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| import os | |
| import streamlit as st | |
| import subprocess | |
| from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, AutoModel, RagRetriever, AutoModelForSeq2SeqLM | |
| import black | |
| from pylint import lint | |
| from io import StringIO | |
| import sys | |
| import torch | |
| from huggingface_hub import hf_hub_url, cached_download, HfApi | |
| import base64 | |
| HF_TOKEN = os.environ.get("HF_TOKEN", None) | |
| # Add the new HTML code below | |
| custom_html = ''' | |
| <div style='position:fixed;bottom:0;left:0;width:100%;'> | |
| <iframe width="100%" scrolling="no" title="CodeGPT Widget" frameborder="0" allowtransparency sandbox="" allowfullscreen="" data-widget-id="c265505c-e667-4af2-b492-291da888ee7c" src="https://widget.codegpt.co/chat-widget.js"></iframe> | |
| </div>''' | |
| # Update the markdown function to accept custom HTML code | |
| def markdown_with_custom_html(md, html): | |
| md_content = md | |
| if html: | |
| return f"{md_content}\n\n{html}" | |
| else: | |
| return md_content | |
| markdown_text = "Compare model responses with me!" | |
| markdown_with_custom_html(markdown_text, custom_html) | |
| # Set your Hugging Face API key here | |
| # hf_token = "YOUR_HUGGING_FACE_API_KEY" # Replace with your actual token | |
| # Get Hugging Face token from secrets.toml - this line should already be in the main code | |
| hf_token = "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" | |
| HUGGING_FACE_REPO_URL = "https://huggingface.co/spaces/acecalisto3/DevToolKit" | |
| PROJECT_ROOT = "projects" | |
| AGENT_DIRECTORY = "agents" | |
| # Global state to manage communication between Tool Box and Workspace Chat App | |
| if 'chat_history' not in st.session_state: | |
| st.session_state.chat_history = [] | |
| if 'terminal_history' not in st.session_state: | |
| st.session_state.terminal_history = [] | |
| if 'workspace_projects' not in st.session_state: | |
| st.session_state.workspace_projects = {} | |
| if 'available_agents' not in st.session_state: | |
| st.session_state.available_agents = [] | |
| if 'current_state' not in st.session_state: | |
| st.session_state.current_state = { | |
| 'toolbox': {}, | |
| 'workspace_chat': {} | |
| } | |
| # List of top downloaded free code-generative models from Hugging Face Hub | |
| AVAILABLE_CODE_GENERATIVE_MODELS = [ | |
| "bigcode/starcoder", # Popular and powerful | |
| "Salesforce/codegen-350M-mono", # Smaller, good for quick tasks | |
| "microsoft/CodeGPT-small", # Smaller, good for quick tasks | |
| "google/flan-t5-xl", # Powerful, good for complex tasks | |
| "facebook/bart-large-cnn", # Good for text-to-code tasks | |
| ] | |
| # Load pre-trained RAG retriever | |
| rag_retriever = RagRetriever.from_pretrained("facebook/rag-token-base") # Use a Hugging Face RAG model | |
| # Load pre-trained chat model | |
| chat_model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/DialoGPT-medium") # Use a Hugging Face chat model | |
| # Load tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium") | |
| def process_input(user_input): | |
| # Input pipeline: Tokenize and preprocess user input | |
| input_ids = tokenizer(user_input, return_tensors="pt").input_ids | |
| attention_mask = tokenizer(user_input, return_tensors="pt").attention_mask | |
| # RAG model: Generate response | |
| with torch.no_grad(): | |
| output = rag_retriever(input_ids, attention_mask=attention_mask) | |
| response = output.generator_outputs[0].sequences[0] | |
| # Chat model: Refine response | |
| chat_input = tokenizer(response, return_tensors="pt") | |
| chat_input["input_ids"] = chat_input["input_ids"].unsqueeze(0) | |
| chat_input["attention_mask"] = chat_input["attention_mask"].unsqueeze(0) | |
| with torch.no_grad(): | |
| chat_output = chat_model(**chat_input) | |
| refined_response = chat_output.sequences[0] | |
| # Output pipeline: Return final response | |
| return refined_response | |
| class AIAgent: | |
| def __init__(self, name, description, skills, hf_api=None): | |
| self.name = name | |
| self.description = description | |
| self.skills = skills | |
| self._hf_api = hf_api | |
| self._hf_token = hf_token # Store the token here | |
| def hf_api(self): | |
| if not self._hf_api and self.has_valid_hf_token(): | |
| self._hf_api = HfApi(token=self._hf_token) | |
| return self._hf_api | |
| def has_valid_hf_token(self): | |
| return bool(self._hf_token) | |
| async def autonomous_build(self, chat_history, workspace_projects, project_name, selected_model, hf_token): | |
| self._hf_token = hf_token | |
| # Continuation of previous methods | |
| summary = "Chat History:\n" + "\n".join([f"User: {u}\nAgent: {a}" for u, a in chat_history]) | |
| summary += "\n\nWorkspace Projects:\n" + "\n".join([f"{p}: {details}" for p, details in workspace_projects.items()]) | |
| # Analyze chat history and workspace projects to suggest actions | |
| # Example: | |
| # - Check if the user has requested to create a new file | |
| # - Check if the user has requested to install a package | |
| # - Check if the user has requested to run a command | |
| # - Check if the user has requested to generate code | |
| # - Check if the user has requested to translate code | |
| # - Check if the user has requested to summarize text | |
| # - Check if the user has requested to analyze sentiment | |
| # Generate a response based on the analysis | |
| next_step = "Based on the current state, the next logical step is to implement the main application logic." | |
| # Ensure project folder exists | |
| project_path = os.path.join(PROJECT_ROOT, project_name) | |
| if not os.path.exists(project_path): | |
| os.makedirs(project_path) | |
| # Create requirements.txt if it doesn't exist | |
| requirements_file = os.path.join(project_path, "requirements.txt") | |
| if not os.path.exists(requirements_file): | |
| with open(requirements_file, "w") as f: | |
| f.write("# Add your project's dependencies here\n") | |
| # Create app.py if it doesn't exist | |
| app_file = os.path.join(project_path, "app.py") | |
| if not os.path.exists(app_file): | |
| with open(app_file, "w") as f: | |
| f.write("# Your project's main application logic goes here\n") | |
| # Generate GUI code for app.py if requested | |
| if "create a gui" in summary.lower(): | |
| gui_code = generate_code("Create a simple GUI for this application", selected_model) | |
| with open(app_file, "a") as f: | |
| f.write(gui_code) | |
| # Run the default build process | |
| build_command = "pip install -r requirements.txt && python app.py" | |
| try: | |
| result = subprocess.run(build_command, shell=True, capture_output=True, text=True, cwd=project_path) | |
| st.write(f"Build Output:\n{result.stdout}") | |
| if result.stderr: | |
| st.error(f"Build Errors:\n{result.stderr}") | |
| except Exception as e: | |
| st.error(f"Build Error: {e}") | |
| return summary, next_step | |
| def deploy_built_space_to_hf(self): | |
| if not self._hf_api or not self._hf_token: | |
| raise ValueError("Cannot deploy the Space since no valid Hugoging Face API connection was established.") | |
| # Assuming you have a function to get the files for your Space | |
| repository_name = f"my-awesome-space_{datetime.now().timestamp()}" | |
| files = get_built_space_files() # Placeholder - you'll need to define this function | |
| # Create the Space | |
| create_space(self.hf_api, repository_name, "Description", True, files) | |
| st.markdown("## Congratulations! Successfully deployed Space 🚀 ##") | |
| st.markdown(f"[Check out your new Space here](https://huggingface.co/spaces/{repository_name})") | |
| # Add any missing functions from your original code (e.g., get_built_space_files) | |
| def get_built_space_files(): | |
| # Replace with your logic to gather the files you want to deploy | |
| return { | |
| "app.py": "# Your Streamlit app code here", | |
| "requirements.txt": "streamlit\ntransformers" | |
| # Add other files as needed | |
| } | |
| def save_agent_to_file(agent): | |
| """Saves the agent's prompt to a file.""" | |
| if not os.path.exists(AGENT_DIRECTORY): | |
| os.makedirs(AGENT_DIRECTORY) | |
| file_path = os.path.join(AGENT_DIRECTORY, f"{agent.name}.txt") | |
| with open(file_path, "w") as file: | |
| file.write(agent.create_agent_prompt()) | |
| st.session_state.available_agents.append(agent.name) | |
| def load_agent_prompt(agent_name): | |
| """Loads an agent prompt from a file.""" | |
| file_path = os.path.join(AGENT_DIRECTORY, f"{agent_name}.txt") | |
| if os.path.exists(file_path): | |
| with open(file_path, "r") as file: | |
| agent_prompt = file.read() | |
| return agent_prompt | |
| else: | |
| return None | |
| def create_agent_from_text(name, text): | |
| skills = text.split('\n') | |
| agent = AIAgent(name, "AI agent created from text input.", skills) | |
| save_agent_to_file(agent) | |
| return agent.create_agent_prompt() | |
| def chat_interface_with_agent(input_text, agent_name): | |
| agent_prompt = load_agent_prompt(agent_name) | |
| if agent_prompt is None: | |
| return f"Agent {agent_name} not found." | |
| model_name ="bigscience/T0_3B" | |
| try: | |
| from transformers import AutoModel, AutoTokenizer # Import AutoModel here | |
| model = ("bigscience/T0_3B") | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| generator = pipeline("text-generation", model=model, tokenizer=tokenizer) | |
| except EnvironmentError as e: | |
| return f"Error loading model: {e}" | |
| combined_input = f"{agent_prompt}\n\nUser: {input_text}\nAgent:" | |
| input_ids = tokenizer.encode(combined_input, return_tensors="pt") | |
| max_input_length = 900 | |
| if input_ids.shape[1] > max_input_length: | |
| input_ids = input_ids[:, :max_input_length] | |
| outputs = model.generate( | |
| input_ids, max_new_tokens=50, num_return_sequences=1, do_sample=True, | |
| pad_token_id=tokenizer.eos_token_id # Set pad_token_id to eos_token_id | |
| ) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return response | |
| # Terminal interface | |
| def terminal_interface(command, project_name=None): | |
| if project_name: | |
| project_path = os.path.join(PROJECT_ROOT, project_name) | |
| if not os.path.exists(project_path): | |
| return f"Project {project_name} does not exist." | |
| result = subprocess.run(command, shell=True, capture_output=True, text=True, cwd=project_path) | |
| else: | |
| result = subprocess.run(command, shell=True, capture_output=True, text=True) | |
| return result.stdout | |
| # Code editor interface | |
| def code_editor_interface(code): | |
| try: | |
| formatted_code = black.format_str(code, mode=black.FileMode()) | |
| except black.NothingChanged: | |
| formatted_code = code | |
| result = StringIO() | |
| sys.stdout = result | |
| sys.stderr = result | |
| (pylint_stdout, pylint_stderr) = lint.py_run(code, return_std=True) | |
| sys.stdout = sys.__stdout__ | |
| sys.stderr = sys.__stderr__ | |
| lint_message = pylint_stdout.getvalue() + pylint_stderr.getvalue() | |
| return formatted_code, lint_message | |
| # Text summarization tool | |
| def summarize_text(text): | |
| summarizer = pipeline("summarization") | |
| summary = summarizer(text, max_length=130, min_length=30, do_sample=False) | |
| return summary[0]['summary_text'] | |
| # Sentiment analysis tool | |
| def sentiment_analysis(text): | |
| analyzer = pipeline("sentiment-analysis") | |
| result = analyzer(text) | |
| return result[0]['label'] | |
| # Text translation tool (code translation) | |
| def translate_code(code, source_language, target_language): | |
| # Use a Hugging Face translation model instead of OpenAI | |
| translator = pipeline("translation", model="bartowski/Codestral-22B-v0.1-GGUF") # Example: English to Spanish | |
| translated_code = translator(code, target_lang=target_language)[0]['translation_text'] | |
| return translated_code | |
| def generate_code(code_idea, model_name): | |
| """Generates code using the selected model.""" | |
| try: | |
| generator = pipeline('text-generation', model=model_name) | |
| generated_code = generator(code_idea, max_length=1000, num_return_sequences=1)[0]['generated_text'] | |
| return generated_code | |
| except Exception as e: | |
| return f"Error generating code: {e}" | |
| def chat_interface(input_text): | |
| """Handles general chat interactions with the user.""" | |
| # Use a Hugging Face chatbot model or your own logic | |
| chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium") | |
| response = chatbot(input_text, max_length=50, num_return_sequences=1)[0]['generated_text'] | |
| return response | |
| # Workspace interface | |
| def workspace_interface(project_name): | |
| project_path = os.path.join(PROJECT_ROOT, project_name) | |
| if not os.path.exists(project_path): | |
| os.makedirs(project_path) | |
| st.session_state.workspace_projects[project_name] = {'files': []} | |
| return f"Project '{project_name}' created successfully." | |
| else: | |
| return f"Project '{project_name}' already exists." | |
| # Add code to workspace | |
| def add_code_to_workspace(project_name, code, file_name): | |
| project_path = os.path.join(PROJECT_ROOT, project_name) | |
| if not os.path.exists(project_path): | |
| return f"Project '{project_name}' does not exist." | |
| file_path = os.path.join(project_path, file_name) | |
| with open(file_path, "w") as file: | |
| file.write(code) | |
| st.session_state.workspace_projects[project_name]['files'].append(file_name) | |
| return f"Code added to '{file_name}' in project '{project_name}'." | |
| def create_space(api, name, description, public, files, entrypoint="launch.py"): | |
| url = f"{hf_hub_url()}spaces/{name}/prepare-repo" | |
| headers = {"Authorization": f"Bearer {api.access_token}"} | |
| payload = { | |
| "public": public, | |
| "gitignore_template": "web", | |
| "default_branch": "main", | |
| "archived": False, | |
| "files": [] | |
| } | |
| for filename, contents in files.items(): | |
| data = { | |
| "content": contents, | |
| "path": filename, | |
| "encoding": "utf-8", | |
| "mode": "overwrite" if "#\{random.randint(0, 1)\}" not in contents else "merge", | |
| } | |
| payload["files"].append(data) | |
| response = requests.post(url, json=payload, headers=headers) | |
| response.raise_for_status() | |
| location = response.headers.get("Location") | |
| # wait_for_processing(location, api) # You might need to implement this if it's not already defined | |
| return Repository(name=name, api=api) | |
| # Streamlit App | |
| st.title("AI Agent Creator") | |
| # Sidebar navigation | |
| st.sidebar.title("Navigation") | |
| app_mode = st.sidebar.selectbox("Choose the app mode", ["AI Agent Creator", "Tool Box", "Workspace Chat App"]) | |
| # Get Hugging Face token from secrets.toml | |
| hf_token = st.secrets["huggingface"]["hf_token"] | |
| if app_mode == "AI Agent Creator": | |
| # AI Agent Creator | |
| st.header("Create an AI Agent from Text") | |
| st.subheader("From Text") | |
| agent_name = st.text_input("Enter agent name:") | |
| text_input = st.text_area("Enter skills (one per line):") | |
| if st.button("Create Agent"): | |
| agent_prompt = create_agent_from_text(agent_name, text_input) | |
| st.success(f"Agent '{agent_name}' created and saved successfully.") | |
| st.session_state.available_agents.append(agent_name) | |
| elif app_mode == "Tool Box": | |
| # Tool Box | |
| st.header("AI-Powered Tools") | |
| # Chat Interface | |
| st.subheader("Chat with CodeCraft") | |
| chat_input = st.text_area("Enter your message:") | |
| if st.button("Send"): | |
| chat_response = chat_interface(chat_input) | |
| st.session_state.chat_history.append((chat_input, chat_response)) | |
| st.write(f"CodeCraft: {chat_response}") | |
| # Terminal Interface | |
| st.subheader("Terminal") | |
| terminal_input = st.text_input("Enter a command:") | |
| if st.button("Run"): | |
| terminal_output = terminal_interface(terminal_input) | |
| st.session_state.terminal_history.append((terminal_input, terminal_output)) | |
| st.code(terminal_output, language="bash") | |
| # Code Editor Interface | |
| st.subheader("Code Editor") | |
| code_editor = st.text_area("Write your code:", height=300) | |
| if st.button("Format & Lint"): | |
| formatted_code, lint_message = code_editor_interface(code_editor) | |
| st.code(formatted_code, language="python") | |
| st.info(lint_message) | |
| # Text Summarization Tool | |
| st.subheader("Summarize Text") | |
| text_to_summarize = st.text_area("Enter text to summarize:") | |
| if st.button("Summarize"): | |
| summary = summarize_text(text_to_summarize) | |
| st.write(f"Summary: {summary}") | |
| # Sentiment Analysis Tool | |
| st.subheader("Sentiment Analysis") | |
| sentiment_text = st.text_area("Enter text for sentiment analysis:") | |
| if st.button("Analyze Sentiment"): | |
| sentiment = sentiment_analysis(sentiment_text) | |
| st.write(f"Sentiment: {sentiment}") | |
| # Text Translation Tool (Code Translation) | |
| st.subheader("Translate Code") | |
| code_to_translate = st.text_area("Enter code to translate:") | |
| source_language = st.text_input("Enter source language (e.g., 'Python'):") | |
| target_language = st.text_input("Enter target language (e.g., 'JavaScript'):") | |
| if st.button("Translate Code"): | |
| translated_code = translate_code(code_to_translate, source_language, target_language) | |
| st.code(translated_code, language=target_language.lower()) | |
| # Code Generation | |
| st.subheader("Code Generation") | |
| code_idea = st.text_input("Enter your code idea:") | |
| if st.button("Generate Code"): | |
| generated_code = generate_code(code_idea) | |
| st.code(generated_code, language="python") | |
| elif app_mode == "Workspace Chat App": | |
| # Workspace Chat App | |
| st.header("Workspace Chat App") | |
| def get_built_space_files(): | |
| """ | |
| Gathers the necessary files for the Hugging Face Space, | |
| handling different project structures and file types. | |
| """ | |
| files = {} | |
| # Get the current project name (adjust as needed) | |
| project_name = st.session_state.get('project_name', 'my_project') | |
| project_path = os.path.join(PROJECT_ROOT, project_name) | |
| # Define a list of files/directories to search for | |
| targets = [ | |
| "app.py", | |
| "requirements.txt", | |
| "Dockerfile", | |
| "docker-compose.yml", # Example YAML file | |
| "src", # Example subdirectory | |
| "assets" # Another example subdirectory | |
| ] | |
| # Iterate through the targets | |
| for target in targets: | |
| target_path = os.path.join(project_path, target) | |
| # If the target is a file, add it to the files dictionary | |
| if os.path.isfile(target_path): | |
| add_file_to_dictionary(files, target_path) | |
| # If the target is a directory, recursively search for files within it | |
| elif os.path.isdir(target_path): | |
| for root, _, filenames in os.walk(target_path): | |
| for filename in filenames: | |
| file_path = os.path.join(root, filename) | |
| add_file_to_dictionary(files, file_path) | |
| return files | |
| def add_file_to_dictionary(files, file_path): | |
| """Helper function to add a file to the files dictionary.""" | |
| filename = os.path.relpath(file_path, PROJECT_ROOT) # Get relative path | |
| # Handle text and binary files | |
| if filename.endswith((".py", ".txt", ".json", ".html", ".css", ".yml", ".yaml")): | |
| with open(file_path, "r") as f: | |
| files[filename] = f.read() | |
| else: | |
| with open(file_path, "rb") as f: | |
| file_content = f.read() | |
| files[filename] = base64.b64encode(file_content).decode("utf-8") | |
| # Project Workspace Creation | |
| st.subheader("Create a New Project") | |
| project_name = st.text_input("Enter project name:") | |
| if st.button("Create Project"): | |
| workspace_status = workspace_interface(project_name) | |
| st.success(workspace_status) | |
| # Automatically create requirements.txt and app.py | |
| project_path = os.path.join(PROJECT_ROOT, project_name) | |
| requirements_file = os.path.join(project_path, "requirements.txt") | |
| if not os.path.exists(requirements_file): | |
| with open(requirements_file, "w") as f: | |
| f.write("# Add your project's dependencies here\n") | |
| app_file = os.path.join(project_path, "app.py") | |
| if not os.path.exists(app_file): | |
| with open(app_file, "w") as f: | |
| f.write("# Your project's main application logic goes here\n") | |
| # Add Code to Workspace | |
| st.subheader("Add Code to Workspace") | |
| code_to_add = st.text_area("Enter code to add to workspace:") | |
| file_name = st.text_input("Enter file name (e.g., 'app.py'):") | |
| if st.button("Add Code"): | |
| add_code_status = add_code_to_workspace(project_name, code_to_add, file_name) | |
| st.session_state.terminal_history.append((f"Add Code: {code_to_add}", add_code_status)) | |
| st.success(add_code_status) | |
| # Terminal Interface with Project Context | |
| st.subheader("Terminal (Workspace Context)") | |
| terminal_input = st.text_input("Enter a command within the workspace:") | |
| if st.button("Run Command"): | |
| terminal_output = terminal_interface(terminal_input, project_name) | |
| st.session_state.terminal_history.append((terminal_input, terminal_output)) | |
| st.code(terminal_output, language="bash") | |
| # Chat Interface for Guidance | |
| st.subheader("Chat with CodeCraft for Guidance") | |
| chat_input = st.text_area("Enter your message for guidance:") | |
| if st.button("Get Guidance"): | |
| chat_response = chat_interface(chat_input) | |
| st.session_state.chat_history.append((chat_input, chat_response)) | |
| st.write(f"CodeCraft: {chat_response}") | |
| # Display Chat History | |
| st.subheader("Chat History") | |
| for user_input, response in st.session_state.chat_history: | |
| st.write(f"User: {user_input}") | |
| st.write(f"CodeCraft: {response}") | |
| # Display Terminal History | |
| st.subheader("Terminal History") | |
| for command, output in st.session_state.terminal_history: | |
| st.write(f"Command: {command}") | |
| st.code(output, language="bash") | |
| # Display Projects and Files | |
| st.subheader("Workspace Projects") | |
| for project, details in st.session_state.workspace_projects.items(): | |
| st.write(f"Project: {project}") | |
| for file in details['files']: | |
| st.write(f" - {file}") | |
| # Chat with AI Agents | |
| st.subheader("Chat with AI Agents") | |
| selected_agent = st.selectbox("Select an AI agent", st.session_state.available_agents) | |
| agent_chat_input = st.text_area("Enter your message for the agent:") | |
| if st.button("Send to Agent"): | |
| agent_chat_response = chat_interface_with_agent(agent_chat_input, selected_agent) | |
| st.session_state.chat_history.append((agent_chat_input, agent_chat_response)) | |
| st.write(f"{selected_agent}: {agent_chat_response}") | |
| # Code Generation | |
| st.subheader("Code Generation") | |
| code_idea = st.text_input("Enter your code idea:") | |
| # Model Selection Menu | |
| selected_model = st.selectbox("Select a code-generative model", AVAILABLE_CODE_GENERATIVE_MODELS) | |
| if st.button("Generate Code"): | |
| generated_code = generate_code(code_idea, selected_model) | |
| st.code(generated_code, language="python") | |
| # Automate Build Process | |
| st.subheader("Automate Build Process") | |
| if st.button("Automate"): | |
| agent = AIAgent(selected_agent, "", []) # Load the agent without skills for now | |
| summary, next_step = agent.autonomous_build(st.session_state.chat_history, st.session_state.workspace_projects, project_name, selected_model) | |
| st.write("Autonomous Build Summary:") | |
| st.write(summary) | |
| st.write("Next Step:") | |
| st.write(next_step) | |
| # Using the modified and extended class and functions, update the callback for the 'Automate' button in the Streamlit UI: | |
| if st.button("Automate", args=(hf_token,)): | |
| agent = AIAgent(selected_agent, "", []) # Load the agent without skills for now | |
| summary, next_step = agent.autonomous_build(st.session_state.chat_history, st.session_state.workspace_projects, project_name, selected_model, hf_token) | |
| st.write("Autonomous Build Summary:") | |
| st.write(summary) | |
| st.write("Next Step:") | |
| st.write(next_step) | |
| # If everything went well, proceed to deploy the Space | |
| if agent._hf_api and agent.has_valid_hf_token(): | |
| agent.deploy_built_space_to_hf() | |
| # Use the hf_token to interact with the Hugging Face API | |
| api = HfApi(token=hf_token) | |
| # Function to create a Space on Hugging Face | |
| def create_space(api, name, description, public, files, entrypoint="launch.py"): | |
| url = f"{hf_hub_url()}spaces/{name}/prepare-repo" | |
| headers = {"Authorization": f"Bearer {api.access_token}"} | |