ """
+ if not has_browser_cookie3:
+ if raise_requirements_error:
+ raise MissingRequirementsError('Install "browser_cookie3" package')
+ return {}
+ cookies = {}
+ for cookie_fn in [_g4f, chrome, chromium, opera, opera_gx, brave, edge, vivaldi, firefox]:
+ try:
+ cookie_jar = cookie_fn(domain_name=domain_name)
+ if len(cookie_jar) and debug.logging:
+ print(f"Read cookies from {cookie_fn.__name__} for {domain_name}")
+ for cookie in cookie_jar:
+ if cookie.name not in cookies:
+ if not cookie.expires or cookie.expires > time.time():
+ cookies[cookie.name] = cookie.value
+ if single_browser and len(cookie_jar):
+ break
+ except BrowserCookieError:
+ pass
+ except Exception as e:
+ if debug.logging:
+ print(f"Error reading cookies from {cookie_fn.__name__} for {domain_name}: {e}")
+ return cookies
+
+def set_cookies_dir(dir: str) -> None:
+ CookiesConfig.cookies_dir = dir
+
+def get_cookies_dir() -> str:
+ return CookiesConfig.cookies_dir
+
+def read_cookie_files(dirPath: str = None):
+ def get_domain(v: dict) -> str:
+ host = [h["value"] for h in v['request']['headers'] if h["name"].lower() in ("host", ":authority")]
+ if not host:
+ return
+ host = host.pop()
+ for d in DOMAINS:
+ if d in host:
+ return d
+
+ harFiles = []
+ cookieFiles = []
+ for root, _, files in os.walk(CookiesConfig.cookies_dir if dirPath is None else dirPath):
+ for file in files:
+ if file.endswith(".har"):
+ harFiles.append(os.path.join(root, file))
+ elif file.endswith(".json"):
+ cookieFiles.append(os.path.join(root, file))
+
+ CookiesConfig.cookies = {}
+ for path in harFiles:
+ with open(path, 'rb') as file:
+ try:
+ harFile = json.load(file)
+ except json.JSONDecodeError:
+ # Error: not a HAR file!
+ continue
+ if debug.logging:
+ print("Read .har file:", path)
+ new_cookies = {}
+ for v in harFile['log']['entries']:
+ domain = get_domain(v)
+ if domain is None:
+ continue
+ v_cookies = {}
+ for c in v['request']['cookies']:
+ v_cookies[c['name']] = c['value']
+ if len(v_cookies) > 0:
+ CookiesConfig.cookies[domain] = v_cookies
+ new_cookies[domain] = len(v_cookies)
+ if debug.logging:
+ for domain, new_values in new_cookies.items():
+ print(f"Cookies added: {new_values} from {domain}")
+ for path in cookieFiles:
+ with open(path, 'rb') as file:
+ try:
+ cookieFile = json.load(file)
+ except json.JSONDecodeError:
+ # Error: not a json file!
+ continue
+ if not isinstance(cookieFile, list):
+ continue
+ if debug.logging:
+ print("Read cookie file:", path)
+ new_cookies = {}
+ for c in cookieFile:
+ if isinstance(c, dict) and "domain" in c:
+ if c["domain"] not in new_cookies:
+ new_cookies[c["domain"]] = {}
+ new_cookies[c["domain"]][c["name"]] = c["value"]
+ for domain, new_values in new_cookies.items():
+ if debug.logging:
+ print(f"Cookies added: {len(new_values)} from {domain}")
+ CookiesConfig.cookies[domain] = new_values
+
+def _g4f(domain_name: str) -> list:
+ """
+ Load cookies from the 'g4f' browser (if exists).
+
+ Args:
+ domain_name (str): The domain for which to load cookies.
+
+ Returns:
+ list: List of cookies.
+ """
+ if not has_platformdirs:
+ return []
+ user_data_dir = user_config_dir("g4f")
+ cookie_file = os.path.join(user_data_dir, "Default", "Cookies")
+ return [] if not os.path.exists(cookie_file) else chrome(cookie_file, domain_name)
diff --git a/g4f/debug.py b/g4f/debug.py
new file mode 100644
index 0000000000000000000000000000000000000000..c107cbdf97941386cda6a47ec70efda4470c1b97
--- /dev/null
+++ b/g4f/debug.py
@@ -0,0 +1,13 @@
+from .providers.types import ProviderType
+
+logging: bool = False
+version_check: bool = True
+last_provider: ProviderType = None
+last_model: str = None
+version: str = None
+log_handler: callable = print
+logs: list = []
+
+def log(text):
+ if logging:
+ log_handler(text)
\ No newline at end of file
diff --git a/g4f/errors.py b/g4f/errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d553ba617094fc1c1a0b05f78bd832bf51ba387
--- /dev/null
+++ b/g4f/errors.py
@@ -0,0 +1,47 @@
+class ProviderNotFoundError(Exception):
+ ...
+
+class ProviderNotWorkingError(Exception):
+ ...
+
+class StreamNotSupportedError(Exception):
+ ...
+
+class ModelNotFoundError(Exception):
+ ...
+
+class ModelNotAllowedError(Exception):
+ ...
+
+class RetryProviderError(Exception):
+ ...
+
+class RetryNoProviderError(Exception):
+ ...
+
+class VersionNotFoundError(Exception):
+ ...
+
+class ModelNotSupportedError(Exception):
+ ...
+
+class MissingRequirementsError(Exception):
+ ...
+
+class NestAsyncioError(MissingRequirementsError):
+ ...
+
+class MissingAuthError(Exception):
+ ...
+
+class NoImageResponseError(Exception):
+ ...
+
+class RateLimitError(Exception):
+ ...
+
+class ResponseError(Exception):
+ ...
+
+class ResponseStatusError(Exception):
+ ...
\ No newline at end of file
diff --git a/g4f/gui/__init__.py b/g4f/gui/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..140711fae1bc6d8a75a4b89ee1def5273b7e5916
--- /dev/null
+++ b/g4f/gui/__init__.py
@@ -0,0 +1,43 @@
+from ..errors import MissingRequirementsError
+
+try:
+ from .server.app import app
+ from .server.website import Website
+ from .server.backend import Backend_Api
+ import_error = None
+except ImportError as e:
+ import_error = e
+
+def get_gui_app():
+ site = Website(app)
+ for route in site.routes:
+ app.add_url_rule(
+ route,
+ view_func=site.routes[route]['function'],
+ methods=site.routes[route]['methods'],
+ )
+
+ backend_api = Backend_Api(app)
+ for route in backend_api.routes:
+ app.add_url_rule(
+ route,
+ view_func = backend_api.routes[route]['function'],
+ methods = backend_api.routes[route]['methods'],
+ )
+ return app
+
+def run_gui(host: str = '0.0.0.0', port: int = 8080, debug: bool = False) -> None:
+ if import_error is not None:
+ raise MissingRequirementsError(f'Install "gui" requirements | pip install -U g4f[gui]\n{import_error}')
+
+ config = {
+ 'host' : host,
+ 'port' : port,
+ 'debug': debug
+ }
+
+ get_gui_app()
+
+ print(f"Running on port {config['port']}")
+ app.run(**config)
+ print(f"Closing port {config['port']}")
diff --git a/g4f/gui/client/index.html b/g4f/gui/client/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..8cbcd57833807824522f9e354f684c632ad3a569
--- /dev/null
+++ b/g4f/gui/client/index.html
@@ -0,0 +1,282 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ g4f - gui
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/g4f/gui/client/static/css/dracula.min.css b/g4f/gui/client/static/css/dracula.min.css
new file mode 100644
index 0000000000000000000000000000000000000000..729bbbfb62e6e015f666641e91a131eacafa5a7a
--- /dev/null
+++ b/g4f/gui/client/static/css/dracula.min.css
@@ -0,0 +1,7 @@
+/*!
+ Theme: Dracula
+ Author: Mike Barkmin (http://github.com/mikebarkmin) based on Dracula Theme (http://github.com/dracula)
+ License: ~ MIT (or more permissive) [via base16-schemes-source]
+ Maintainer: @highlightjs/core-team
+ Version: 2021.09.0
+*/pre code.hljs{display:block;overflow-x:auto;padding:1em}code.hljs{padding:3px 5px}.hljs{color:#e9e9f4;background:#282936}.hljs ::selection,.hljs::selection{background-color:#4d4f68;color:#e9e9f4}.hljs-comment{color:#626483}.hljs-tag{color:#62d6e8}.hljs-operator,.hljs-punctuation,.hljs-subst{color:#e9e9f4}.hljs-operator{opacity:.7}.hljs-bullet,.hljs-deletion,.hljs-name,.hljs-selector-tag,.hljs-template-variable,.hljs-variable{color:#ea51b2}.hljs-attr,.hljs-link,.hljs-literal,.hljs-number,.hljs-symbol,.hljs-variable.constant_{color:#b45bcf}.hljs-class .hljs-title,.hljs-title,.hljs-title.class_{color:#00f769}.hljs-strong{font-weight:700;color:#00f769}.hljs-addition,.hljs-code,.hljs-string,.hljs-title.class_.inherited__{color:#ebff87}.hljs-built_in,.hljs-doctag,.hljs-keyword.hljs-atrule,.hljs-quote,.hljs-regexp{color:#a1efe4}.hljs-attribute,.hljs-function .hljs-title,.hljs-section,.hljs-title.function_,.ruby .hljs-property{color:#62d6e8}.diff .hljs-meta,.hljs-keyword,.hljs-template-tag,.hljs-type{color:#b45bcf}.hljs-emphasis{color:#b45bcf;font-style:italic}.hljs-meta,.hljs-meta .hljs-keyword,.hljs-meta .hljs-string{color:#00f769}.hljs-meta .hljs-keyword,.hljs-meta-keyword{font-weight:700}
\ No newline at end of file
diff --git a/g4f/gui/client/static/css/style.css b/g4f/gui/client/static/css/style.css
new file mode 100644
index 0000000000000000000000000000000000000000..977a8908a21e287e3d199078f16ca0bbf3476f54
--- /dev/null
+++ b/g4f/gui/client/static/css/style.css
@@ -0,0 +1,1217 @@
+@import url("https://fonts.googleapis.com/css2?family=Inter:wght@100;200;300;400;500;600;700;800;900&display=swap");
+
+.adsbox {
+ backdrop-filter: blur(20px);
+ -webkit-backdrop-filter: blur(20px);
+ background-color: var(--blur-bg);
+ height: 100%;
+ width: 100%;
+ border-radius: var(--border-radius-1);
+ border: 1px solid var(--blur-border);
+}
+
+.ads {
+ align-items: center;
+ margin: auto;
+ display: flex;
+ flex-direction: column;
+ gap: var(--inner-gap);
+ max-width: 200px;
+ padding: var(--section-gap);
+ overflow: none;
+ flex-shrink: 0;
+ display: flex;
+ flex-direction: column;
+ justify-content: space-between;
+}
+
+@media screen and (max-width: 728px) {
+ .ads {
+ display: none;
+ }
+}
+
+/* :root {
+ --colour-1: #ffffff;
+ --colour-2: #000000;
+ --colour-3: #000000;
+ --colour-4: #000000;
+ --colour-5: #000000;
+ --colour-6: #000000;
+
+ --accent: #ffffff;
+ --blur-bg: #98989866;
+ --blur-border: #00000040;
+ --user-input: #000000;
+ --conversations: #000000;
+} */
+
+:root {
+ --colour-1: #000000;
+ --colour-2: #ccc;
+ --colour-3: #e4d4ff;
+ --colour-4: #f0f0f0;
+ --colour-5: #181818;
+ --colour-6: #242424;
+
+ --accent: #8b3dff;
+ --gradient: var(--accent);
+ --blur-bg: #16101b66;
+ --blur-border: #84719040;
+ --user-input: #ac87bb;
+ --conversations: #c7a2ff;
+ --conversations-hover: #c7a2ff4d;
+ --scrollbar: var(--colour-3);
+ --scrollbar-thumb: var(--blur-bg);
+}
+
+:root {
+ --font-1: "Inter", sans-serif;
+ --section-gap: 25px;
+ --inner-gap: 15px;
+ --border-radius-1: 8px;
+}
+
+* {
+ margin: 0;
+ padding: 0;
+ box-sizing: border-box;
+ position: relative;
+ font-family: var(--font-1);
+}
+
+html,
+body {
+ scroll-behavior: smooth;
+ overflow: hidden;
+}
+
+body {
+ background: var(--colour-1);
+ color: var(--colour-3);
+ height: 100vh;
+}
+
+.row {
+ display: flex;
+ gap: 10px;
+ height: 100%;
+}
+
+.box {
+ backdrop-filter: blur(20px);
+ -webkit-backdrop-filter: blur(20px);
+ background-color: var(--blur-bg);
+ height: 100%;
+ width: 100%;
+ border-radius: var(--border-radius-1);
+ border: 1px solid var(--blur-border);
+}
+
+.new_version {
+ position: absolute;
+ right: 0;
+ top: 0;
+ padding: 10px;
+ font-weight: 500;
+ background-color: rgba(0, 0, 0, 0.5);
+ color: var(--colour-3);
+}
+
+.white .new_version {
+ color: var(--colour-1);
+}
+
+.new_version a {
+ color: var(--colour-4);
+ text-decoration: underline dotted;
+}
+
+.conversations {
+ max-width: 300px;
+ padding: var(--section-gap);
+ overflow: auto;
+ flex-shrink: 0;
+ display: flex;
+ flex-direction: column;
+ justify-content: space-between;
+}
+
+.conversation {
+ width: 100%;
+ display: flex;
+ flex-direction: column;
+ gap: 5px;
+}
+
+.conversation #messages {
+ width: 100%;
+ height: 100%;
+ display: flex;
+ flex-direction: column;
+ overflow: auto;
+ overflow-wrap: break-word;
+ padding-bottom: 10px;
+}
+
+.conversation .user-input {
+ margin-bottom: 4px;
+}
+
+.conversation .user-input input {
+ font-size: 15px;
+ width: 100%;
+ height: 100%;
+ padding: 12px 15px;
+ background: none;
+ border: none;
+ outline: none;
+ color: var(--colour-3);
+}
+
+.conversation .user-input input::placeholder {
+ color: var(--user-input)
+}
+
+.gradient:nth-child(1) {
+ --top: 0;
+ --right: 0;
+ --size: 70vw;
+ --blur: calc(0.5 * var(--size));
+ --opacity: 0.3;
+ animation: zoom_gradient 6s infinite;
+}
+
+.gradient {
+ position: absolute;
+ z-index: -1;
+ border-radius: calc(0.5 * var(--size));
+ background-color: var(--accent);
+ background: radial-gradient(circle at center, var(--gradient), var(--gradient));
+ width: 70vw;
+ height: 70vw;
+ top: 50%;
+ right: 0;
+ transform: translateY(-50%);
+ filter: blur(calc(0.5 * 70vw)) opacity(var(--opacity));
+}
+
+body.white .gradient{
+ display: none;
+}
+
+.conversations {
+ display: flex;
+ flex-direction: column;
+ gap: 10px;
+ padding: 10px;
+}
+
+.conversations .title {
+ font-size: 14px;
+ font-weight: 500;
+}
+
+.conversations .convo {
+ padding: 8px 12px;
+ display: flex;
+ gap: 10px;
+ align-items: center;
+ user-select: none;
+ justify-content: space-between;
+ border: 1px dashed var(--conversations);
+ border-radius: var(--border-radius-1);
+}
+
+.conversations .convo .left {
+ width: 100%;
+ cursor: pointer;
+ display: flex;
+ align-items: center;
+ gap: 4px;
+}
+
+.conversations .convo .fa-ellipsis-vertical {
+ position: absolute;
+ right: 8px;
+ width: 14px;
+ text-align: center;
+}
+
+.conversations .convo .choise {
+ position: absolute;
+ right: 8px;
+ background-color: var(--blur-bg);
+}
+
+.conversations i, .bottom_buttons i {
+ color: var(--conversations);
+ cursor: pointer;
+}
+
+.bottom_buttons i {
+ width: 14px;
+}
+
+.convo-title {
+ color: var(--colour-3);
+ font-size: 14px;
+ max-width: 100%;
+ text-overflow: ellipsis;
+ overflow: hidden;
+ white-space: nowrap;
+ margin-right: 10px;
+ background-color: transparent;
+ border: 0;
+ width: 100%;
+}
+
+.convo-title:focus {
+ outline: 1px solid var(--colour-3) !important;
+}
+
+.convo .datetime {
+ white-space: nowrap;
+ font-size: 10px;
+}
+
+.message {
+ width: 100%;
+ overflow-wrap: break-word;
+ display: flex;
+ flex-direction: column;
+ gap: var(--section-gap);
+ padding: var(--inner-gap) var(--section-gap);
+}
+
+.message.print {
+ height: 100%;
+ position: absolute;
+ background-color: #fff;
+ z-index: 100;
+ top: 0;
+}
+
+.message.regenerate {
+ background-color: var(--colour-6);
+}
+
+.white .message.regenerate {
+ background-color: var(--colour-4);
+}
+
+.message:last-child {
+ animation: 0.6s show_message;
+}
+
+@keyframes show_message {
+ from {
+ transform: translateY(10px);
+ opacity: 0;
+ }
+}
+
+.message .user {
+ max-width: 48px;
+ max-height: 48px;
+ flex-shrink: 0;
+}
+
+.message .user img {
+ width: 100%;
+ height: 100%;
+ object-fit: cover;
+ border-radius: 8px;
+ outline: 1px solid var(--blur-border);
+}
+
+.message .user:after {
+ content: "63";
+ position: absolute;
+ bottom: 0;
+ right: 0;
+ height: 60%;
+ width: 60%;
+ background: var(--colour-3);
+ filter: blur(10px) opacity(0.5);
+ z-index: 10000;
+}
+
+.message .assistant{
+ max-width: 48px;
+ max-height: 48px;
+ flex-shrink: 0;
+}
+
+.message .assistant img {
+ width: 100%;
+ height: 100%;
+ object-fit: cover;
+ border-radius: 8px;
+ outline: 1px solid var(--blur-border);
+}
+
+.message .assistant:after {
+ content: "63";
+ position: absolute;
+ bottom: 0;
+ right: 0;
+ height: 60%;
+ width: 60%;
+ background: var(--colour-3);
+ filter: blur(10px) opacity(0.5);
+ z-index: 10000;
+}
+
+.message .content {
+ display: flex;
+ flex-direction: column;
+ gap: 10px;
+ flex-wrap: wrap;
+}
+
+.message .content_inner,
+.message .content_inner a:link,
+.message .content_inner a:visited{
+ font-size: 15px;
+ line-height: 1.3;
+ color: var(--colour-3);
+}
+.message .content_inner pre{
+ white-space: pre-wrap;
+}
+
+.message .content img{
+ max-width: 400px;
+}
+
+.message .content .audio{
+ display: flex;
+}
+
+.message .user i {
+ position: absolute;
+ bottom: -6px;
+ right: -6px;
+ z-index: 1000;
+}
+
+.message .assistant .fa-phone-arrow-up-right,
+.message .assistant .fa-phone-arrow-down-left {
+ position: absolute;
+ bottom: -6px;
+ right: -6px;
+ z-index: 1000;
+}
+
+.message .assistant .fa-xmark,
+.message .user .fa-xmark {
+ position: absolute;
+ top: -2px;
+ left: 0px;
+ z-index: 1000;
+ display: none;
+ cursor: pointer;
+}
+
+.message .user .fa-xmark {
+ color: var(--colour-1);
+}
+
+.message .count .fa-clipboard,
+.message .count .fa-volume-high,
+.message .count .fa-rotate,
+.message .count .fa-print {
+ z-index: 1000;
+ cursor: pointer;
+}
+
+.message .count .fa-clipboard,
+.message .count .fa-whatsapp {
+ color: var(--colour-3);
+}
+
+.message .count .fa-clipboard.clicked,
+.message .count .fa-print.clicked,
+.message .count .fa-rotate.clicked,
+.message .count .fa-volume-high.active {
+ color: var(--accent);
+}
+
+.message .assistant:hover .fa-xmark,
+.message .user:hover .fa-xmark {
+ display: block;
+}
+
+.message .content .provider a,
+.message .content .provider {
+ font-size: 12px;
+ text-decoration: none;
+}
+
+.message .content .provider a {
+ font-weight: bold;
+}
+
+.message .content .count {
+ font-size: 12px;
+}
+
+.media_player {
+ display: none;
+}
+
+.media_player audio {
+ right: 28px;
+ position: absolute;
+ top: -4px;
+ z-index: 900;
+}
+
+.media_player.show {
+ display: block;
+}
+
+.media_player .fa-x {
+ position: absolute;
+ right: 8px;
+ top: 8px;
+ z-index: 1000;
+}
+
+.count_total {
+ font-size: 12px;
+ padding-left: 25px;
+ padding-top: 10px;
+}
+
+.new_convo {
+ padding: 8px 12px;
+ display: flex;
+ gap: 18px;
+ align-items: center;
+ cursor: pointer;
+ user-select: none;
+ background: transparent;
+ border: 1px solid var(--conversations);
+ border-radius: var(--border-radius-1);
+ transition: all 0.2s ease;
+}
+
+.new_convo:hover {
+ box-shadow: inset 0px 0px 20px var(--conversations-hover);
+}
+
+.new_convo span {
+ color: var(--colour-3);
+ font-size: 14px;
+}
+
+.toolbar {
+ position: relative;
+}
+
+#input-count {
+ width: fit-content;
+ font-size: 12px;
+ padding: 6px 6px;
+}
+
+#input-count .text {
+ padding: 0 4px;
+}
+
+.stop_generating-hidden, .regenerate-hidden {
+ animation: hide_popup 0.4s;
+ display: none;
+}
+
+.stop_generating, .toolbar .regenerate {
+ position: absolute;
+ z-index: 1000000;
+ top: 0;
+ right: 0;
+}
+
+.stop_generating button, .toolbar .regenerate button{
+ backdrop-filter: blur(20px);
+ -webkit-backdrop-filter: blur(20px);
+ background-color: var(--blur-bg);
+ border-radius: var(--border-radius-1);
+ border: 1px solid var(--blur-border);
+ padding: 5px var(--inner-gap);
+ color: var(--colour-3);
+ display: flex;
+ justify-content: center;
+ align-items: center;
+ gap: 12px;
+ cursor: pointer;
+ animation: show_popup 0.4s;
+ height: 28px;
+}
+
+.toolbar .regenerate {
+ left: 50%;
+ transform: translateX(-50%);
+ right: auto;
+}
+
+.toolbar .regenerate span {
+ display: none;
+}
+
+@media only screen and (min-width: 40em) {
+ .stop_generating {
+ right: 4px;
+ }
+ .toolbar .regenerate span {
+ display: block;
+ }
+}
+
+.toolbar .hide-input {
+ background: transparent;
+ border: none;
+ color: var(--colour-3);
+ cursor: pointer;
+}
+
+@keyframes show_popup {
+ from {
+ opacity: 0;
+ transform: translateY(10px);
+ }
+}
+
+@keyframes hide_popup {
+ to {
+ opacity: 0;
+ transform: translateY(10px);
+ }
+}
+
+.typing {
+ position: absolute;
+ top: -25px;
+ left: 0;
+ font-size: 14px;
+ animation: show_popup 0.4s;
+}
+
+.typing-hiding {
+ animation: hide_popup 0.4s;
+}
+
+.typing-hidden {
+ display: none;
+}
+
+#image, #file, #camera {
+ display: none;
+}
+
+.file-label,
+.micro-label {
+ cursor: pointer;
+ position: absolute;
+ top: 10px;
+ left: 10px;
+}
+
+.file-label:has(> input:valid),
+.file-label.selected,
+.micro-label.recognition {
+ color: var(--accent);
+}
+
+label[for="image"] {
+ top: 32px;
+}
+
+label[for="micro"] {
+ top: 54px;
+}
+
+label[for="camera"] {
+ top: 74px;
+ display: none;
+}
+
+@media (pointer:none), (pointer:coarse) {
+ label[for="camera"] {
+ display: block;
+ }
+}
+
+.buttons input[type="checkbox"],
+.settings input[type="checkbox"] {
+ height: 0;
+ width: 0;
+ display: none;
+}
+
+.buttons label,
+.settings label.toogle {
+ cursor: pointer;
+ text-indent: -9999px;
+ width: 50px;
+ height: 30px;
+ backdrop-filter: blur(20px);
+ -webkit-backdrop-filter: blur(20px);
+ background-color: var(--blur-bg);
+ border-radius: var(--border-radius-1);
+ border: 1px solid var(--blur-border);
+ display: block;
+ border-radius: 100px;
+ position: relative;
+ overflow: hidden;
+ transition: 0.33s;
+}
+
+.buttons label:after,
+.settings label.toogle:after {
+ content: "";
+ position: absolute;
+ top: 50%;
+ transform: translateY(-50%);
+ left: 5px;
+ width: 20px;
+ height: 20px;
+ background: var(--colour-3);
+ border-radius: 90px;
+ transition: 0.33s;
+}
+
+.buttons input:checked+label,
+.settings input:checked+label {
+ background: var(--accent);
+}
+
+.settings .bottom_buttons {
+ flex-direction: column;
+}
+
+.settings .bottom_buttons button {
+ display: inline-block;
+ max-width: 210px;
+ width: 100%;
+}
+
+.buttons input:checked+label:after,
+.settings input:checked+label:after {
+ left: calc(100% - 5px - 20px);
+}
+
+.buttons {
+ display: flex;
+ align-items: center;
+ justify-content: left;
+ width: 100%;
+ margin-bottom: 4px;
+}
+
+.field {
+ height: fit-content;
+ display: flex;
+ align-items: center;
+ gap: var(--inner-gap);
+}
+
+.field .about {
+ font-size: 14px;
+ color: var(--colour-3);
+}
+
+
+select {
+ -webkit-border-radius: 8px;
+ -moz-border-radius: 8px;
+ border-radius: 8px;
+
+ -webkit-backdrop-filter: blur(20px);
+ backdrop-filter: blur(20px);
+
+ cursor: pointer;
+ background-color: var(--colour-1);
+ border: 1px solid var(--blur-border);
+ color: var(--colour-3);
+ display: block;
+ position: relative;
+ overflow: hidden;
+ outline: none;
+ padding: 8px 16px;
+
+ appearance: none;
+ width: 160px;
+}
+
+#systemPrompt, .settings textarea {
+ font-size: 15px;
+ width: 100%;
+ color: var(--colour-3);
+ min-height: 49px;
+ height: 59px;
+ outline: none;
+ padding: var(--inner-gap) var(--section-gap);
+ resize: vertical;
+}
+
+.slide-systemPrompt {
+ position: absolute;
+ top: 0;
+ padding: var(--inner-gap) 10px;
+ border: none;
+ background: transparent;
+ cursor: pointer;
+ height: 49px;
+ color: var(--colour-3);
+}
+
+@media only screen and (min-width: 40em) {
+ select {
+ width: 200px;
+ }
+ .field {
+ padding-right: 15px
+ }
+ .message {
+ flex-direction: row;
+ }
+ .settings .bottom_buttons {
+ flex-direction: row;
+ }
+ .count_total {
+ padding-left: 98px;
+ }
+}
+
+.input-box {
+ display: flex;
+ align-items: center;
+ cursor: pointer;
+}
+
+.info {
+ padding: 8px 12px;
+ display: flex;
+ gap: 18px;
+ align-items: center;
+ user-select: none;
+ background: transparent;
+ border-radius: var(--border-radius-1);
+ width: 100%;
+ cursor: default;
+ border: 1px dashed var(--conversations)
+}
+
+.bottom_buttons {
+ width: 100%;
+ display: flex;
+ flex-direction: column;
+ gap: 10px;
+ margin: 4px 0;
+}
+
+.bottom_buttons button {
+ padding: 8px 12px;
+ display: flex;
+ gap: 18px;
+ align-items: center;
+ cursor: pointer;
+ user-select: none;
+ background: transparent;
+ border: 1px solid var(--conversations);
+ border-radius: var(--border-radius-1);
+ width: 100%;
+}
+
+.bottom_buttons button a,
+.bottom_buttons button span,
+.bottom_buttons .info a,
+.bottom_buttons .info i {
+ color: var(--colour-3);
+ font-weight: 500;
+}
+
+.conversations .top {
+ display: flex;
+ flex-direction: column;
+ gap: var(--inner-gap);
+ overflow: auto;
+}
+
+.cursor {
+ line-height: 17px;
+ margin-left: 3px;
+ -webkit-animation: blink 0.8s infinite;
+ animation: blink 0.8s infinite;
+ width: 7px;
+ height: 15px;
+ display: inline-block;
+}
+
+@keyframes blink {
+ 0% {
+ background: #ffffff00;
+ }
+
+ 50% {
+ background: var(--colour-3);
+ }
+
+ 100% {
+ background: #ffffff00;
+ }
+}
+
+@-webkit-keyframes blink {
+ 0% {
+ background: #ffffff00;
+ }
+
+ 50% {
+ background: white;
+ }
+
+ 100% {
+ background: #ffffff00;
+ }
+}
+
+
+ol,
+ul {
+ padding-left: 20px;
+}
+
+
+@keyframes spinner {
+ to {
+ transform: rotate(360deg);
+ }
+}
+
+.spinner:before {
+ content: '';
+ box-sizing: border-box;
+ position: absolute;
+ top: 50%;
+ left: 45%;
+ width: 20px;
+ height: 20px;
+
+ border-radius: 50%;
+ border: 1px solid var(--conversations);
+ border-top-color: white;
+ animation: spinner .6s linear infinite;
+}
+
+.grecaptcha-badge {
+ visibility: hidden;
+}
+
+.mobile-sidebar {
+ display: none;
+ position: absolute;
+ z-index: 100000;
+ top: 0;
+ left: 0;
+ margin: 10px;
+ font-size: 20px;
+ cursor: pointer;
+ backdrop-filter: blur(20px);
+ -webkit-backdrop-filter: blur(20px);
+ background-color: var(--blur-bg);
+ border-radius: 10px;
+ border: 1px solid var(--blur-border);
+ width: 40px;
+ height: 40px;
+ justify-content: center;
+ align-items: center;
+ transition: 0.33s;
+}
+
+.mobile-sidebar i {
+ transition: 0.33s;
+}
+
+.rotated {
+ transform: rotate(360deg);
+}
+
+.settings h3 {
+ padding-left: 10px;
+ padding-top: 10px;
+}
+
+@media screen and (max-width: 990px) {
+ .conversations {
+ display: none;
+ width: 100%;
+ max-width: none;
+ }
+
+ .settings h3 {
+ padding-left: 50px;
+ }
+
+ .buttons {
+ align-items: flex-start;
+ flex-wrap: wrap;
+ gap: 15px;
+ }
+
+ .mobile-sidebar {
+ display: flex;
+ }
+
+ #systemPrompt {
+ padding-left: 48px;
+ }
+}
+
+.shown {
+ display: flex;
+}
+
+
+a:-webkit-any-link {
+ color: var(--accent);
+}
+
+.conversation .user-input textarea {
+ font-size: 15px;
+ width: 100%;
+ height: 100%;
+ padding: 12px var(--inner-gap);
+ background: none;
+ border: none;
+ outline: none;
+ color: var(--colour-3);
+
+ resize: vertical;
+ max-height: 200px;
+ min-height: 100px;
+}
+
+/* style for hljs copy */
+.hljs-copy-wrapper {
+ position: relative;
+ overflow: hidden
+}
+
+.hljs-copy-wrapper:hover .hljs-copy-button,
+.hljs-copy-button:focus {
+ transform: translateX(0)
+}
+
+.hljs-copy-button {
+ position: absolute;
+ transform: translateX(calc(100% + 1.125em));
+ top: 1em;
+ right: 1em;
+ width: 2rem;
+ height: 2rem;
+ text-indent: -9999px;
+ color: #fff;
+ border-radius: .25rem;
+ border: 1px solid #ffffff22;
+ background-color: #2d2b57;
+ background-image: url('data:image/svg+xml;utf-8,');
+ background-repeat: no-repeat;
+ background-position: center;
+ transition: background-color 200ms ease, transform 200ms ease-out
+}
+
+.hljs-copy-button:hover {
+ border-color: #ffffff44
+}
+
+.hljs-copy-button:active {
+ border-color: #ffffff66
+}
+
+.hljs-copy-button[data-copied="true"] {
+ text-indent: 0;
+ width: auto;
+ background-image: none
+}
+
+@media(prefers-reduced-motion) {
+ .hljs-copy-button {
+ transition: none
+ }
+}
+
+.hljs-copy-alert {
+ clip: rect(0 0 0 0);
+ clip-path: inset(50%);
+ height: 1px;
+ overflow: hidden;
+ position: absolute;
+ white-space: nowrap;
+ width: 1px
+}
+
+.visually-hidden {
+ clip: rect(0 0 0 0);
+ clip-path: inset(50%);
+ height: 1px;
+ overflow: hidden;
+ position: absolute;
+ white-space: nowrap;
+ width: 1px;
+}
+
+.white {
+ --blur-bg: transparent;
+ --accent: #007bff;
+ --gradient: #ccc;
+ --conversations: #0062cc;
+ --colour-1: #ffffff;
+ --colour-3: #212529;
+ --scrollbar: var(--colour-1);
+ --scrollbar-thumb: var(--gradient);
+}
+
+.white .message .assistant .fa-xmark {
+ color: var(--colour-1);
+}
+
+.white .message .user .fa-xmark {
+ color: var(--colour-3);
+}
+
+#send-button {
+ border: 1px dashed #e4d4ffa6;
+ border-radius: 4px;
+ cursor: pointer;
+ padding-left: 8px;
+ padding-right: 5px;
+ padding-top: 2px;
+ padding-bottom: 2px;
+ position: absolute;
+ bottom: 8px;
+ right: 8px;
+}
+
+#send-button:hover {
+ border: 1px solid #e4d4ffc9;
+}
+
+.settings textarea {
+ height: 19px;
+ min-height: 19px;
+ padding: 0;
+}
+
+.settings .field.box {
+ padding: var(--inner-gap) var(--inner-gap) var(--inner-gap) 0;
+}
+
+.settings, .log {
+ width: 100%;
+ display: flex;
+ flex-direction: column;
+ overflow: auto;
+}
+
+.log {
+ white-space: pre-wrap;
+}
+
+.log.hidden {
+ display: none;
+}
+
+.settings .paper {
+ flex-direction: column;
+ min-width: 400px;
+}
+
+.settings .field {
+ margin: var(--inner-gap) 0;
+}
+
+.settings textarea {
+ background-color: transparent;
+ border: none;
+}
+
+.settings input {
+ background-color: transparent;
+ padding: 2px;
+ border: none;
+ font-size: 15px;
+ width: 100%;
+ color: var(--colour-3);
+}
+
+.settings input:focus {
+ outline: none;
+}
+
+.settings .label {
+ font-size: 15px;
+ margin-left: var(--inner-gap);
+ white-space:nowrap;
+}
+
+::-webkit-scrollbar-track {
+ background: var(--scrollbar);
+}
+::-webkit-scrollbar-thumb {
+ background: var(--scrollbar-thumb);
+ border-radius: 2px;
+}
+::-webkit-scrollbar-thumb:hover {
+ background: var(--accent);
+}
+
+.hljs {
+ color: #e9e9f4;
+ background: #28293629;
+ border-radius: var(--border-radius-1);
+ border: 1px solid var(--blur-border);
+ font-size: 15px;
+}
+
+#message-input {
+ height: 90px;
+ margin-left: 20px;
+ max-height: 200px;
+}
+
+.hidden {
+ display: none;
+}
+
+.blink {
+ animation: blinker 1s step-start infinite;
+}
+
+@keyframes blinker {
+ 50% {
+ opacity: 0;
+ }
+}
+
+@media print {
+ #systemPrompt:placeholder-shown,
+ .conversations,
+ .conversation .user-input,
+ .conversation .buttons,
+ .conversation .toolbar,
+ .conversation .slide-systemPrompt,
+ .message .count i,
+ .message .assistant,
+ .message .user {
+ display: none;
+ }
+ body {
+ height: auto;
+ }
+ .box {
+ backdrop-filter: none;
+ }
+}
diff --git a/g4f/gui/client/static/img/android-chrome-192x192.png b/g4f/gui/client/static/img/android-chrome-192x192.png
new file mode 100644
index 0000000000000000000000000000000000000000..3c32aceb59c84838095aea1c8962b4143ea180f6
Binary files /dev/null and b/g4f/gui/client/static/img/android-chrome-192x192.png differ
diff --git a/g4f/gui/client/static/img/android-chrome-512x512.png b/g4f/gui/client/static/img/android-chrome-512x512.png
new file mode 100644
index 0000000000000000000000000000000000000000..ae601c93048536303b4aed41d0c58178139b22f7
Binary files /dev/null and b/g4f/gui/client/static/img/android-chrome-512x512.png differ
diff --git a/g4f/gui/client/static/img/apple-touch-icon.png b/g4f/gui/client/static/img/apple-touch-icon.png
new file mode 100644
index 0000000000000000000000000000000000000000..1143d19a8927dcc85f085800240d9eadef9b8e6b
Binary files /dev/null and b/g4f/gui/client/static/img/apple-touch-icon.png differ
diff --git a/g4f/gui/client/static/img/favicon-16x16.png b/g4f/gui/client/static/img/favicon-16x16.png
new file mode 100644
index 0000000000000000000000000000000000000000..6e934fb84cff69a755de17327b1358a37b172082
Binary files /dev/null and b/g4f/gui/client/static/img/favicon-16x16.png differ
diff --git a/g4f/gui/client/static/img/favicon-32x32.png b/g4f/gui/client/static/img/favicon-32x32.png
new file mode 100644
index 0000000000000000000000000000000000000000..efc095b5c65af688a634985dc48e994ddbfc1d07
Binary files /dev/null and b/g4f/gui/client/static/img/favicon-32x32.png differ
diff --git a/g4f/gui/client/static/img/gpt.png b/g4f/gui/client/static/img/gpt.png
new file mode 100644
index 0000000000000000000000000000000000000000..60e24da029500196f0d7ad1f916d6d669de8ecc9
Binary files /dev/null and b/g4f/gui/client/static/img/gpt.png differ
diff --git a/g4f/gui/client/static/img/site.webmanifest b/g4f/gui/client/static/img/site.webmanifest
new file mode 100644
index 0000000000000000000000000000000000000000..f8eab4d70e5d5c21f077ab35a8dd7751c30f1de2
--- /dev/null
+++ b/g4f/gui/client/static/img/site.webmanifest
@@ -0,0 +1,19 @@
+{
+ "name": "",
+ "short_name": "",
+ "icons": [
+ {
+ "src": "/assets/img/android-chrome-192x192.png",
+ "sizes": "192x192",
+ "type": "image/png"
+ },
+ {
+ "src": "/assets/img/android-chrome-512x512.png",
+ "sizes": "512x512",
+ "type": "image/png"
+ }
+ ],
+ "theme_color": "#ffffff",
+ "background_color": "#ffffff",
+ "display": "standalone"
+}
\ No newline at end of file
diff --git a/g4f/gui/client/static/img/user.png b/g4f/gui/client/static/img/user.png
new file mode 100644
index 0000000000000000000000000000000000000000..d1908e1d0a0f0f10cba697b674589277cce6e019
Binary files /dev/null and b/g4f/gui/client/static/img/user.png differ
diff --git a/g4f/gui/client/static/js/chat.v1.js b/g4f/gui/client/static/js/chat.v1.js
new file mode 100644
index 0000000000000000000000000000000000000000..0bf49ac31d5795efaf148ff15366ff69f4316f3c
--- /dev/null
+++ b/g4f/gui/client/static/js/chat.v1.js
@@ -0,0 +1,1616 @@
+const colorThemes = document.querySelectorAll('[name="theme"]');
+const message_box = document.getElementById(`messages`);
+const messageInput = document.getElementById(`message-input`);
+const box_conversations = document.querySelector(`.top`);
+const stop_generating = document.querySelector(`.stop_generating`);
+const regenerate = document.querySelector(`.regenerate`);
+const sidebar = document.querySelector(".conversations");
+const sidebar_button = document.querySelector(".mobile-sidebar");
+const sendButton = document.getElementById("send-button");
+const imageInput = document.getElementById("image");
+const cameraInput = document.getElementById("camera");
+const fileInput = document.getElementById("file");
+const microLabel = document.querySelector(".micro-label");
+const inputCount = document.getElementById("input-count").querySelector(".text");
+const providerSelect = document.getElementById("provider");
+const modelSelect = document.getElementById("model");
+const modelProvider = document.getElementById("model2");
+const systemPrompt = document.getElementById("systemPrompt");
+const settings = document.querySelector(".settings");
+const chat = document.querySelector(".conversation");
+const album = document.querySelector(".images");
+const log_storage = document.querySelector(".log");
+
+const optionElements = document.querySelectorAll(".settings input, .settings textarea, #model, #model2, #provider")
+
+let provider_storage = {};
+let message_storage = {};
+let controller_storage = {};
+let content_storage = {};
+let error_storage = {};
+let synthesize_storage = {};
+
+messageInput.addEventListener("blur", () => {
+ window.scrollTo(0, 0);
+});
+
+messageInput.addEventListener("focus", () => {
+ document.documentElement.scrollTop = document.documentElement.scrollHeight;
+});
+
+appStorage = window.localStorage || {
+ setItem: (key, value) => self[key] = value,
+ getItem: (key) => self[key],
+ removeItem: (key) => delete self[key],
+ length: 0
+}
+let markdown_render = () => null;
+if (window.markdownit) {
+ const markdown = window.markdownit();
+ markdown_render = (content) => {
+ return markdown.render(content
+ .replaceAll(/|/gm, "")
+ .replaceAll(/![]()
/gm, "")
+ )
+ .replaceAll("', '')
+ }
+}
+function filter_message(text) {
+ return text.replaceAll(
+ /[\s\S]+/gm, ""
+ )
+}
+
+function fallback_clipboard (text) {
+ var textBox = document.createElement("textarea");
+ textBox.value = text;
+ textBox.style.top = "0";
+ textBox.style.left = "0";
+ textBox.style.position = "fixed";
+ document.body.appendChild(textBox);
+ textBox.focus();
+ textBox.select();
+ try {
+ var success = document.execCommand('copy');
+ var msg = success ? 'succeeded' : 'failed';
+ console.log('Clipboard Fallback: Copying text command ' + msg);
+ } catch (e) {
+ console.error('Clipboard Fallback: Unable to copy', e);
+ }
+ document.body.removeChild(textBox);
+}
+
+hljs.addPlugin(new CopyButtonPlugin());
+let typesetPromise = Promise.resolve();
+const highlight = (container) => {
+ container.querySelectorAll('code:not(.hljs').forEach((el) => {
+ if (el.className != "hljs") {
+ hljs.highlightElement(el);
+ }
+ });
+ if (window.MathJax) {
+ typesetPromise = typesetPromise.then(
+ () => MathJax.typesetPromise([container])
+ ).catch(
+ (err) => console.log('Typeset failed: ' + err.message)
+ );
+ }
+}
+
+const register_message_buttons = async () => {
+ document.querySelectorAll(".message .fa-xmark").forEach(async (el) => {
+ if (!("click" in el.dataset)) {
+ el.dataset.click = "true";
+ el.addEventListener("click", async () => {
+ const message_el = el.parentElement.parentElement;
+ await remove_message(window.conversation_id, message_el.dataset.index);
+ await safe_load_conversation(window.conversation_id, false);
+ });
+ }
+ });
+
+ document.querySelectorAll(".message .fa-clipboard").forEach(async (el) => {
+ if (!("click" in el.dataset)) {
+ el.dataset.click = "true";
+ el.addEventListener("click", async () => {
+ const message_el = el.parentElement.parentElement.parentElement;
+ const copyText = await get_message(window.conversation_id, message_el.dataset.index);
+ try {
+ if (!navigator.clipboard) {
+ throw new Error("navigator.clipboard: Clipboard API unavailable.");
+ }
+ await navigator.clipboard.writeText(copyText);
+ } catch (e) {
+ console.error(e);
+ console.error("Clipboard API writeText() failed! Fallback to document.exec(\"copy\")...");
+ fallback_clipboard(copyText);
+ }
+ el.classList.add("clicked");
+ setTimeout(() => el.classList.remove("clicked"), 1000);
+ })
+ }
+ });
+
+ document.querySelectorAll(".message .fa-volume-high").forEach(async (el) => {
+ if (!("click" in el.dataset)) {
+ el.dataset.click = "true";
+ el.addEventListener("click", async () => {
+ const message_el = el.parentElement.parentElement.parentElement;
+ let audio;
+ if (message_el.dataset.synthesize_url) {
+ el.classList.add("active");
+ setTimeout(()=>el.classList.remove("active"), 2000);
+ const media_player = document.querySelector(".media_player");
+ if (!media_player.classList.contains("show")) {
+ media_player.classList.add("show");
+ audio = new Audio(message_el.dataset.synthesize_url);
+ audio.controls = true;
+ media_player.appendChild(audio);
+ } else {
+ audio = media_player.querySelector("audio");
+ audio.src = message_el.dataset.synthesize_url;
+ }
+ audio.play();
+ return;
+ }
+ let playlist = [];
+ function play_next() {
+ const next = playlist.shift();
+ if (next && el.dataset.do_play) {
+ next.play();
+ }
+ }
+ if (el.dataset.stopped) {
+ el.classList.remove("blink")
+ delete el.dataset.stopped;
+ return;
+ }
+ if (el.dataset.running) {
+ el.dataset.stopped = true;
+ el.classList.add("blink")
+ playlist = [];
+ return;
+ }
+ el.dataset.running = true;
+ el.classList.add("blink")
+ el.classList.add("active")
+
+ let speechText = await get_message(window.conversation_id, message_el.dataset.index);
+
+ speechText = speechText.replaceAll(/([^0-9])\./gm, "$1.;");
+ speechText = speechText.replaceAll("?", "?;");
+ speechText = speechText.replaceAll(/\[(.+)\]\(.+\)/gm, "($1)");
+ speechText = speechText.replaceAll(/```[a-z]+/gm, "");
+ speechText = filter_message(speechText.replaceAll("`", "").replaceAll("#", ""))
+ const lines = speechText.trim().split(/\n|;/).filter(v => count_words(v));
+
+ window.onSpeechResponse = (url) => {
+ if (!el.dataset.stopped) {
+ el.classList.remove("blink")
+ }
+ if (url) {
+ var sound = document.createElement('audio');
+ sound.controls = 'controls';
+ sound.src = url;
+ sound.type = 'audio/wav';
+ sound.onended = function() {
+ el.dataset.do_play = true;
+ setTimeout(play_next, 1000);
+ };
+ sound.onplay = function() {
+ delete el.dataset.do_play;
+ };
+ var container = document.createElement('div');
+ container.classList.add("audio");
+ container.appendChild(sound);
+ content_el.appendChild(container);
+ if (!el.dataset.stopped) {
+ playlist.push(sound);
+ if (el.dataset.do_play) {
+ play_next();
+ }
+ }
+ }
+ let line = lines.length > 0 ? lines.shift() : null;
+ if (line && !el.dataset.stopped) {
+ handleGenerateSpeech(line);
+ } else {
+ el.classList.remove("active");
+ el.classList.remove("blink");
+ delete el.dataset.running;
+ }
+ }
+ el.dataset.do_play = true;
+ let line = lines.shift();
+ handleGenerateSpeech(line);
+ });
+ }
+ });
+ document.querySelectorAll(".message .fa-rotate").forEach(async (el) => {
+ if (!("click" in el.dataset)) {
+ el.dataset.click = "true";
+ el.addEventListener("click", async () => {
+ const message_el = el.parentElement.parentElement.parentElement;
+ el.classList.add("clicked");
+ setTimeout(() => el.classList.remove("clicked"), 1000);
+ await ask_gpt(get_message_id(), message_el.dataset.index);
+ });
+ }
+ });
+ document.querySelectorAll(".message .fa-whatsapp").forEach(async (el) => {
+ if (!el.parentElement.href) {
+ const text = el.parentElement.parentElement.parentElement.innerText;
+ el.parentElement.href = `https://wa.me/?text=${encodeURIComponent(text)}`;
+ }
+ });
+ document.querySelectorAll(".message .fa-print").forEach(async (el) => {
+ if (!("click" in el.dataset)) {
+ el.dataset.click = "true";
+ el.addEventListener("click", async () => {
+ const message_el = el.parentElement.parentElement.parentElement;
+ el.classList.add("clicked");
+ message_box.scrollTop = 0;
+ message_el.classList.add("print");
+ setTimeout(() => el.classList.remove("clicked"), 1000);
+ setTimeout(() => message_el.classList.remove("print"), 1000);
+ window.print()
+ })
+ }
+ });
+}
+
+const delete_conversations = async () => {
+ const remove_keys = [];
+ for (let i = 0; i < appStorage.length; i++){
+ let key = appStorage.key(i);
+ if (key.startsWith("conversation:")) {
+ remove_keys.push(key);
+ }
+ }
+ remove_keys.forEach((key)=>appStorage.removeItem(key));
+ hide_sidebar();
+ await new_conversation();
+};
+
+const handle_ask = async () => {
+ messageInput.style.height = "82px";
+ messageInput.focus();
+ await scroll_to_bottom();
+
+ let message = messageInput.value;
+ if (message.length <= 0) {
+ return;
+ }
+ messageInput.value = "";
+ await count_input()
+ await add_conversation(window.conversation_id, message);
+
+ if ("text" in fileInput.dataset) {
+ message += '\n```' + fileInput.dataset.type + '\n';
+ message += fileInput.dataset.text;
+ message += '\n```'
+ }
+ let message_index = await add_message(window.conversation_id, "user", message);
+ let message_id = get_message_id();
+
+ if (imageInput.dataset.src) URL.revokeObjectURL(imageInput.dataset.src);
+ const input = imageInput && imageInput.files.length > 0 ? imageInput : cameraInput
+ if (input.files.length > 0) imageInput.dataset.src = URL.createObjectURL(input.files[0]);
+ else delete imageInput.dataset.src
+
+ message_box.innerHTML += `
+
+ `;
+ highlight(message_box);
+ await ask_gpt(message_id);
+};
+
+async function safe_remove_cancel_button() {
+ for (let key in controller_storage) {
+ if (!controller_storage[key].signal.aborted) {
+ return;
+ }
+ }
+ stop_generating.classList.add("stop_generating-hidden");
+}
+
+regenerate.addEventListener("click", async () => {
+ regenerate.classList.add("regenerate-hidden");
+ setTimeout(()=>regenerate.classList.remove("regenerate-hidden"), 3000);
+ await hide_message(window.conversation_id);
+ await ask_gpt(get_message_id());
+});
+
+stop_generating.addEventListener("click", async () => {
+ stop_generating.classList.add("stop_generating-hidden");
+ regenerate.classList.remove("regenerate-hidden");
+ let key;
+ for (key in controller_storage) {
+ if (!controller_storage[key].signal.aborted) {
+ controller_storage[key].abort();
+ let message = message_storage[key];
+ if (message) {
+ content_storage[key].inner.innerHTML += " [aborted]";
+ message_storage[key] += " [aborted]";
+ console.log(`aborted ${window.conversation_id} #${key}`);
+ }
+ }
+ }
+ await load_conversation(window.conversation_id, false);
+});
+
+document.querySelector(".media_player .fa-x").addEventListener("click", ()=>{
+ const media_player = document.querySelector(".media_player");
+ media_player.classList.remove("show");
+ const audio = document.querySelector(".media_player audio");
+ media_player.removeChild(audio);
+});
+
+const prepare_messages = (messages, message_index = -1) => {
+ if (message_index >= 0) {
+ messages = messages.filter((_, index) => message_index >= index);
+ }
+
+ // Removes none user messages at end
+ let last_message;
+ while (last_message = messages.pop()) {
+ if (last_message["role"] == "user") {
+ messages.push(last_message);
+ break;
+ }
+ }
+
+ let new_messages = [];
+ if (systemPrompt?.value) {
+ new_messages.push({
+ "role": "system",
+ "content": systemPrompt.value
+ });
+ }
+
+ // Remove history, if it's selected
+ if (document.getElementById('history')?.checked) {
+ if (message_index == null) {
+ messages = [messages.pop(), messages.pop()];
+ } else {
+ messages = [messages.pop()];
+ }
+ }
+
+ messages.forEach((new_message) => {
+ // Include only not regenerated messages
+ if (new_message && !new_message.regenerate) {
+ // Remove generated images from history
+ new_message.content = filter_message(new_message.content);
+ delete new_message.provider;
+ delete new_message.synthesize;
+ new_messages.push(new_message)
+ }
+ });
+
+ return new_messages;
+}
+
+async function add_message_chunk(message, message_id) {
+ content_map = content_storage[message_id];
+ if (message.type == "conversation") {
+ console.info("Conversation used:", message.conversation)
+ } else if (message.type == "provider") {
+ provider_storage[message_id] = message.provider;
+ content_map.content.querySelector('.provider').innerHTML = `
+
+ ${message.provider.label ? message.provider.label : message.provider.name}
+
+ ${message.provider.model ? ' with ' + message.provider.model : ''}
+ `
+ } else if (message.type == "message") {
+ console.error(message.message)
+ } else if (message.type == "error") {
+ error_storage[message_id] = message.error
+ console.error(message.error);
+ content_map.inner.innerHTML += `An error occured: ${message.error}
`;
+ let p = document.createElement("p");
+ p.innerText = message.error;
+ log_storage.appendChild(p);
+ } else if (message.type == "preview") {
+ content_map.inner.innerHTML = markdown_render(message.preview);
+ } else if (message.type == "content") {
+ message_storage[message_id] += message.content;
+ html = markdown_render(message_storage[message_id]);
+ let lastElement, lastIndex = null;
+ for (element of ['', '', '\n\n', '\n', '\n']) {
+ const index = html.lastIndexOf(element)
+ if (index - element.length > lastIndex) {
+ lastElement = element;
+ lastIndex = index;
+ }
+ }
+ if (lastIndex) {
+ html = html.substring(0, lastIndex) + '' + lastElement;
+ }
+ content_map.inner.innerHTML = html;
+ content_map.count.innerText = count_words_and_tokens(message_storage[message_id], provider_storage[message_id]?.model);
+ highlight(content_map.inner);
+ } else if (message.type == "log") {
+ let p = document.createElement("p");
+ p.innerText = message.log;
+ log_storage.appendChild(p);
+ } else if (message.type == "synthesize") {
+ synthesize_storage[message_id] = message.synthesize;
+ }
+ let scroll_down = ()=>{
+ if (message_box.scrollTop >= message_box.scrollHeight - message_box.clientHeight - 100) {
+ window.scrollTo(0, 0);
+ message_box.scrollTo({ top: message_box.scrollHeight, behavior: "auto" });
+ }
+ }
+ if (!content_map.container.classList.contains("regenerate")) {
+ scroll_down();
+ setTimeout(scroll_down, 200);
+ }
+}
+
+cameraInput?.addEventListener("click", (e) => {
+ if (window?.pywebview) {
+ e.preventDefault();
+ pywebview.api.take_picture();
+ }
+});
+
+imageInput?.addEventListener("click", (e) => {
+ if (window?.pywebview) {
+ e.preventDefault();
+ pywebview.api.choose_image();
+ }
+});
+
+const ask_gpt = async (message_id, message_index = -1) => {
+ let messages = await get_messages(window.conversation_id);
+ messages = prepare_messages(messages, message_index);
+ message_storage[message_id] = "";
+ stop_generating.classList.remove("stop_generating-hidden");
+
+ if (message_index == -1) {
+ await scroll_to_bottom();
+ }
+
+ let count_total = message_box.querySelector('.count_total');
+ count_total ? count_total.parentElement.removeChild(count_total) : null;
+
+ const message_el = document.createElement("div");
+ message_el.classList.add("message");
+ if (message_index != -1) {
+ message_el.classList.add("regenerate");
+ }
+ message_el.innerHTML += `
+
+ ${gpt_image}
+
+
+
+
+
+
+
+
+ `;
+ if (message_index == -1) {
+ message_box.appendChild(message_el);
+ } else {
+ parent_message = message_box.querySelector(`.message[data-index="${message_index}"]`);
+ if (!parent_message) {
+ return;
+ }
+ parent_message.after(message_el);
+ }
+
+ controller_storage[message_id] = new AbortController();
+
+ let content_el = document.getElementById(`gpt_${message_id}`)
+ let content_map = content_storage[message_id] = {
+ container: message_el,
+ content: content_el,
+ inner: content_el.querySelector('.content_inner'),
+ count: content_el.querySelector('.count'),
+ }
+ if (message_index == -1) {
+ await scroll_to_bottom();
+ }
+ try {
+ const input = imageInput && imageInput.files.length > 0 ? imageInput : cameraInput;
+ const file = input && input.files.length > 0 ? input.files[0] : null;
+ const provider = providerSelect.options[providerSelect.selectedIndex].value;
+ const auto_continue = document.getElementById("auto_continue")?.checked;
+ const download_images = document.getElementById("download_images")?.checked;
+ let api_key = get_api_key_by_provider(provider);
+ await api("conversation", {
+ id: message_id,
+ conversation_id: window.conversation_id,
+ model: get_selected_model(),
+ web_search: document.getElementById("switch").checked,
+ provider: provider,
+ messages: messages,
+ auto_continue: auto_continue,
+ download_images: download_images,
+ api_key: api_key,
+ }, file, message_id);
+ if (!error_storage[message_id]) {
+ html = markdown_render(message_storage[message_id]);
+ content_map.inner.innerHTML = html;
+ highlight(content_map.inner);
+ if (imageInput) imageInput.value = "";
+ if (cameraInput) cameraInput.value = "";
+ if (fileInput) fileInput.value = "";
+ }
+ } catch (e) {
+ console.error(e);
+ if (e.name != "AbortError") {
+ error_storage[message_id] = true;
+ content_map.inner.innerHTML += `An error occured: ${e}
`;
+ }
+ }
+ delete controller_storage[message_id];
+ if (!error_storage[message_id] && message_storage[message_id]) {
+ const message_provider = message_id in provider_storage ? provider_storage[message_id] : null;
+ await add_message(
+ window.conversation_id,
+ "assistant",
+ message_storage[message_id],
+ message_provider,
+ message_index,
+ synthesize_storage[message_id]
+ );
+ await safe_load_conversation(window.conversation_id, message_index == -1);
+ } else {
+ let cursorDiv = message_el.querySelector(".cursor");
+ if (cursorDiv) cursorDiv.parentNode.removeChild(cursorDiv);
+ }
+ if (message_index == -1) {
+ await scroll_to_bottom();
+ }
+ await safe_remove_cancel_button();
+ await register_message_buttons();
+ await load_conversations();
+ regenerate.classList.remove("regenerate-hidden");
+};
+
+async function scroll_to_bottom() {
+ window.scrollTo(0, 0);
+ message_box.scrollTop = message_box.scrollHeight;
+}
+
+const clear_conversations = async () => {
+ const elements = box_conversations.childNodes;
+ let index = elements.length;
+
+ if (index > 0) {
+ while (index--) {
+ const element = elements[index];
+ if (
+ element.nodeType === Node.ELEMENT_NODE &&
+ element.tagName.toLowerCase() !== `button`
+ ) {
+ box_conversations.removeChild(element);
+ }
+ }
+ }
+};
+
+const clear_conversation = async () => {
+ let messages = message_box.getElementsByTagName(`div`);
+
+ while (messages.length > 0) {
+ message_box.removeChild(messages[0]);
+ }
+};
+
+async function set_conversation_title(conversation_id, title) {
+ conversation = await get_conversation(conversation_id)
+ conversation.new_title = title;
+ appStorage.setItem(
+ `conversation:${conversation.id}`,
+ JSON.stringify(conversation)
+ );
+}
+
+const show_option = async (conversation_id) => {
+ const conv = document.getElementById(`conv-${conversation_id}`);
+ const choi = document.getElementById(`cho-${conversation_id}`);
+
+ conv.style.display = "none";
+ choi.style.display = "block";
+
+ const el = document.getElementById(`convo-${conversation_id}`);
+ const trash_el = el.querySelector(".fa-trash");
+ const title_el = el.querySelector("span.convo-title");
+ if (title_el) {
+ const left_el = el.querySelector(".left");
+ const input_el = document.createElement("input");
+ input_el.value = title_el.innerText;
+ input_el.classList.add("convo-title");
+ input_el.onfocus = () => trash_el.style.display = "none";
+ input_el.onchange = () => set_conversation_title(conversation_id, input_el.value);
+ left_el.removeChild(title_el);
+ left_el.appendChild(input_el);
+ }
+};
+
+const hide_option = async (conversation_id) => {
+ const conv = document.getElementById(`conv-${conversation_id}`);
+ const choi = document.getElementById(`cho-${conversation_id}`);
+
+ conv.style.display = "block";
+ choi.style.display = "none";
+
+ const el = document.getElementById(`convo-${conversation_id}`);
+ el.querySelector(".fa-trash").style.display = "";
+ const input_el = el.querySelector("input.convo-title");
+ if (input_el) {
+ const left_el = el.querySelector(".left");
+ const span_el = document.createElement("span");
+ span_el.innerText = input_el.value;
+ span_el.classList.add("convo-title");
+ span_el.onclick = () => set_conversation(conversation_id);
+ left_el.removeChild(input_el);
+ left_el.appendChild(span_el);
+ }
+};
+
+const delete_conversation = async (conversation_id) => {
+ appStorage.removeItem(`conversation:${conversation_id}`);
+
+ const conversation = document.getElementById(`convo-${conversation_id}`);
+ conversation.remove();
+
+ if (window.conversation_id == conversation_id) {
+ await new_conversation();
+ }
+
+ await load_conversations();
+};
+
+const set_conversation = async (conversation_id) => {
+ history.pushState({}, null, `/chat/${conversation_id}`);
+ window.conversation_id = conversation_id;
+
+ await clear_conversation();
+ await load_conversation(conversation_id);
+ load_conversations();
+ hide_sidebar();
+ log_storage.classList.add("hidden");
+};
+
+const new_conversation = async () => {
+ history.pushState({}, null, `/chat/`);
+ window.conversation_id = uuid();
+
+ await clear_conversation();
+ if (systemPrompt) {
+ systemPrompt.value = "";
+ }
+ load_conversations();
+ hide_sidebar();
+ log_storage.classList.add("hidden");
+ say_hello();
+};
+
+const load_conversation = async (conversation_id, scroll=true) => {
+ let conversation = await get_conversation(conversation_id);
+ let messages = conversation?.items || [];
+
+ if (!conversation) {
+ return;
+ }
+
+ if (systemPrompt) {
+ systemPrompt.value = conversation.system || "";
+ }
+
+ let elements = "";
+ let last_model = null;
+ for (i in messages) {
+ let item = messages[i];
+ last_model = item.provider?.model;
+ let next_i = parseInt(i) + 1;
+ let next_provider = item.provider ? item.provider : (messages.length > next_i ? messages[next_i].provider : null);
+ let provider_label = item.provider?.label ? item.provider.label : item.provider?.name;
+ let provider_link = item.provider?.name ? `${provider_label}` : "";
+ let provider = provider_link ? `
+
+ ${provider_link}
+ ${item.provider.model ? ' with ' + item.provider.model : ''}
+
+ ` : "";
+ let synthesize_params = {text: item.content}
+ let synthesize_provider = "Gemini";
+ if (item.synthesize) {
+ synthesize_params = item.synthesize.data
+ synthesize_provider = item.synthesize.provider;
+ }
+ synthesize_params = (new URLSearchParams(synthesize_params)).toString();
+ let synthesize_url = `/backend-api/v2/synthesize/${synthesize_provider}?${synthesize_params}`;
+
+ elements += `
+
+ `;
+ }
+
+ if (window.GPTTokenizer_cl100k_base) {
+ const filtered = prepare_messages(messages, null);
+ if (filtered.length > 0) {
+ last_model = last_model?.startsWith("gpt-4") ? "gpt-4" : "gpt-3.5-turbo"
+ let count_total = GPTTokenizer_cl100k_base?.encodeChat(filtered, last_model).length
+ if (count_total > 0) {
+ elements += `(${count_total} tokens used)`;
+ }
+ }
+ }
+
+ message_box.innerHTML = elements;
+ register_message_buttons();
+ highlight(message_box);
+ regenerate.classList.remove("regenerate-hidden");
+
+ if (scroll) {
+ message_box.scrollTo({ top: message_box.scrollHeight, behavior: "smooth" });
+
+ setTimeout(() => {
+ message_box.scrollTop = message_box.scrollHeight;
+ }, 500);
+ }
+};
+
+async function safe_load_conversation(conversation_id, scroll=true) {
+ let is_running = false
+ for (const key in controller_storage) {
+ if (!controller_storage[key].signal.aborted) {
+ is_running = true;
+ break
+ }
+ }
+ if (!is_running) {
+ load_conversation(conversation_id, scroll);
+ }
+}
+
+async function get_conversation(conversation_id) {
+ let conversation = await JSON.parse(
+ appStorage.getItem(`conversation:${conversation_id}`)
+ );
+ return conversation;
+}
+
+async function save_conversation(conversation_id, conversation) {
+ conversation.updated = Date.now();
+ appStorage.setItem(
+ `conversation:${conversation_id}`,
+ JSON.stringify(conversation)
+ );
+}
+
+async function get_messages(conversation_id) {
+ const conversation = await get_conversation(conversation_id);
+ return conversation?.items || [];
+}
+
+async function add_conversation(conversation_id, content) {
+ if (appStorage.getItem(`conversation:${conversation_id}`) == null) {
+ await save_conversation(conversation_id, {
+ id: conversation_id,
+ title: "",
+ added: Date.now(),
+ system: systemPrompt?.value,
+ items: [],
+ });
+ }
+ history.pushState({}, null, `/chat/${conversation_id}`);
+}
+
+async function save_system_message() {
+ if (!window.conversation_id) {
+ return;
+ }
+ const conversation = await get_conversation(window.conversation_id);
+ if (conversation) {
+ conversation.system = systemPrompt?.value;
+ await save_conversation(window.conversation_id, conversation);
+ }
+}
+const hide_message = async (conversation_id, message_index =- 1) => {
+ const conversation = await get_conversation(conversation_id)
+ if (!conversation) return;
+ message_index = message_index == -1 ? conversation.items.length - 1 : message_index
+ const last_message = message_index in conversation.items ? conversation.items[message_index] : null;
+ if (last_message !== null) {
+ if (last_message["role"] == "assistant") {
+ last_message["regenerate"] = true;
+ }
+ conversation.items[message_index] = last_message;
+ }
+ await save_conversation(conversation_id, conversation);
+};
+
+const remove_message = async (conversation_id, index) => {
+ const conversation = await get_conversation(conversation_id);
+ let new_items = [];
+ for (i in conversation.items) {
+ if (i == index - 1) {
+ if (!conversation.items[index]?.regenerate) {
+ delete conversation.items[i]["regenerate"];
+ }
+ }
+ if (i != index) {
+ new_items.push(conversation.items[i])
+ }
+ }
+ conversation.items = new_items;
+ await save_conversation(conversation_id, conversation);
+};
+
+const get_message = async (conversation_id, index) => {
+ const messages = await get_messages(conversation_id);
+ if (index in messages)
+ return messages[index]["content"];
+};
+
+const add_message = async (
+ conversation_id, role, content,
+ provider = null,
+ message_index = -1,
+ synthesize_data = null
+) => {
+ const conversation = await get_conversation(conversation_id);
+ if (!conversation) return;
+ const new_message = {
+ role: role,
+ content: content,
+ provider: provider,
+ };
+ if (synthesize_data) {
+ new_message.synthesize = synthesize_data;
+ }
+ if (message_index == -1) {
+ conversation.items.push(new_message);
+ } else {
+ const new_messages = [];
+ conversation.items.forEach((item, index)=>{
+ new_messages.push(item);
+ if (index == message_index) {
+ new_message.regenerate = true;
+ new_messages.push(new_message);
+ }
+ });
+ conversation.items = new_messages;
+ }
+ await save_conversation(conversation_id, conversation);
+ return conversation.items.length - 1;
+};
+
+const load_conversations = async () => {
+ let conversations = [];
+ for (let i = 0; i < appStorage.length; i++) {
+ if (appStorage.key(i).startsWith("conversation:")) {
+ let conversation = appStorage.getItem(appStorage.key(i));
+ conversations.push(JSON.parse(conversation));
+ }
+ }
+ conversations.sort((a, b) => (b.updated||0)-(a.updated||0));
+
+ await clear_conversations();
+
+ let html = "";
+ conversations.forEach((conversation) => {
+ if (conversation?.items.length > 0 && !conversation.new_title) {
+ let new_value = (conversation.items[0]["content"]).trim();
+ let new_lenght = new_value.indexOf("\n");
+ new_lenght = new_lenght > 200 || new_lenght < 0 ? 200 : new_lenght;
+ conversation.new_title = new_value.substring(0, new_lenght);
+ appStorage.setItem(
+ `conversation:${conversation.id}`,
+ JSON.stringify(conversation)
+ );
+ }
+ let updated = "";
+ if (conversation.updated) {
+ const date = new Date(conversation.updated);
+ updated = date.toLocaleString('en-GB', {dateStyle: 'short', timeStyle: 'short', monthStyle: 'short'});
+ updated = updated.replace("/" + date.getFullYear(), "")
+ }
+ html += `
+
+
+
+ ${updated}
+ ${conversation.new_title}
+
+
+
+
+ `;
+ });
+ box_conversations.innerHTML += html;
+};
+
+const hide_input = document.querySelector(".toolbar .hide-input");
+hide_input.addEventListener("click", async (e) => {
+ const icon = hide_input.querySelector("i");
+ const func = icon.classList.contains("fa-angles-down") ? "add" : "remove";
+ const remv = icon.classList.contains("fa-angles-down") ? "remove" : "add";
+ icon.classList[func]("fa-angles-up");
+ icon.classList[remv]("fa-angles-down");
+ document.querySelector(".conversation .user-input").classList[func]("hidden");
+ document.querySelector(".conversation .buttons").classList[func]("hidden");
+});
+
+const uuid = () => {
+ return `xxxxxxxx-xxxx-4xxx-yxxx-${Date.now().toString(16)}`.replace(
+ /[xy]/g,
+ function (c) {
+ var r = (Math.random() * 16) | 0,
+ v = c == "x" ? r : (r & 0x3) | 0x8;
+ return v.toString(16);
+ }
+ );
+};
+
+function get_message_id() {
+ random_bytes = (Math.floor(Math.random() * 1338377565) + 2956589730).toString(
+ 2
+ );
+ unix = Math.floor(Date.now() / 1000).toString(2);
+
+ return BigInt(`0b${unix}${random_bytes}`).toString();
+};
+
+async function hide_sidebar() {
+ sidebar.classList.remove("shown");
+ sidebar_button.classList.remove("rotated");
+ settings.classList.add("hidden");
+ chat.classList.remove("hidden");
+ if (window.location.pathname == "/menu/" || window.location.pathname == "/settings/") {
+ history.back();
+ }
+}
+
+window.addEventListener('popstate', hide_sidebar, false);
+
+sidebar_button.addEventListener("click", (event) => {
+ settings.classList.add("hidden");
+ if (sidebar.classList.contains("shown")) {
+ hide_sidebar();
+ } else {
+ sidebar.classList.add("shown");
+ sidebar_button.classList.add("rotated");
+ history.pushState({}, null, "/menu/");
+ }
+ window.scrollTo(0, 0);
+});
+
+function open_settings() {
+ if (settings.classList.contains("hidden")) {
+ chat.classList.add("hidden");
+ sidebar.classList.remove("shown");
+ settings.classList.remove("hidden");
+ history.pushState({}, null, "/settings/");
+ } else {
+ settings.classList.add("hidden");
+ chat.classList.remove("hidden");
+ }
+ log_storage.classList.add("hidden");
+}
+
+function open_album() {
+ if (album.classList.contains("hidden")) {
+ sidebar.classList.remove("shown");
+ settings.classList.add("hidden");
+ album.classList.remove("hidden");
+ history.pushState({}, null, "/images/");
+ } else {
+ album.classList.add("hidden");
+ }
+}
+
+const register_settings_storage = async () => {
+ optionElements.forEach((element) => {
+ if (element.type == "textarea") {
+ element.addEventListener('input', async (event) => {
+ appStorage.setItem(element.id, element.value);
+ });
+ } else {
+ element.addEventListener('change', async (event) => {
+ switch (element.type) {
+ case "checkbox":
+ appStorage.setItem(element.id, element.checked);
+ break;
+ case "select-one":
+ appStorage.setItem(element.id, element.selectedIndex);
+ break;
+ case "text":
+ case "number":
+ appStorage.setItem(element.id, element.value);
+ break;
+ default:
+ console.warn("Unresolved element type");
+ }
+ });
+ }
+ });
+}
+
+const load_settings_storage = async () => {
+ optionElements.forEach((element) => {
+ if (!(value = appStorage.getItem(element.id))) {
+ return;
+ }
+ if (value) {
+ switch (element.type) {
+ case "checkbox":
+ element.checked = value === "true";
+ break;
+ case "select-one":
+ element.selectedIndex = parseInt(value);
+ break;
+ case "text":
+ case "number":
+ case "textarea":
+ element.value = value;
+ break;
+ default:
+ console.warn("Unresolved element type");
+ }
+ }
+ });
+}
+
+const say_hello = async () => {
+ tokens = [`Hello`, `!`, ` How`,` can`, ` I`,` assist`,` you`,` today`,`?`]
+
+ message_box.innerHTML += `
+
+ `;
+
+ to_modify = document.querySelector(`.welcome-message`);
+ for (token of tokens) {
+ await new Promise(resolve => setTimeout(resolve, (Math.random() * (100 - 200) + 100)))
+ to_modify.textContent += token;
+ }
+}
+
+// Theme storage for recurring viewers
+const storeTheme = function (theme) {
+ appStorage.setItem("theme", theme);
+};
+
+// set theme when visitor returns
+const setTheme = function () {
+ const activeTheme = appStorage.getItem("theme");
+ colorThemes.forEach((themeOption) => {
+ if (themeOption.id === activeTheme) {
+ themeOption.checked = true;
+ }
+ });
+ // fallback for no :has() support
+ document.documentElement.className = activeTheme;
+};
+
+colorThemes.forEach((themeOption) => {
+ themeOption.addEventListener("click", () => {
+ storeTheme(themeOption.id);
+ // fallback for no :has() support
+ document.documentElement.className = themeOption.id;
+ });
+});
+
+function count_tokens(model, text) {
+ if (model) {
+ if (window.llamaTokenizer)
+ if (model.startsWith("llama") || model.startsWith("codellama")) {
+ return llamaTokenizer.encode(text).length;
+ }
+ if (window.mistralTokenizer)
+ if (model.startsWith("mistral") || model.startsWith("mixtral")) {
+ return mistralTokenizer.encode(text).length;
+ }
+ }
+ if (window.GPTTokenizer_cl100k_base) {
+ return GPTTokenizer_cl100k_base.encode(text).length;
+ }
+}
+
+function count_words(text) {
+ return text.trim().match(/[\w\u4E00-\u9FA5]+/gu)?.length || 0;
+}
+
+function count_chars(text) {
+ return text.match(/[^\s\p{P}]/gu)?.length || 0;
+}
+
+function count_words_and_tokens(text, model) {
+ text = filter_message(text);
+ return `(${count_words(text)} words, ${count_chars(text)} chars, ${count_tokens(model, text)} tokens)`;
+}
+
+let countFocus = messageInput;
+let timeoutId;
+const count_input = async () => {
+ if (timeoutId) clearTimeout(timeoutId);
+ timeoutId = setTimeout(() => {
+ if (countFocus.value) {
+ inputCount.innerText = count_words_and_tokens(countFocus.value, get_selected_model());
+ } else {
+ inputCount.innerText = "";
+ }
+ }, 100);
+};
+messageInput.addEventListener("keyup", count_input);
+systemPrompt.addEventListener("keyup", count_input);
+systemPrompt.addEventListener("focus", function() {
+ countFocus = systemPrompt;
+ count_input();
+});
+systemPrompt.addEventListener("input", function() {
+ countFocus = messageInput;
+ count_input();
+});
+
+window.addEventListener('load', async function() {
+ await on_load();
+ if (window.conversation_id == "{{chat_id}}") {
+ window.conversation_id = uuid();
+ } else {
+ await on_api();
+ }
+});
+
+window.addEventListener('pywebviewready', async function() {
+ await on_api();
+});
+
+async function on_load() {
+ setTheme();
+ count_input();
+
+ if (/\/chat\/.+/.test(window.location.href)) {
+ load_conversation(window.conversation_id);
+ } else {
+ say_hello()
+ }
+ load_conversations();
+}
+
+async function on_api() {
+ let prompt_lock = false;
+ messageInput.addEventListener("keydown", async (evt) => {
+ if (prompt_lock) return;
+
+ // If not mobile
+ if (!window.matchMedia("(pointer:coarse)").matches)
+ if (evt.keyCode === 13 && !evt.shiftKey) {
+ evt.preventDefault();
+ console.log("pressed enter");
+ prompt_lock = true;
+ setTimeout(()=>prompt_lock=false, 3000);
+ await handle_ask();
+ } else {
+ messageInput.style.removeProperty("height");
+ messageInput.style.height = messageInput.scrollHeight + "px";
+ }
+ });
+ sendButton.addEventListener(`click`, async () => {
+ console.log("clicked send");
+ if (prompt_lock) return;
+ prompt_lock = true;
+ setTimeout(()=>prompt_lock=false, 3000);
+ await handle_ask();
+ });
+ messageInput.focus();
+
+ register_settings_storage();
+
+ models = await api("models");
+ models.forEach((model) => {
+ let option = document.createElement("option");
+ option.value = option.text = model;
+ modelSelect.appendChild(option);
+ });
+
+ providers = await api("providers")
+ Object.entries(providers).forEach(([provider, label]) => {
+ let option = document.createElement("option");
+ option.value = provider;
+ option.text = label;
+ providerSelect.appendChild(option);
+ })
+
+ await load_settings_storage()
+ await load_provider_models(appStorage.getItem("provider"));
+
+ const hide_systemPrompt = document.getElementById("hide-systemPrompt")
+ const slide_systemPrompt_icon = document.querySelector(".slide-systemPrompt i");
+ if (hide_systemPrompt.checked) {
+ systemPrompt.classList.add("hidden");
+ slide_systemPrompt_icon.classList.remove("fa-angles-up");
+ slide_systemPrompt_icon.classList.add("fa-angles-down");
+ }
+ hide_systemPrompt.addEventListener('change', async (event) => {
+ if (event.target.checked) {
+ systemPrompt.classList.add("hidden");
+ } else {
+ systemPrompt.classList.remove("hidden");
+ }
+ });
+ document.querySelector(".slide-systemPrompt")?.addEventListener("click", () => {
+ hide_systemPrompt.click();
+ let checked = hide_systemPrompt.checked;
+ systemPrompt.classList[checked ? "add": "remove"]("hidden");
+ slide_systemPrompt_icon.classList[checked ? "remove": "add"]("fa-angles-up");
+ slide_systemPrompt_icon.classList[checked ? "add": "remove"]("fa-angles-down");
+ });
+ const messageInputHeight = document.getElementById("message-input-height");
+ if (messageInputHeight) {
+ if (messageInputHeight.value) {
+ messageInput.style.maxHeight = `${messageInputHeight.value}px`;
+ }
+ messageInputHeight.addEventListener('change', async () => {
+ messageInput.style.maxHeight = `${messageInputHeight.value}px`;
+ });
+ }
+ const darkMode = document.getElementById("darkMode");
+ if (darkMode) {
+ if (!darkMode.checked) {
+ document.body.classList.add("white");
+ }
+ darkMode.addEventListener('change', async (event) => {
+ if (event.target.checked) {
+ document.body.classList.remove("white");
+ } else {
+ document.body.classList.add("white");
+ }
+ });
+ }
+}
+
+async function load_version() {
+ const versions = await api("version");
+ document.title = 'g4f - ' + versions["version"];
+ let text = "version ~ "
+ if (versions["version"] != versions["latest_version"]) {
+ let release_url = 'https://github.com/xtekky/gpt4free/releases/latest';
+ let title = `New version: ${versions["latest_version"]}`;
+ text += `${versions["version"]} š`;
+ const new_version = document.createElement("div");
+ new_version.classList.add("new_version");
+ const link = `v${versions["latest_version"]}`;
+ new_version.innerHTML = `g4f ${link} š`;
+ new_version.addEventListener("click", ()=>new_version.parentElement.removeChild(new_version));
+ document.body.appendChild(new_version);
+ } else {
+ text += versions["version"];
+ }
+ document.getElementById("version_text").innerHTML = text
+}
+setTimeout(load_version, 100);
+
+[imageInput, cameraInput].forEach((el) => {
+ el.addEventListener('click', async () => {
+ el.value = '';
+ if (imageInput.dataset.src) {
+ URL.revokeObjectURL(imageInput.dataset.src);
+ delete imageInput.dataset.src
+ }
+ });
+});
+
+fileInput.addEventListener('click', async (event) => {
+ fileInput.value = '';
+ delete fileInput.dataset.text;
+});
+
+async function upload_cookies() {
+ const file = fileInput.files[0];
+ const formData = new FormData();
+ formData.append('file', file);
+ response = await fetch("/backend-api/v2/upload_cookies", {
+ method: 'POST',
+ body: formData,
+ });
+ if (response.status == 200) {
+ inputCount.innerText = `${file.name} was uploaded successfully`;
+ }
+ fileInput.value = "";
+}
+
+fileInput.addEventListener('change', async (event) => {
+ if (fileInput.files.length) {
+ type = fileInput.files[0].name.split('.').pop()
+ if (type == "har") {
+ return await upload_cookies();
+ }
+ fileInput.dataset.type = type
+ const reader = new FileReader();
+ reader.addEventListener('load', async (event) => {
+ fileInput.dataset.text = event.target.result;
+ if (type == "json") {
+ const data = JSON.parse(fileInput.dataset.text);
+ if ("g4f" in data.options) {
+ let count = 0;
+ Object.keys(data).forEach(key => {
+ if (key != "options" && !localStorage.getItem(key)) {
+ appStorage.setItem(key, JSON.stringify(data[key]));
+ count += 1;
+ }
+ });
+ delete fileInput.dataset.text;
+ await load_conversations();
+ fileInput.value = "";
+ inputCount.innerText = `${count} Conversations were imported successfully`;
+ } else {
+ await upload_cookies();
+ }
+ }
+ });
+ reader.readAsText(fileInput.files[0]);
+ } else {
+ delete fileInput.dataset.text;
+ }
+});
+
+systemPrompt?.addEventListener("input", async () => {
+ await save_system_message();
+});
+
+function get_selected_model() {
+ if (modelProvider.selectedIndex >= 0) {
+ return modelProvider.options[modelProvider.selectedIndex].value;
+ } else if (modelSelect.selectedIndex >= 0) {
+ return modelSelect.options[modelSelect.selectedIndex].value;
+ }
+}
+
+async function api(ressource, args=null, file=null, message_id=null) {
+ if (window?.pywebview) {
+ if (args !== null) {
+ if (ressource == "models") {
+ ressource = "provider_models";
+ }
+ return pywebview.api[`get_${ressource}`](args);
+ }
+ return pywebview.api[`get_${ressource}`]();
+ }
+ let api_key;
+ if (ressource == "models" && args) {
+ api_key = get_api_key_by_provider(args);
+ ressource = `${ressource}/${args}`;
+ }
+ const url = `/backend-api/v2/${ressource}`;
+ const headers = {};
+ if (api_key) {
+ headers.authorization = `Bearer ${api_key}`;
+ }
+ if (ressource == "conversation") {
+ let body = JSON.stringify(args);
+ headers.accept = 'text/event-stream';
+ if (file !== null) {
+ const formData = new FormData();
+ formData.append('file', file);
+ formData.append('json', body);
+ body = formData;
+ } else {
+ headers['content-type'] = 'application/json';
+ }
+ response = await fetch(url, {
+ method: 'POST',
+ signal: controller_storage[message_id].signal,
+ headers: headers,
+ body: body,
+ });
+ return read_response(response, message_id);
+ }
+ response = await fetch(url, {headers: headers});
+ return await response.json();
+}
+
+async function read_response(response, message_id) {
+ const reader = response.body.pipeThrough(new TextDecoderStream()).getReader();
+ let buffer = ""
+ while (true) {
+ const { value, done } = await reader.read();
+ if (done) {
+ break;
+ }
+ for (const line of value.split("\n")) {
+ if (!line) {
+ continue;
+ }
+ try {
+ add_message_chunk(JSON.parse(buffer + line), message_id);
+ buffer = "";
+ } catch {
+ buffer += line
+ }
+ }
+ }
+}
+
+function get_api_key_by_provider(provider) {
+ let api_key = null;
+ if (provider) {
+ api_key = document.getElementById(`${provider}-api_key`)?.value || null;
+ if (api_key == null)
+ api_key = document.querySelector(`.${provider}-api_key`)?.value || null;
+ }
+ return api_key;
+}
+
+async function load_provider_models(providerIndex=null) {
+ if (!providerIndex) {
+ providerIndex = providerSelect.selectedIndex;
+ }
+ modelProvider.innerHTML = '';
+ const provider = providerSelect.options[providerIndex].value;
+ if (!provider) {
+ modelProvider.classList.add("hidden");
+ modelSelect.classList.remove("hidden");
+ return;
+ }
+ const models = await api('models', provider);
+ if (models.length > 0) {
+ modelSelect.classList.add("hidden");
+ modelProvider.classList.remove("hidden");
+ models.forEach((model) => {
+ let option = document.createElement('option');
+ option.value = model.model;
+ option.text = `${model.model}${model.image ? " (Image Generation)" : ""}${model.vision ? " (Image Upload)" : ""}`;
+ option.selected = model.default;
+ modelProvider.appendChild(option);
+ });
+ } else {
+ modelProvider.classList.add("hidden");
+ modelSelect.classList.remove("hidden");
+ }
+};
+providerSelect.addEventListener("change", () => load_provider_models());
+
+function save_storage() {
+ let filename = `chat ${new Date().toLocaleString()}.json`.replaceAll(":", "-");
+ let data = {"options": {"g4f": ""}};
+ for (let i = 0; i < appStorage.length; i++) {
+ let key = appStorage.key(i);
+ let item = appStorage.getItem(key);
+ if (key.startsWith("conversation:")) {
+ data[key] = JSON.parse(item);
+ } else if (!key.includes("api_key")) {
+ data["options"][key] = item;
+ }
+ }
+ data = JSON.stringify(data, null, 4);
+ const blob = new Blob([data], {type: 'application/json'});
+ if(window.navigator.msSaveOrOpenBlob) {
+ window.navigator.msSaveBlob(blob, filename);
+ } else{
+ const elem = window.document.createElement('a');
+ elem.href = window.URL.createObjectURL(blob);
+ elem.download = filename;
+ document.body.appendChild(elem);
+ elem.click();
+ document.body.removeChild(elem);
+ }
+}
+
+const SpeechRecognition = window.SpeechRecognition || window.webkitSpeechRecognition;
+if (SpeechRecognition) {
+ const mircoIcon = microLabel.querySelector("i");
+ mircoIcon.classList.add("fa-microphone");
+ mircoIcon.classList.remove("fa-microphone-slash");
+
+ const recognition = new SpeechRecognition();
+ recognition.continuous = true;
+ recognition.interimResults = true;
+ recognition.maxAlternatives = 1;
+
+ let startValue;
+ let lastDebounceTranscript;
+ recognition.onstart = function() {
+ microLabel.classList.add("recognition");
+ startValue = messageInput.value;
+ lastDebounceTranscript = "";
+ };
+ recognition.onend = function() {
+ messageInput.focus();
+ };
+ recognition.onresult = function(event) {
+ if (!event.results) {
+ return;
+ }
+ let result = event.results[event.resultIndex];
+ let isFinal = result.isFinal && (result[0].confidence > 0);
+ let transcript = result[0].transcript;
+ if (isFinal) {
+ if(transcript == lastDebounceTranscript) {
+ return;
+ }
+ lastDebounceTranscript = transcript;
+ }
+ if (transcript) {
+ messageInput.value = `${startValue ? startValue+"\n" : ""}${transcript.trim()}`;
+ if (isFinal) {
+ startValue = messageInput.value;
+ }
+ messageInput.style.height = messageInput.scrollHeight + "px";
+ messageInput.scrollTop = messageInput.scrollHeight;
+ }
+ };
+
+ microLabel.addEventListener("click", () => {
+ if (microLabel.classList.contains("recognition")) {
+ recognition.stop();
+ microLabel.classList.remove("recognition");
+ } else {
+ const lang = document.getElementById("recognition-language")?.value;
+ recognition.lang = lang || navigator.language;
+ recognition.start();
+ }
+ });
+}
+
+document.getElementById("showLog").addEventListener("click", ()=> {
+ log_storage.classList.remove("hidden");
+ settings.classList.add("hidden");
+ log_storage.scrollTop = log_storage.scrollHeight;
+});
\ No newline at end of file
diff --git a/g4f/gui/client/static/js/highlight.min.js b/g4f/gui/client/static/js/highlight.min.js
new file mode 100644
index 0000000000000000000000000000000000000000..d410b45b38119606525a0a7c0c60c428c5ee6eb7
--- /dev/null
+++ b/g4f/gui/client/static/js/highlight.min.js
@@ -0,0 +1 @@
+var hljs=function(){"use strict";var e={exports:{}};function n(e){return e instanceof Map?e.clear=e.delete=e.set=()=>{throw Error("map is read-only")}:e instanceof Set&&(e.add=e.clear=e.delete=()=>{throw Error("set is read-only")}),Object.freeze(e),Object.getOwnPropertyNames(e).forEach(t=>{var a=e[t];"object"!=typeof a||Object.isFrozen(a)||n(a)}),e}e.exports=n,e.exports.default=n;class t{constructor(e){void 0===e.data&&(e.data={}),this.data=e.data,this.isMatchIgnored=!1}ignoreMatch(){this.isMatchIgnored=!0}}function a(e){return e.replace(/&/g,"&").replace(//g,">").replace(/"/g,""").replace(/'/g,"'")}function i(e,...n){let t=Object.create(null);for(let a in e)t[a]=e[a];return n.forEach(e=>{for(let n in e)t[n]=e[n]}),t}let r=e=>!!e.scope||e.sublanguage&&e.language;class s{constructor(e,n){this.buffer="",this.classPrefix=n.classPrefix,e.walk(this)}addText(e){this.buffer+=a(e)}openNode(e){if(!r(e))return;let 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diff --git a/g4f/gui/client/static/js/highlightjs-copy.min.js b/g4f/gui/client/static/js/highlightjs-copy.min.js
new file mode 100644
index 0000000000000000000000000000000000000000..cd8ae9573b2b39e237f0f6405800cf655b3f4d02
--- /dev/null
+++ b/g4f/gui/client/static/js/highlightjs-copy.min.js
@@ -0,0 +1,54 @@
+class CopyButtonPlugin {
+ constructor(options = {}) {
+ self.hook = options.hook;
+ self.callback = options.callback
+ }
+ "after:highlightElement"({
+ el,
+ text
+ }) {
+ let button = Object.assign(document.createElement("button"), {
+ innerHTML: "Copy",
+ className: "hljs-copy-button"
+ });
+ button.dataset.copied = false;
+ el.parentElement.classList.add("hljs-copy-wrapper");
+ el.parentElement.appendChild(button);
+ el.parentElement.style.setProperty("--hljs-theme-background", window.getComputedStyle(el).backgroundColor);
+ button.onclick = async () => {
+ let newText = text;
+ if (hook && typeof hook === "function") {
+ newText = hook(text, el) || text
+ }
+ try {
+ if (!navigator.clipboard) {
+ throw new Error("navigator.clipboard: Clipboard API unavailable.");
+ }
+ await navigator.clipboard.writeText(newText);
+ } catch (e) {
+ console.error(e);
+ console.error("Clipboard API writeText() failed! Fallback to document.exec(\"copy\")...");
+ fallback_clipboard(newText);
+ }
+ button.innerHTML = "Copied!";
+ button.dataset.copied = true;
+ let alert = Object.assign(document.createElement("div"), {
+ role: "status",
+ className: "hljs-copy-alert",
+ innerHTML: "Copied to clipboard"
+ });
+ el.parentElement.appendChild(alert);
+ setTimeout(() => {
+ button.innerHTML = "Copy";
+ button.dataset.copied = false;
+ el.parentElement.removeChild(alert);
+ alert = null
+ }, 2e3)
+ }
+
+
+ if (typeof callback === "function") return callback(newText, el);
+
+ }
+
+}
diff --git a/g4f/gui/client/static/js/icons.js b/g4f/gui/client/static/js/icons.js
new file mode 100644
index 0000000000000000000000000000000000000000..84fed38dd35e0d0203370a8314a360d27f350dd6
--- /dev/null
+++ b/g4f/gui/client/static/js/icons.js
@@ -0,0 +1 @@
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int b = index / ${s};\n index -= b * ${s};\n\n // reverse r and c order for packed texture\n int r = imod(index, ${r}) * 2;\n int c = 2 * (index / ${r});\n\n return ivec3(b, r, c);\n }\n `;return new i.GlslLibRoutine(o)}getOutputPackedNDCoords(t,e){const n=[e[0],e[1]],r=Math.ceil(t[t.length-1]/2),s=r*Math.ceil(t[t.length-2]/2);let o=s,a="",u="b, r, c";for(let e=2;e=0;--e)s[e]=s[e+1]*t[e+1];const o=["r","c","d"],a=s.map(((t,e)=>`int ${o[e]} = index / ${t}; ${e===s.length-1?`int ${o[e+1]} = index - ${o[e]} * ${t}`:`index -= ${o[e]} * ${t}`};`)).join("");return n=`\n ivec3 getOutputCoords() {\n ivec2 resTexRC = ivec2(TexCoords.xy *\n vec2(${e[0]}, ${e[1]}));\n int index = resTexRC.y * ${e[0]} + resTexRC.x;\n ${a}\n return ivec3(r, c, d);\n }\n `,new i.GlslLibRoutine(n)}getOutputUnpacked4DCoords(t,e){let n="";const r=t.length;let s=null;r<2&&(s=[]),s=new Array(r-1),s[r-2]=t[r-1];for(let e=r-3;e>=0;--e)s[e]=s[e+1]*t[e+1];const o=["r","c","d","d2"],a=s.map(((t,e)=>`int ${o[e]} = index / 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texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n }\n "),e="packedUVfrom2D",t[e]=new i.GlslLibRoutine("\n vec2 packedUVfrom2D(int texNumR, int texNumC, int texelsInLogicalRow, int row, int col) {\n int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = texelIndex / texNumC;\n int texC = texelIndex - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n }\n "),e="packedUVfrom3D",t[e]=new i.GlslLibRoutine("\n vec2 packedUVfrom3D(int texNumR, int texNumC,\n int texelsInBatch, int texelsInLogicalRow, int b,\n int row, int col) {\n int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n int texR = index / texNumC;\n int texC = index - texR * texNumC;\n return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n }\n "),e="sampleTexture";const n=(0,s.getGlsl)(this.context.glContext.version);return t[e]=new i.GlslLibRoutine(`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${n.texture2D}(textureSampler, uv).r;\n }`),t}getInputsSamplingSnippets(){const t={},e=this.context.outputTextureLayout;return this.context.programInfo.inputNames.forEach(((n,r)=>{const i=this.context.inputTextureLayouts[r],s=(0,a.generateShaderFuncNameFromInputSamplerName)(n);i.isPacked?t[s]=this.getPackedSamplerFromInput(s,n,i):t[s]=this.getUnpackedSamplerFromInput(s,n,i);const o=(0,a.generateShaderFuncNameFromInputSamplerNameAtOutCoords)(n);i.unpackedShape.length<=e.unpackedShape.length&&(i.isPacked?t[o]=this.getPackedSamplerAtOutputCoords(o,i,e,n):t[o]=this.getUnpackedSamplerAtOutputCoords(o,i,e,n))})),t}getPackedSamplerAtOutputCoords(t,e,n,s){const o=e.unpackedShape,u=n.unpackedShape,l=s,c=(0,a.generateShaderFuncNameFromInputSamplerName)(l),d=o.length,h=u.length,p=r.BroadcastUtil.getBroadcastDims(o,u),f=(0,a.getCoordsDataType)(h),g=h-d;let m;const _=(0,a.getGlChannels)();m=0===d?"":h<2&&p.length>=1?"coords = 0;":p.map((t=>`coords.${_[t+g]} = 0;`)).join("\n");let b="";b=h<2&&d>0?"coords":o.map(((t,e)=>`coords.${_[e+g]}`)).join(", ");let y="return outputValue;";const w=1===r.ShapeUtil.size(o),v=1===r.ShapeUtil.size(u);if(1!==d||w||v){if(w&&!v)y=1===h?"\n return vec4(outputValue.x, outputValue.x, 0., 0.);\n ":"\n return vec4(outputValue.x);\n ";else if(p.length){const t=d-2,e=d-1;p.indexOf(t)>-1&&p.indexOf(e)>-1?y="return vec4(outputValue.x);":p.indexOf(t)>-1?y="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":p.indexOf(e)>-1&&(y="return vec4(outputValue.xx, outputValue.zz);")}}else y="\n return vec4(outputValue.xy, outputValue.xy);\n ";const x=`\n vec4 ${t}() {\n ${f} coords = getOutputCoords();\n \n int lastDim = coords.${_[h-1]};\n coords.${_[h-1]} = coords.${_[h-2]};\n coords.${_[h-2]} = lastDim;\n \n ${m}\n vec4 outputValue = ${c}(${b});\n ${y}\n }\n `;return new i.GlslLibRoutine(x,["coordinates.getOutputCoords"])}getUnpackedSamplerAtOutputCoords(t,e,n,s){const 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output_channel = coords.y;\n ivec2 xRCCorner = coords.zw * strides - pads;\n int group_id = output_channel / ${h};\n\n float value = 0.0;\n for (int wInChannel = 0; wInChannel < ${d[1]}; wInChannel++) {\n int input_channel = group_id * ${d[1]} + wInChannel;\n for (int wHeight = 0; wHeight < ${d[2]}; wHeight++) {\n int xHeight = xRCCorner.x + wHeight * ${u.dilations[0]};\n\n if (xHeight < 0 || xHeight >= ${c[2]}) {\n continue;\n }\n\n for (int wWidth = 0; wWidth < ${d[3]}; wWidth++) {\n int xWidth = xRCCorner.y + wWidth * ${u.dilations[1]};\n if (xWidth < 0 || xWidth >= ${c[3]}) {\n continue;\n }\n\n float xVal = getX(batch, input_channel, xWidth, xHeight);\n float wVal = getW(output_channel, wInChannel, wWidth, wHeight);\n value += xVal*wVal;\n }\n }\n }\n ${l}\n ${m}\n ${f.output} = vec4(value, .0, .0, .0);\n }\n`;return Object.assign(Object.assign({},n),{output:{dims:p,type:e[0].type,textureType:s.TextureType.unpacked},shaderSource:_,hasMain:!0})})(t,e,u,n)})}},1386:(t,e,n)=>{"use 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r=n(246),i=n(5060),s=n(2039),o=n(2823),a=(t,e,n,r,i,s)=>(t-1)*e+n+(r-1)*i+1-s,u=(t,e,n,r,i)=>{const s=Math.floor(t/2);"SAME_UPPER"===e?(n[r]=s,n[i]=t-s):"SAME_LOWER"===e&&(n[r]=t-s,n[i]=s)};e.convTranspose=(t,e,n)=>(h(e,n),l(t,e,n));const l=(t,e,n)=>{const r=d(n,e);return[c(t,e,r)]},c=(t,e,n)=>t.run(((t,e,n)=>{const r=(a=e.length>2,u=n.cacheKey,{name:"ConvTranspose",inputNames:a?["X","W","B"]:["X","W"],inputTypes:a?[s.TextureType.unpacked,s.TextureType.unpacked,s.TextureType.unpacked]:[s.TextureType.unpacked,s.TextureType.unpacked],cacheHint:u});var a,u;return Object.assign(Object.assign({},r),{get:()=>((t,e,n,r)=>{const a=e.length>2?"getB(output_channel)":"0.0",u=e[0].dims,l=e[1].dims,c=l[1],d=l[0]/r.group,h=[e[0].dims[0],e[1].dims[1]*r.group,...r.outputShape],p=(0,i.getGlsl)(t.session.backend.glContext.version),{activationFunction:f,applyActivation:g}=(0,o.getActivationSnippet)(r),m=`\n const ivec2 strides = ivec2(${r.strides[0]}, ${r.strides[1]});\n const ivec2 pads = ivec2(${r.pads[0]}, ${r.pads[1]});\n ${f}\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int output_channel = coords.y;\n\n ivec2 loc = coords.zw + pads;\n\n int group_id = output_channel / ${c};\n int wOutChannel = output_channel - group_id * ${c};\n\n float value = ${a};\n for (int inChannelOffset = 0; inChannelOffset < ${d}; inChannelOffset++) {\n int input_channel = group_id * ${d} + inChannelOffset;\n for (int wWOff = 0; wWOff < ${l[2]}; wWOff++) {\n for (int wHOff = 0; wHOff < ${l[3]}; wHOff++) {\n ivec2 wOff = ivec2(wWOff * ${r.dilations[0]}, wHOff * ${r.dilations[1]});\n ivec2 wLoc = loc - wOff;\n ivec2 wLocIn = wLoc / strides;\n if (\n wLocIn * strides == wLoc &&\n wLocIn.x >= 0 && wLocIn.x < ${u[2]} &&\n wLocIn.y >= 0 && wLocIn.y < ${u[3]}\n ) {\n float xVal = getX(batch, input_channel, wLocIn.y, wLocIn.x);\n float wVal = getW(input_channel, wOutChannel, wHOff, wWOff);\n value += xVal * wVal;\n }\n }\n }\n }\n ${g}\n ${p.output} = vec4(value, .0, .0, .0);\n }\n`;return Object.assign(Object.assign({},n),{output:{dims:h,type:e[0].type,textureType:s.TextureType.unpacked},shaderSource:m,hasMain:!0})})(t,e,r,n)})})(t,e,n),e),d=(t,e)=>{const n=t.kernelShape.slice();if(0===t.kernelShape.length)for(let t=2;t{const c=t.length-2,d=0===l.length;for(let h=0;h{const e=t.attributes,n=(0,o.parseInternalActivationAttributes)(e),i=e.getString("auto_pad","NOTSET"),s=e.getInts("dilations",[1,1]),a=e.getInt("group",1),u=e.getInts("kernel_shape",[]),l=e.getInts("output_padding",[0,0]),c=e.getInts("output_shape",[]),d=e.getInts("pads",[0,0,0,0]),h=e.getInts("strides",[1,1]);return(0,r.createAttributeWithCacheKey)(Object.assign({autoPad:i,dilations:s,group:a,kernelShape:u,outputPadding:l,outputShape:c,pads:d,strides:h},n))};const h=(t,e)=>{if(!t||2!==t.length&&3!==t.length)throw new Error("Conv requires 2 or 3 inputs");if(4!==t[0].dims.length||4!==t[1].dims.length)throw new Error("currently only support 2-dimensional conv");if(t[0].dims[1]!==t[1].dims[0])throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");const n=t[1].dims[1]*e.group;if(3===t.length&&(1!==t[2].dims.length||t[2].dims[0]!==n))throw new Error("invalid bias");const r=t[0].dims.length-2;if(e.dilations.length!==r)throw new Error(`dilations should be ${r}D`);if(e.strides.length!==r)throw new Error(`strides should be ${r}D`);if(e.pads.length!==2*r)throw new Error(`pads should be ${2*r}D`);if(e.outputPadding.length!==r)throw new Error(`output_padding should be ${r}D`);if(0!==e.kernelShape.length&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape");if(0!==e.outputShape.length&&e.outputShape.length!==t[0].dims.length-2)throw new Error("invalid output shape");if("float32"!==t[0].type||"float32"!==t[1].type)throw new Error("ConvTranspose input(X,W) should be float tensor");if(3===t.length&&"float32"!==t[2].type)throw new Error("ConvTranspose input(bias) should be float tensor")}},8138:(t,e,n)=>{"use strict";Object.defineProperty(e,"__esModule",{value:!0}),e.parseConvAttributes=e.conv=e.calculateOutputShape=void 0;const r=n(246),i=n(2517),s=n(4770),o=n(1386),a=n(9828),u=n(2823),l=n(3248),c=n(5623);e.calculateOutputShape=(t,e,n,r,i)=>{const s=t[0],o=t.slice(2),a=o.length,u=e[0],l=e.slice(2).map(((t,e)=>t+(t-1)*(n[e]-1))),c=o.map(((t,e)=>t+r[e]+r[e+a])).map(((t,e)=>Math.floor((t-l[e]+i[e])/i[e])));return[s,u].concat(...c)},e.conv=(t,e,n)=>(g(e,n),d(t,e,n));const d=(t,e,n)=>{const r=f(n,e),i=t.session.pack,a=1===r.kernelShape[0]&&1===r.kernelShape[1];return r.group>1?[t.run((0,s.createUnpackedGroupedConvProgramInfoLoader)(t,e,r),e)]:a&&i?[h(t,e,r)]:i&&4===e[0].dims.length&&1===e[0].dims[0]&&!a?[(0,o.conv2DPacked)(t,e,r)]:[p(t,e,r)]},h=(t,n,r)=>{const i=n[0].dims,s=n[1].dims,o=(0,e.calculateOutputShape)(i,s,r.dilations,r.pads,r.strides),a=t.reshapeUnpacked(n[0],[i[1],i[2]*i[3]]),u=t.reshapeUnpacked(n[1],[s[0],s[1]]),l=n.length>2?[u,a,n[2]]:[u,a],d=t.run((0,c.createMatmulProgramInfoLoader)(l,r),l);return t.reshapeUnpacked(d,o)},p=(t,n,r)=>{const i=n[0].dims,s=n[1].dims,o=(0,e.calculateOutputShape)(i,s,r.dilations,r.pads,r.strides),u=t.run((0,l.createIm2ColProgramInfoLoader)(t,n[0],n[1],o,r),[n[0]]),c=3===n.length?[u,n[1],n[2]]:[u,n[1]];return t.run((0,a.createDotProductProgramInfoLoader)(t,n,o,r),c)},f=(t,e)=>{const n=t.kernelShape.slice();if(0===t.kernelShape.length)for(let t=2;t{const 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${2*n}D`);if(0!==e.kernelShape.length&&e.kernelShape.length!==t[1].dims.length-2)throw new Error("invalid kernel shape");if("float32"!==t[0].type||"float32"!==t[1].type)throw new Error("Conv input(X,W) should be float tensor");if(3===t.length&&"float32"!==t[2].type)throw new Error("Conv input(bias) should be float tensor")}},5193:(t,e,n)=>{"use strict";Object.defineProperty(e,"__esModule",{value:!0}),e.parseDepthToSpaceAttributes=e.depthToSpace=void 0;const r=n(3738);e.depthToSpace=(t,e,n)=>{i(e);const s=n.blocksize,o=s*s,a="DCR"===n.mode?[0,3,4,1,5,2]:[0,1,4,2,5,3],u="DCR"===n.mode?[e[0].dims[0],s,s,e[0].dims[1]/o,e[0].dims[2],e[0].dims[3]]:[e[0].dims[0],e[0].dims[1]/o,s,s,e[0].dims[2],e[0].dims[3]],l=t.reshapeUnpacked(e[0],u),c={perm:a,cacheKey:`${a}`},[d]=(0,r.transpose)(t,[l],c),h=[e[0].dims[0],e[0].dims[1]/o,e[0].dims[2]*s,e[0].dims[3]*s];return[t.reshapeUnpacked(d,h)]},e.parseDepthToSpaceAttributes=t=>{const e=t.attributes.getInt("blocksize");if(e<1)throw new Error(`blocksize must be >= 1, but got : ${e} for DepthToSpace`);const n=t.attributes.getString("mode","DCR");if("DCR"!==n&&"CRD"!==n)throw new Error(`unrecognized mode: ${n} for DepthToSpace`);return{mode:n,blocksize:e}};const i=t=>{if(1!==t.length)throw new Error(`DepthToSpace expect 1 inputs, but got ${t.length}`);if("string"===t[0].type||4!==t[0].dims.length)throw new TypeError("DepthToSpace input should be a 4-D numeric tensor")}},9828:(t,e,n)=>{"use strict";Object.defineProperty(e,"__esModule",{value:!0}),e.createDotProductProgramInfoLoader=void 0;const r=n(2517),i=n(5060),s=n(2039),o=n(2823),a=n(3248);e.createDotProductProgramInfoLoader=(t,e,n,u)=>{const l=((t,e)=>({name:"ConvDotProduct",inputNames:t?["Im2Col","K","B"]:["Im2Col","K"],inputTypes:t?[s.TextureType.unpacked,s.TextureType.packedLastDimension,s.TextureType.unpacked]:[s.TextureType.unpacked,s.TextureType.packedLastDimension],cacheKey:e.activationCacheKey}))(e.length>2,u);return Object.assign(Object.assign({},l),{get:()=>((t,e,n,u,l)=>{const c=n[0].dims,d=n[1].dims,h=[d[0],Math.ceil(c[1]*d[2]*d[3]/4)],p=(0,a.calculateIm2ColDims)(c,d,u),[f,g]=t.calculateTextureWidthAndHeight(h,s.TextureType.packedLastDimension),m=r.ShapeUtil.computeStrides(p),[_,b]=t.calculateTextureWidthAndHeight(p,s.TextureType.packedLastDimension),y=u.length,w=n.length<3?"0.0":"_B(b)",v=Math.ceil(c[1]*d[2]*d[3]/4),{activationFunction:x,applyActivation:T}=(0,o.getActivationSnippet)(l),S=(0,i.getGlsl)(t.session.backend.glContext.version),A=`\n${x}\nfloat process(int indices[${y}]) {\n int b[1];\n b[0] = indices[1];\n int im2col[4];\n im2col[0] = indices[0];\n im2col[1] = indices[2];\n im2col[2] = indices[3];\n int im2colOffset = im2col[0] * ${m[0]} + im2col[1] * ${m[1]} + im2col[2] * ${m[2]};\n int kernelOffset = indices[1] * ${h[1]};\n float value = ${w};\n for (int i = 0; i < ${v}; ++i) {\n vec2 im2colCoords = offsetToCoords(im2colOffset, ${_}, ${b});\n vec2 kernelCoords = offsetToCoords(kernelOffset, ${f}, ${g});\n value += dot(${S.texture2D}(Im2Col, im2colCoords), ${S.texture2D}(K, kernelCoords));\n ++im2colOffset;\n ++kernelOffset;\n }\n ${T}\n return value;\n}`;return Object.assign(Object.assign({},e),{output:{dims:u,type:n[0].type,textureType:s.TextureType.unpacked},shaderSource:A})})(t,l,e,n,u)})}},7992:(t,e,n)=>{"use strict";Object.defineProperty(e,"__esModule",{value:!0}),e.parseFlattenAttributes=e.flatten=void 0;const r=n(2517);e.flatten=(t,e,n)=>{i(e,n);const s=r.ShapeUtil.flattenShape(e[0].dims,n);return[t.reshapeUnpacked(e[0],s)]},e.parseFlattenAttributes=t=>t.attributes.getInt("axis",1);const i=(t,e)=>{if(!t||1!==t.length)throw new Error("Flatten requires 1 input.");const n=t[0].dims.length;if(0===n)throw new Error("scalar tensor is not supported.");if(e<-n||e>n)throw new Error("Invalid axis");if("string"===t[0].type)throw new Error("string tensor is not supported.")}},2823:(t,e,n)=>{"use 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r=n(246),i=n(782),s=n(2517),o=n(2039);e.gather=(t,e,n)=>(l(e,n.axis),[t.run(u(t,e,n),e)]),e.parseGatherAttributes=t=>(0,r.createAttributeWithCacheKey)({axis:t.attributes.getInt("axis",0)});const a={name:"Gather",inputNames:["A","B"],inputTypes:[o.TextureType.unpacked,o.TextureType.unpacked]},u=(t,e,n)=>{const r=Object.assign(Object.assign({},a),{cacheHint:n.cacheKey});return Object.assign(Object.assign({},r),{get:()=>((t,e,n,r)=>{const i=n[0].dims.slice(),a=n[1].dims.slice(),u=new Array(i.length+a.length-1);r=s.ShapeUtil.normalizeAxis(r,i.length);const l=[];for(let t=0;t{if(!t||2!==t.length)throw new Error("Gather requires 2 inputs.");const n=t[0].dims.length;if(n<1)throw new Error("Invalid input shape.");if(e<-n||e>n-1)throw new Error("Invalid axis.");if(-1===i.NUMBER_TYPES.indexOf(t[0].type))throw new Error("Invaid input type.");if("int32"!==t[1].type&&"int16"!==t[1].type)throw new Error("Invaid input type.")}},4776:(t,e,n)=>{"use strict";Object.defineProperty(e,"__esModule",{value:!0}),e.parseGemmAttributesV11=e.parseGemmAttributesV7=e.gemm=void 0;const r=n(246),i=n(2517),s=n(2039);e.gemm=(t,e,n)=>(l(e,n),[t.run(a(e,n),e)]);const o=(t,e)=>{const n=0!==t.attributes.getInt("transA",0),i=0!==t.attributes.getInt("transB",0),s=t.attributes.getFloat("alpha",1),o=t.attributes.getFloat("beta",1);return(0,r.createAttributeWithCacheKey)({transA:n,transB:i,alpha:s,beta:o,isOptionalC:e})};e.parseGemmAttributesV7=t=>o(t,!1),e.parseGemmAttributesV11=t=>o(t,!0);const a=(t,e)=>{const n={name:"Gemm",inputNames:3===t.length?["A","B","C"]:["A","B"],inputTypes:3===t.length?[s.TextureType.unpacked,s.TextureType.unpacked,s.TextureType.unpacked]:[s.TextureType.unpacked,s.TextureType.unpacked],key:e.cacheKey};return Object.assign(Object.assign({},n),{get:()=>u(n,t,e)})},u=(t,e,n)=>{const r=e[0].dims.slice(),o=e[1].dims.slice(),[a,u]=i.GemmUtil.getShapeOfGemmResult(r,n.transA,o,n.transB,3===e.length?e[2].dims:void 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Error("Input is missing");if(e.isOptionalC&&(t.length<2||t.length>3))throw new Error("Invaid input shape.");if(!e.isOptionalC&&3!==t.length)throw new Error("Gemm requires 3 inputs");if(3===t.length&&1!==t[2].dims.length&&2!==t[2].dims.length)throw new Error("Invalid input shape of C");if("float32"!==t[0].type&&"float64"!==t[0].type||"float32"!==t[1].type&&"float64"!==t[1].type||3===t.length&&"float32"!==t[2].type&&"float64"!==t[2].type)throw new Error("Invalid input type.");if(t[0].type!==t[1].type||3===t.length&&t[0].type!==t[2].type)throw new Error("Input types are mismatched")}},8555:(t,e,n)=>{"use strict";Object.defineProperty(e,"__esModule",{value:!0}),e.createPackedIm2ColProgramInfoLoader=void 0;const r=n(5060),i=n(2039),s=n(2827);e.createPackedIm2ColProgramInfoLoader=(t,e,n,o,a)=>{const u=(l=a.cacheKey,{name:"Im2Col (packed)",inputNames:["A"],inputTypes:[i.TextureType.packed],cacheHint:l});var l;return Object.assign(Object.assign({},u),{get:()=>((t,e,n,o,a,u)=>{const 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Object.assign(Object.assign({},t),{output:{dims:o,type:e.type,textureType:i.TextureType.packedLastDimension},shaderSource:a})})(s,t)}),a={name:"InstanceNormalization_ComputeOutput",inputNames:["X","MeanAndVariance","Scale","B"],inputTypes:[i.TextureType.unpacked,i.TextureType.packedLastDimension,i.TextureType.unpacked,i.TextureType.unpacked]},u=(t,e,n,s)=>{const o=Object.assign(Object.assign({},a),{cacheHint:`${n}`});return Object.assign(Object.assign({},o),{get:()=>((t,e,n,s,o)=>{const a=(0,r.getGlsl)(t.session.backend.glContext.version),[u,l]=t.calculateTextureWidthAndHeight(o,i.TextureType.packedLastDimension),[c,d]=[u/4,l],h=`\n vec4 get_MeanAndVariance(int[2] mv) {\n int offset = indicesToOffset_MeanAndVariance(mv);\n vec2 coords = offsetToCoords(offset, ${c}, ${d});\n return ${a.texture2D}(MeanAndVariance, coords);\n }\n\n float process(int[4] indices) {\n int mv[2];\n mv[0] = indices[0];\n mv[1] = indices[1];\n vec4 mean_and_variance = get_MeanAndVariance(mv);\n float mean = mean_and_variance.r;\n float variance = mean_and_variance.g;\n\n int sb[1];\n sb[0] = indices[1];\n float scale = _Scale(sb);\n float b = _B(sb);\n\n return scale * (_X(indices) - mean) / sqrt(variance + epsilon) + b;\n }`;return Object.assign(Object.assign({},e),{output:{dims:n.dims,type:n.type,textureType:i.TextureType.unpacked},variables:[{name:"epsilon",type:"float",data:s}],shaderSource:h})})(t,o,e,n,s)})},l=t=>{if(!t||3!==t.length)throw new Error("InstanceNormalization requires 3 inputs.");const e=t[0],n=t[1],r=t[2];if(e.dims.length<3||1!==n.dims.length||1!==r.dims.length)throw new Error("Invalid input shape.");if(n.dims[0]!==e.dims[1]||r.dims[0]!==e.dims[1])throw new Error("Input shapes are mismatched.");if("float32"!==e.type&&"float64"!==e.type||"float32"!==n.type&&"float64"!==n.type||"float32"!==r.type&&"float64"!==r.type)throw new Error("Invalid input type.");if(4!==t[0].dims.length)throw new Error("Only support 4-D input shape.")}},708:(t,e,n)=>{"use 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m=h[h.length-1],_=Math.ceil(m/2),b=h.length,y=p.length,w=(0,i.getGlsl)(t.session.backend.glContext.version),v=(0,o.getCoordsDataType)(f.length),x=f.length,T=(0,o.getGlChannels)(),{activationFunction:S,applyActivation:A}=(0,a.getActivationSnippet)(l),k=c?`${(0,u.getBiasForMatmul)(v,T,n[2].dims,f,!0)}`:"",O=g?`${function(t,e,n,i){let s=[],o=[];const a=n[0].dims,u=n[1].dims,l=a.length,c=u.length,d=i.length,h=d-l,p=d-c;s=a.map(((t,n)=>`coords.${e[n+h]}`)),s[l-1]="i*2",s.join(", "),o=u.map(((t,n)=>`coords.${e[n+p]}`)),o[c-2]="i*2",o.join(", ");const f=r.BroadcastUtil.getBroadcastDims(a,i),g=r.BroadcastUtil.getBroadcastDims(u,i),m=f.map((t=>`coords.${e[t+h]} = 0;`)).join("\n"),_=g.map((t=>`coords.${e[t+p]} = 0;`)).join("\n"),b=`int lastDim = coords.${e[d-1]};\n coords.${e[d-1]} = coords.${e[d-2]};\n coords.${e[d-2]} = lastDim;`;return`\nvec4 getAAtOutCoordsMatmul(int i) {\n ${t} coords = getOutputCoords();\n ${b}\n ${m}\n vec4 outputValue = getA(${s});\n return outputValue;\n}\n\nvec4 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r={name:"GlobalAveragePool",inputNames:["X"],inputTypes:[s.TextureType.unpacked],cacheHint:`${n.countIncludePad}`};return[t.run(Object.assign(Object.assign({},r),{get:()=>o(e,r,!0,n)}),e)]},e.parseGlobalAveragePoolAttributes=t=>{const e=0!==t.attributes.getInt("count_include_pad",0);return(0,r.createAttributeWithCacheKey)({autoPad:"",ceilMode:0,countIncludePad:e,kernelShape:[],strides:[],pads:[]})},e.maxPool=(t,e,n)=>{d(e);const r={name:"MaxPool",inputNames:["X"],inputTypes:[s.TextureType.unpacked],cacheHint:n.cacheKey};return[t.run(Object.assign(Object.assign({},r),{get:()=>a(e,r,!1,n)}),e)]},e.parseMaxPoolAttributes=t=>{const e=t.attributes.getString("auto_pad","NOTSET"),n=t.attributes.getInt("ceil_mode",0),i=t.attributes.getInts("kernel_shape"),s=t.attributes.getInts("strides",[]),o=t.attributes.getInts("pads",[]),a=t.attributes.getInt("storage_order",0),u=t.attributes.getInts("dilations",[]);if(0!==a)throw new Error("column major storage order is not yet supported for 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s?Object.assign(d,{kernelShape:o,strides:a,pads:l,dilations:u,cacheKey:e.cacheKey}):Object.assign(d,{kernelShape:o,strides:a,pads:l,cacheKey:e.cacheKey}),[d,c]},l={autoPad:"",ceilMode:0,countIncludePad:!1,kernelShape:[],strides:[],pads:[],storageOrder:0,dilations:[],cacheKey:""},c={name:"GlobalMaxPool",inputNames:["X"],inputTypes:[s.TextureType.unpacked]};e.globalMaxPool=(t,e)=>(d(e),[t.run(Object.assign(Object.assign({},c),{get:()=>a(e,c,!0,l)}),e)]);const d=t=>{if(!t||1!==t.length)throw new Error("Pool ops requires 1 input.");if("float32"!==t[0].type&&"float64"!==t[0].type)throw new Error("Invalid input type.")},h=(t,e,n,r,s)=>{const o=t.length;if(e.kernelShape.length<=2){const i=e.kernelShape[e.kernelShape.length-1],a=e.strides[e.strides.length-1],u=e.pads[e.pads.length/2-1],l=e.pads[e.pads.length-1],c=t[o-1];let d="",h="",p="";if(d=u+l!==0?`\n for (int i = 0; i < ${i}; i++) {\n x[${o} - 1] = indices[${o} - 1] * ${a} - ${u} + i;\n if (x[${o} - 1] < 0 || x[${o} - 1] >= ${c}) {\n 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n=0===t.length||0===e.length||(t.length<2||e.length<2?t[t.length-1]===e[e.length-1]:t[t.length-1]===e[e.length-1]&&t[t.length-2]===e[e.length-2]),n}},718:(t,e,n)=>{"use strict";Object.defineProperty(e,"__esModule",{value:!0}),e.reshape=void 0;const r=n(2517);e.reshape=(t,e)=>{const n=r.ShapeUtil.calculateReshapedDims(e[0].dims,e[1].integerData);return t.session.pack?[t.reshapePacked(e[0],n)]:[t.reshapeUnpacked(e[0],n)]}},2268:(t,e,n)=>{"use strict";Object.defineProperty(e,"__esModule",{value:!0}),e.parseResizeAttributesV11=e.parseResizeAttributesV10=e.resize=void 0;const r=n(5060),i=n(2039),s=n(9390),o=n(2827),a=n(9793),u={name:"Resize",inputNames:["A"],inputTypes:[i.TextureType.packed]};e.resize=(t,e,n)=>((0,a.validateInputs)(e,n),[t.run(Object.assign(Object.assign({},u),{cacheHint:n.cacheKey,get:()=>l(t,e,n)}),e)]),e.parseResizeAttributesV10=t=>(0,a.parseUpsampleAttributes)(t,10),e.parseResizeAttributesV11=t=>(0,a.parseUpsampleAttributes)(t,11);const l=(t,e,n)=>{const a=(0,r.getGlsl)(t.session.backend.glContext.version),[l,d]=c(e,n);if(l.every((t=>1===t))&&"tf_crop_and_resize"!==n.coordinateTransformMode)return Object.assign(Object.assign({},u),{output:{dims:d,type:e[0].type,textureType:i.TextureType.packed},hasMain:!0,shaderSource:`void main() {\n vec4 v = ${a.texture2D}(X, TexCoords);\n ${a.output} = v;\n }`});const h=d.length;if(h<2)throw new Error(`output dimension should be at least 2, but got ${h}`);const p=d[h-2],f=d[h-1],g=e[0].dims;if(h!==g.length)throw new Error(`output dimension should match input ${g.length}, but got ${h}`);const m=g[h-2],_=g[h-1],b=l[h-2],y=l[h-1];let w="";if("linear"!==n.mode)throw new Error(`resize (packed) does not support mode: '${n.mode}'`);switch(n.coordinateTransformMode){case"asymmetric":w="\n vec4 getSourceFracIndex(ivec4 coords) {\n return vec4(coords) / scaleWHWH;\n }\n ";break;case"half_pixel":w="\n vec4 getSourceFracIndex(ivec4 coords) {\n return (vec4(coords) + 0.5) / scaleWHWH - 0.5;\n }\n ";break;case"pytorch_half_pixel":w=`\n vec4 getSourceFracIndex(ivec4 coords) {\n vec4 fcoords = vec4(coords);\n return vec4(\n ${f}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0,\n ${p}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0,\n ${f}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0,\n ${p}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0\n );\n }\n `;break;case"align_corners":w=`\n vec4 getSourceFracIndex(ivec4 coords) {\n vec4 resized = vec4(${f}.0 - 1.0, ${p}.0 - 1.0, ${f}.0 - 1.0,\n ${p}.0 - 1.0);\n vec4 original = vec4(${_}.0 - 1.0, ${m}.0 - 1.0, ${_}.0 - 1.0,\n ${m}.0 - 1.0);\n vec4 new_scale = original / resized;\n return vec4(coords) * new_scale;\n }\n `;break;default:throw new Error(`resize (packed) does not support coordinateTransformMode: '${n.coordinateTransformMode}'`)}const v=(0,s.getCoordsDataType)(h),x=`\n const vec2 inputWH = vec2(${m}.0, ${_}.0);\n const vec4 scaleWHWH = vec4(float(${b}), float(${y}), float(${b}), float(${y}));\n ${(0,o.unpackFromChannel)()}\n ${w}\n float getAValue(int x10, int r, int c, int d) {\n return getChannel(getA(x10, r, c, d), vec2(c, d));\n }\n void main() {\n ${v} rc = getOutputCoords();\n\n int batch = rc[0];\n int depth = rc[1];\n\n // retrieve the 4 coordinates that is used in the 4 packed output values.\n ivec4 coords = ivec4(rc.wz, rc.w + 1, rc.z + 1);\n\n // calculate the source index in fraction\n vec4 sourceFrac = getSourceFracIndex(coords);\n\n // get the lower and upper bound of the 4 values that will be packed into one texel.\n ivec4 x00 = ivec4(max(sourceFrac.xy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xy)));\n ivec4 x01 = ivec4(max(sourceFrac.xw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xw)));\n ivec4 x10 = ivec4(max(sourceFrac.zy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zy)));\n ivec4 x11 = ivec4(max(sourceFrac.zw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zw)));\n\n bool hasNextRow = rc.w < ${p-1};\n bool hasNextCol = rc.z < ${f-1};\n\n // pack x00, x01, x10, x11's top-left corner into one vec4 structure\n vec4 topLeft = vec4(\n getAValue(batch, depth, x00.x, x00.y),\n hasNextCol ? getAValue(batch, depth, x01.x, x01.y) : 0.0,\n hasNextRow ? getAValue(batch, depth, x10.x, x10.y) : 0.0,\n (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.y) : 0.0);\n\n // pack x00, x01, x10, x11's top-right corner into one vec4 structure\n vec4 topRight = vec4(\n getAValue(batch, depth, x00.x, x00.w),\n hasNextCol ? getAValue(batch, depth, x01.x, x01.w) : 0.0,\n hasNextRow ? getAValue(batch, depth, x10.x, x10.w) : 0.0,\n (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.w) : 0.0);\n\n // pack x00, x01, x10, x11's bottom-left corner into one vec4 structure\n vec4 bottomLeft = vec4(\n getAValue(batch, depth, x00.z, x00.y),\n hasNextCol ? getAValue(batch, depth, x01.z, x01.y) : 0.0,\n hasNextRow ? getAValue(batch, depth, x10.z, x10.y) : 0.0,\n (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.y) : 0.0);\n\n // pack x00, x01, x10, x11's bottom-right corner into one vec4 structure\n vec4 bottomRight = vec4(\n getAValue(batch, depth, x00.z, x00.w),\n hasNextCol ? getAValue(batch, depth, x01.z, x01.w) : 0.0,\n hasNextRow ? getAValue(batch, depth, x10.z, x10.w) : 0.0,\n (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.w) : 0.0);\n\n // calculate the interpolation fraction on u and v direction\n vec4 frac = vec4(sourceFrac) - floor(sourceFrac);\n vec4 clampFrac = clamp(frac, vec4(0.0), vec4(1.0));\n\n vec4 top = mix(topLeft, topRight, clampFrac.ywyw);\n vec4 bottom = mix(bottomLeft, bottomRight, clampFrac.ywyw);\n vec4 newValue = mix(top, bottom, clampFrac.xxzz);\n\n ${a.output} = vec4(newValue);\n }\n `;return Object.assign(Object.assign({},u),{output:{dims:d,type:e[0].type,textureType:i.TextureType.packed},hasMain:!0,shaderSource:x})},c=(t,e)=>{const n=t[0].dims;let r,i=e.scales;if(0===i.length){const s=t[e.scalesInputIdx];if(s&&0!==s.size){if(t[e.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");i=d(s,e.mode,e.isResize)}else{const s=t[e.sizesInputIdx];if(!s||0===s.size)throw new Error("Either scales or sizes MUST be provided as input.");r=Array.from(s.integerData),i=h(r,n,e.mode,e.isResize)}}else if(t[e.sizesInputIdx])throw new Error("Only one of scales or sizes must be provided as input.");const s=r||n.map(((t,e)=>Math.floor(t*i[e])));return[i,s]},d=(t,e,n)=>{const r=Array.from(t.floatData);return(0,a.scalesValidation)(r,e,n),r},h=(t,e,n,r)=>{const i=e.length,s=new Array(i);for(let n=0,r=i;n{"use strict";Object.defineProperty(e,"__esModule",{value:!0}),e.shape=void 0;const r=n(9162);e.shape=(t,e)=>(i(e),[new r.Tensor([e[0].dims.length],"int32",void 0,void 0,new Int32Array(e[0].dims))]);const i=t=>{if(!t||1!==t.length)throw new Error("Shape requires 1 input.")}},2278:(t,e,n)=>{"use strict";Object.defineProperty(e,"__esModule",{value:!0}),e.sliceV10=e.parseSliceAttributes=e.slice=void 0;const r=n(246),i=n(782),s=n(2517),o=n(2039),a={name:"Slice",inputNames:["A"],inputTypes:[o.TextureType.unpacked]};e.slice=(t,e,n)=>(l(e),[t.run(Object.assign(Object.assign({},a),{cacheHint:n.cacheKey,get:()=>u(t,e[0],n)}),e)]),e.parseSliceAttributes=t=>{const e=t.attributes.getInts("starts"),n=t.attributes.getInts("ends"),i=t.attributes.getInts("axes",[]);return(0,r.createAttributeWithCacheKey)({starts:e,ends:n,axes:i})};const u=(t,e,n)=>{const r=0===n.axes.length?e.dims.slice(0).map(((t,e)=>e)):n.axes,i=s.ShapeUtil.normalizeAxes(r,e.dims.length),u=n.starts.map(((t,n)=>t>e.dims[i[n]]-1?e.dims[i[n]]:s.ShapeUtil.normalizeAxis(t,e.dims[i[n]]))),l=n.ends.map(((t,n)=>t>e.dims[i[n]]-1?e.dims[i[n]]:s.ShapeUtil.normalizeAxis(t,e.dims[i[n]]))),c=e.dims.slice(),d=[];for(let t=0;t0&&d.push(`outputIdx[${i[t]}] += ${u[t]};`);const h=`\n float process(int outputIdx[${c.length}]) {\n ${d.join("\n ")}\n return _A(outputIdx);\n }`;return Object.assign(Object.assign({},a),{output:{dims:c,type:e.type,textureType:o.TextureType.unpacked},shaderSource:h})},l=t=>{if(!t||1!==t.length)throw new Error("Slice requires 1 input.");if(-1===i.NUMBER_TYPES.indexOf(t[0].type))throw new Error("Invalid input type.")};e.sliceV10=(t,e)=>{d(e);const n=c(t,e);return[t.run(Object.assign(Object.assign({},a),{cacheHint:n.cacheKey,get:()=>u(t,e[0],n)}),[e[0]])]};const c=(t,e)=>{if(!t.session.isInitializer(e[1].dataId)||!t.session.isInitializer(e[2].dataId)||e.length>=4&&!t.session.isInitializer(e[3].dataId)||e.length>=5&&!t.session.isInitializer(e[4].dataId))throw new Error("dynamic slice attributes are not allowed");if(e.length>=5&&e[4].integerData.some((t=>1!==t)))throw new Error("currently non-1 steps is not supported for Slice");const n=Array.from(e[1].integerData),r=Array.from(e[2].integerData),i=e.length>=4?Array.from(e[3].integerData):[];return{starts:n,ends:r,axes:i,cacheKey:`${i};${n};${r}`}},d=t=>{if(!t||t.length<3||t.length>5)throw new Error("Invalid input number.");if("int32"!==t[1].type||1!==t[1].dims.length)throw new Error("Invalid input type.");if("int32"!==t[2].type||1!==t[2].dims.length)throw new Error("Invalid input type.");if(t.length>=4&&("int32"!==t[3].type||1!==t[3].dims.length))throw new Error("Invalid input type.");if(t.length>=5&&("int32"!==t[4].type||1!==t[4].dims.length))throw new Error("Invalid input type.")}},5524:(t,e,n)=>{"use strict";Object.defineProperty(e,"__esModule",{value:!0}),e.softmaxV13=e.parseSoftmaxAttributesV13=e.parseSoftmaxAttributes=e.softmax=void 0;const r=n(246),i=n(2517),s=n(5060),o=n(2039),a=n(3738),u={name:"SoftmaxComputeMax",inputNames:["A"],inputTypes:[o.TextureType.unpacked]},l={name:"SoftmaxComputeScale",inputNames:["A","Max"],inputTypes:[o.TextureType.unpacked,o.TextureType.unpacked]},c={name:"SoftMax",inputNames:["A","Max","Norm"],inputTypes:[o.TextureType.unpacked,o.TextureType.unpacked,o.TextureType.unpacked]};e.softmax=(t,e,n)=>{g(e);const r=e[0].dims.slice(),s=i.ShapeUtil.normalizeAxis(n.axis,r.length),o=i.ShapeUtil.sizeToDimension(r,s),a=i.ShapeUtil.sizeFromDimension(r,s);return d(t,e,n,o,a)},e.parseSoftmaxAttributes=t=>(0,r.createAttributeWithCacheKey)({axis:t.attributes.getInt("axis",1)}),e.parseSoftmaxAttributesV13=t=>(0,r.createAttributeWithCacheKey)({axis:t.attributes.getInt("axis",-1)}),e.softmaxV13=(t,e,n)=>{g(e);const s=e[0].dims.slice(),o=i.ShapeUtil.normalizeAxis(n.axis,s.length),u=s.length,l=o!==u-1,c=[];let h,p=[],f=[];l&&(p=Array.from({length:u}).map(((t,e)=>e)),p[o]=u-1,p[u-1]=o,p.map((t=>c.push(s[t]))),h=(0,r.createAttributeWithCacheKey)({perm:p}),f=(0,a.transpose)(t,e,h));const m=l?i.ShapeUtil.sizeToDimension(c,u-1):i.ShapeUtil.sizeToDimension(s,u-1),_=l?i.ShapeUtil.sizeFromDimension(c,u-1):i.ShapeUtil.sizeFromDimension(s,u-1),b=d(t,l?f:e,n,m,_);return l?(0,a.transpose)(t,b,h):b};const d=(t,e,n,r,i)=>{const s=h(t,e[0],r,i,[r]),o=t.run(Object.assign(Object.assign({},u),{cacheHint:n.cacheKey,get:()=>s}),e),a=p(t,e[0],r,i,s.output.dims,[r]),d=t.run(Object.assign(Object.assign({},l),{cacheHint:n.cacheKey,get:()=>a}),[e[0],o]),g=f(t,e[0],r,i,s.output.dims,a.output.dims);return[t.run(Object.assign(Object.assign({},c),{cacheHint:n.cacheKey,get:()=>g}),[e[0],o,d])]},h=(t,e,n,r,i)=>{const[a,l]=t.calculateTextureWidthAndHeight(e.dims,o.TextureType.unpacked),c=i.length;if(n<1||r<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(1!==i.length)throw new Error("Dimensionality of the output should be 1");if(i[0]!==n)throw new Error("Shape of the output should be equal to logical row count");const d=(0,s.getGlsl)(t.session.backend.glContext.version),h=`\n float process(int[${c}] indices) {\n int logical_row_start_offset = indices[0] * ${r};\n\n float max = getColorAsFloat(${d.texture2D}(A, offsetToCoords(logical_row_start_offset, ${a},\n ${l} )));\n for(int i=1; i<${r}; ++i)\n {\n float current = getColorAsFloat(${d.texture2D}(A, offsetToCoords(logical_row_start_offset + i,\n ${a}, ${l})));\n if(current > max)\n max = current;\n }\n\n return max;\n }`;return Object.assign(Object.assign({},u),{output:{dims:i,type:e.type,textureType:o.TextureType.unpacked},shaderSource:h})},p=(t,e,n,r,i,a)=>{const[u,c]=t.calculateTextureWidthAndHeight(e.dims,o.TextureType.unpacked),d=a.length;if(n<1||r<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(1!==a.length)throw new Error("Dimensionality of the output should be 1");if(a[0]!==n)throw new Error("Shape of the output should be equal to logical row count");if(1!==i.length)throw new Error("Dimensionality of the intermediate results should be 1");if(i[0]!==n)throw new Error("Shape of the intermediate results should be equal to logical row count");const h=`\n float process(int[${d}] indices) {\n int logical_row_start_offset = indices[0] * ${r};\n\n float norm_factor = 0.0;\n float max = _Max(indices);\n for(int i=0; i<${r}; ++i)\n {\n norm_factor += exp(getColorAsFloat(${(0,s.getGlsl)(t.session.backend.glContext.version).texture2D}(A, offsetToCoords(logical_row_start_offset + i,\n ${u}, ${c}))) - max);\n }\n\n return norm_factor;\n }`;return Object.assign(Object.assign({},l),{output:{dims:a,type:e.type,textureType:o.TextureType.unpacked},shaderSource:h})},f=(t,e,n,r,i,s)=>{const[a,u]=t.calculateTextureWidthAndHeight(e.dims,o.TextureType.unpacked),l=e.dims.length;if(n<1||r<1)throw new Error("Logical row count N and feature count D must be greater than or equal to 1");if(1!==i.length||1!==s.length)throw new Error("Dimensionality of the intermediate results should be 1");if(i[0]!==n||s[0]!==n)throw new Error("Shape of the intermediate results should be equal to logical row count");const d=`\n float process(int[${l}] indices) {\n\n // get offset of current logical tensor index from the 2-D texture coordinates (TexCoords)\n int offset = coordsToOffset(TexCoords, ${a}, ${u});\n\n //determine the logical row for this index\n int logical_row_index[1];\n logical_row_index[0] = offset / ${r};\n\n float norm_factor = _Norm(logical_row_index);\n\n // avoid possible division by 0\n // if norm_facor is 0, all elements are zero\n // if so, return 0\n if(norm_factor == 0.0)\n return 0.0;\n\n return exp(_A(indices) - _Max(logical_row_index)) / norm_factor;\n }`;return Object.assign(Object.assign({},c),{output:{dims:e.dims,type:e.type,textureType:o.TextureType.unpacked},shaderSource:d})},g=t=>{if(!t||1!==t.length)throw new Error("Softmax requires 1 input.");if("float32"!==t[0].type&&"float64"!==t[0].type)throw new Error("Invalid input type")}},5975:(t,e,n)=>{"use strict";Object.defineProperty(e,"__esModule",{value:!0}),e.parseSplitAttributes=e.split=void 0;const r=n(246),i=n(2517),s=n(2039),o={name:"Split",inputNames:["A"],inputTypes:[s.TextureType.unpacked]};e.split=(t,e,n)=>{l(e);const r=i.ShapeUtil.normalizeAxis(n.axis,e[0].dims.length),s=a(t,e,r,n),c=[];for(let i=0;iu(t,e[0],n,r,i)}),e));return c},e.parseSplitAttributes=t=>{const e=t.attributes.getInt("axis",0),n=t.attributes.getInts("split",[]),i=t.outputs.length;return(0,r.createAttributeWithCacheKey)({axis:e,split:n,numOutputs:i})};const a=(t,e,n,r)=>{const[,s]=i.SplitUtil.splitShape(e[0].dims,n,r.split,r.numOutputs);return s.length},u=(t,e,n,r,a)=>{const[u,l]=i.SplitUtil.splitShape(e.dims,r,n.split,n.numOutputs),c=l[a],d=u[a],h=`\n float process(int indices[${d.length}]) {\n indices[${r}] += ${c};\n return _A(indices);\n }\n `;return Object.assign(Object.assign({},o),{cacheHint:`${n.cacheKey}:${a}`,output:{dims:d,type:e.type,textureType:s.TextureType.unpacked},shaderSource:h})},l=t=>{if(!t||1!==t.length)throw new Error("Split requires one input.");if("int8"!==t[0].type&&"uint8"!==t[0].type&&"int16"!==t[0].type&&"uint16"!==t[0].type&&"int32"!==t[0].type&&"uint32"!==t[0].type&&"float32"!==t[0].type&&"float64"!==t[0].type&&"bool"!==t[0].type)throw new Error("Invalid input type.")}},3933:(t,e,n)=>{"use strict";Object.defineProperty(e,"__esModule",{value:!0}),e.parseSqueezeAttributes=e.squeezeV13=e.squeeze=void 0;const r=n(2517);e.squeeze=(t,e,n)=>{i(e);const s=r.ShapeUtil.squeezeShape(e[0].dims,n);return[t.reshapeUnpacked(e[0],s)]},e.squeezeV13=(t,n)=>(s(n),(0,e.squeeze)(t,[n[0]],Array.from(n[1].integerData))),e.parseSqueezeAttributes=t=>t.attributes.getInts("axes");const i=t=>{if(!t||1!==t.length)throw new Error("Squeeze requires 1 input.");if("string"===t[0].type)throw new Error("invalid input tensor types.")},s=t=>{if(!t||2!==t.length)throw new Error("Squeeze requires 2 inputs.");if("int32"!==t[1].type)throw new Error("Invalid input type.")}},6558:(t,e,n)=>{"use strict";Object.defineProperty(e,"__esModule",{value:!0}),e.sum=void 0;const r=n(5060),i=n(2039);e.sum=(t,e)=>{o(e);const n={name:"Sum",inputNames:e.map(((t,e)=>`X${e}`)),inputTypes:new Array(e.length).fill(i.TextureType.unpacked)};return[t.run(Object.assign(Object.assign({},n),{get:()=>s(t,e,n)}),e)]};const s=(t,e,n)=>{const s=(0,r.getGlsl)(t.session.backend.glContext.version),o=e[0].dims.slice(),a=`\n void main() {\n vec4 result = ${e.map(((t,e)=>`${s.texture2D}(X${e},TexCoords)`)).join(" + ")};\n ${s.output} = result;\n }\n `;return Object.assign(Object.assign({},n),{output:{dims:o,type:e[0].type,textureType:i.TextureType.unpacked},hasMain:!0,shaderSource:a})},o=t=>{if(!t||0===t.length)throw new Error("Sum requires inputs.");const e=t[0].dims.length;for(let n=1;n{"use strict";Object.defineProperty(e,"__esModule",{value:!0}),e.tile=void 0;const r=n(782),i=n(2039);e.tile=(t,e)=>{o(e);const n={name:"Tile",inputNames:["A"],inputTypes:[i.TextureType.unpacked]};return[t.run(Object.assign(Object.assign({},n),{get:()=>s(t,e,n)}),e)]};const s=(t,e,n)=>{const r=e[0].dims.slice(),s=new Array(r.length),o=[];for(let t=0;t{if(!t||2!==t.length)throw new Error("Tile requires 2 input.");if(1!==t[1].dims.length)throw new Error("The second input shape must 1 dimension.");if(t[1].dims[0]!==t[0].dims.length)throw new Error("Invalid input shape.");if(-1===r.NUMBER_TYPES.indexOf(t[0].type))throw new Error("Invalid input type.");if("int32"!==t[1].type&&"int16"!==t[1].type)throw new Error("Invalid repeat type.")}},3738:(t,e,n)=>{"use strict";Object.defineProperty(e,"__esModule",{value:!0}),e.parseTransposeAttributes=e.transpose=void 0;const r=n(246),i=n(2517),s=n(2039),o={name:"Transpose",inputNames:["A"],inputTypes:[s.TextureType.unpacked]};e.transpose=(t,e,n)=>(d(e),[t.run(Object.assign(Object.assign({},o),{cacheHint:n.cacheKey,get:()=>a(t,e[0],n.perm)}),e)]),e.parseTransposeAttributes=t=>(0,r.createAttributeWithCacheKey)({perm:t.attributes.getInts("perm",[])});const a=(t,e,n)=>{const r=e.dims;n=u(r,n);const i=l(r,n),a=r.length,d=`\n ${c("perm",n,a)}\n float process(int indices[${a}]) {\n int a[${a}];\n perm(a, indices);\n return _A(a);\n }`;return Object.assign(Object.assign({},o),{output:{dims:i,type:e.type,textureType:s.TextureType.unpacked},shaderSource:d})},u=(t,e)=>(e&&e.length!==t.length&&(e=[...t.keys()].reverse()),e),l=(t,e)=>(e=u(t,e),i.ShapeUtil.sortBasedOnPerm(t,e)),c=(t,e,n)=>{const r=[];r.push(`void ${t}(out int a[${n}], int src[${n}]) {`);for(let t=0;t{if(!t||1!==t.length)throw new Error("Transpose requires 1 input.");if("float32"!==t[0].type&&"float64"!==t[0].type)throw new Error("input should be float 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