app.py

from fastapi import FastAPI, Request
from typing import List
import gradio as gr
import requests
import argparse
import aiohttp
import uvicorn
import random
import string
import json
import math
import sys
import os

API_BASE = "env"
api_key = os.environ['OPENAI_API_KEY']
base_url = os.environ.get('OPENAI_BASE_URL', "https://api.openai.com/v1")
def_models = '["gpt-4", "gpt-4-0125-preview", "gpt-4-0613", "gpt-4-1106-preview", "gpt-4-turbo", "gpt-4-turbo-2024-04-09", "gpt-4-turbo-preview", "chatgpt-4o-latest", "gpt-4o", "gpt-4o-2024-05-13", "gpt-4o-2024-08-06", "gpt-4o-mini", "gpt-4o-mini-2024-07-18"]'

def checkModels():
    global base_url
    if API_BASE == "env":
        try:
            response = requests.get(f"{base_url}/models", headers={"Authorization": f"Bearer {get_api_key()}"})
            response.raise_for_status()
            if not ('data' in response.json()):
                base_url = "https://api.openai.com/v1"
                api_key = oai_api_key
        except Exception as e:
            print(f"Error testing API endpoint: {e}")
    else:
        base_url = "https://api.openai.com/v1"
        api_key = oai_api_key

def loadModels():
    global models, modelList
    models = json.loads(def_models)
    models = sorted(models)
    
    modelList = {
        "object": "list",
        "data": [{"id": v, "object": "model", "created": 0, "owned_by": "system"} for v in models]
    }

def handleApiKeys():
    global api_key
    if ',' in api_key:
        output = []
        for key in api_key.split(','):
            try:
                response = requests.get(f"{base_url}/models", headers={"Authorization": f"Bearer {key}"})
                response.raise_for_status()
                if ('data' in response.json()):
                    output.append(key)
            except Exception as e:
                print((F"API key {key} is not valid or an actuall error happend {e}"))
        if len(output)==1:
            raise RuntimeError("No API key is working")
        api_key = ",".join(output)
    else:
        try:
            response = requests.get(f"{base_url}/models", headers={"Authorization": f"Bearer {api_key}"})
            response.raise_for_status()
            if not ('data' in response.json()):
                raise RuntimeError("Current API key is not valid")
        except Exception as e:
            raise RuntimeError(f"Current API key is not valid or an actual error happened: {e}")

def encodeChat(messages):
    output = []
    for message in messages:
        role = message['role']
        name = f" [{message['name']}]" if 'name' in message else ''
        content = message['content']
        formatted_message = f"<|im_start|>{role}{name}\n{content}<|end_of_text|>"
        output.append(formatted_message)
    return "\n".join(output)

def get_api_key(call='api_key'):
    if call == 'api_key':
        key = api_key
    elif call == 'oai_api_key':
        key = oai_api_key
    else:
        key = api_key

    if ',' in key:
        return random.choice(key.split(','))
    return key

def moderate(messages):
    try:
        response = requests.post(
            f"{base_url}/moderations",
            headers={
                "Content-Type": "application/json",
                "Authorization": f"Bearer {get_api_key(call='api_key')}"
            },
            json={"input": encodeChat(messages)}
        )
        response.raise_for_status()
        moderation_result = response.json()
    except requests.exceptions.RequestException as e:
        print(f"Error during moderation request to {base_url}: {e}")
        try:
            response = requests.post(
                "https://api.openai.com/v1/moderations",
                headers={
                    "Content-Type": "application/json",
                    "Authorization": f"Bearer {get_api_key(call='oai_api_key')}"
                },
                json={"input": encodeChat(messages)}
            )
            response.raise_for_status()
            moderation_result = response.json()
        except requests.exceptions.RequestException as e:
            print(f"Error during moderation request to fallback URL: {e}")
            return False

    try:
        if any(result["flagged"] for result in moderation_result["results"]):
            return moderation_result
    except KeyError:
        if moderation_result["flagged"]:
            return moderation_result
    
    return False

async def streamChat(params):
        async with aiohttp.ClientSession() as session:
            try:
                async with session.post(f"{base_url}/chat/completions", headers={"Authorization": f"Bearer {get_api_key(call='api_key')}", "Content-Type": "application/json"}, json=params) as r:
                    r.raise_for_status()
                    async for line in r.content:
                        if line:
                            line_str = line.decode('utf-8')
                            if line_str.startswith("data: "):
                                line_str = line_str[6:].strip()
                            if line_str == "[DONE]":
                                continue
                            try:
                                message = json.loads(line_str)
                                yield message
                            except json.JSONDecodeError:
                                continue
            except aiohttp.ClientError:
                try:
                    async with session.post("https://api.openai.com/v1/chat/completions", headers={"Authorization": f"Bearer {get_api_key(call='oai_api_key')}", "Content-Type": "application/json"}, json=params) as r:
                        r.raise_for_status()
                        async for line in r.content:
                            if line:
                                line_str = line.decode('utf-8')
                                if line_str.startswith("data: "):
                                    line_str = line_str[6:].strip()
                                if line_str == "[DONE]":
                                    continue
                                try:
                                    message = json.loads(line_str)
                                    yield message
                                except json.JSONDecodeError:
                                    continue
                except aiohttp.ClientError:
                    return

def rnd(length=8):
    letters = string.ascii_letters + string.digits
    return ''.join(random.choice(letters) for i in range(length))


async def respond(
    message,
    history: list[tuple[str, str]],
    model_name,
    max_tokens,
    temperature,
    top_p,
):
    messages = [];

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    if message:
        mode = moderate(messages)
        if mode:
            reasons = []
            categories = mode[0].get('categories', {}) if isinstance(mode, list) else mode.get('categories', {})
            for category, flagged in categories.items():
                if flagged:
                    reasons.append(category)
            if reasons:
                yield "[MODERATION] I'm sorry, but I can't assist with that.\n\nReasons:\n```\n" + "\n".join([f"{i+1}. {reason}" for i, reason in enumerate(reasons)]) + "\n```"
            else:
                yield "[MODERATION] I'm sorry, but I can't assist with that."
            return
    
    async def handleResponse(completion, prefix="", image_count=0, didSearchedAlready=False):
        response = ""
        isRequeryNeeded = False
        async for token in completion:
            response += token['choices'][0]['delta'].get("content", token['choices'][0]['delta'].get("refusal", ""))
            yield f"{prefix}{response}"
        mode = moderate([handleMultimodalData(model_name, "user", message),{"role": "assistant", "content": response}])
        if mode:
            reasons = []
            categories = mode[0].get('categories', {}) if isinstance(mode, list) else mode.get('categories', {})
            for category, flagged in categories.items():
                if flagged:
                    reasons.append(category)
            if reasons:
                yield "[MODERATION] I'm sorry, but I can't assist with that.\n\nReasons:\n```\n" + "\n".join([f"{i+1}. {reason}" for i, reason in enumerate(reasons)]) + "\n```"
            else:
                yield "[MODERATION] I'm sorry, but I can't assist with that."
            return
        for line in response.split('\n'):
            try:
                data = json.loads(line)
                if isinstance(data, dict) and data.get("tool") == "imagine" and data.get("isCall") and "prompt" in data:
                    if image_count < 4:
                        image_count += 1
                        def fetch_image_url(prompt, line):
                            image_url = imagine(prompt)
                            return line, f'<img src="{image_url}" alt="{prompt}" width="512"/>'

                        def replace_line_in_response(line, replacement):
                            nonlocal response
                            response = response.replace(line, replacement)

                        thread = threading.Thread(target=lambda: replace_line_in_response(*fetch_image_url(data["prompt"], line)))
                        thread.start()
                        thread.join()
                    else:
                        response = response.replace(line, f'[System: 4 image per message limit; prompt asked: `{data["prompt"]}]`')
                    yield f"{prefix}{response}"
                elif isinstance(data, dict) and data.get("tool") == "calc" and data.get("isCall") and "prompt" in data:
                    isRequeryNeeded = True
                    try:
                        result = safe_eval(data["prompt"])
                        response = response.replace(line, f'[System: `{data["prompt"]}` === `{result}`]')
                    except Exception as e:
                        response = response.replace(line, f'[System: Error in calculation; `{e}`]')
                    yield f"{prefix}{response}"
                elif isinstance(data, dict) and data.get("tool") == "search" and data.get("isCall") and "prompt" in data:
                    isRequeryNeeded = True
                    if didSearchedAlready:
                        response = response.replace(line, f'[System: One search per response is allowed; due to how long and resource it takes; query: `{data["prompt"]}]`]')
                    else:
                        try:
                            result = searchEngine(data["prompt"])
                            result_escaped = result.replace('`', '\\`')
                            response = response.replace(line, f'[System: `{data["prompt"]}` ===\n```\n{result_escaped}\n```\n]')
                            didSearchedAlready = True
                        except Exception as e:
                            response = response.replace(line, f'[System: Error in search function; `{e}`]')
                        yield f"{prefix}{response}"
                    yield f"{prefix}{response}"
            except (json.JSONDecodeError, AttributeError, Exception):
                continue
            if isRequeryNeeded:
                messages.append({"role": "assistant", "content": response})
                async for res in handleResponse(streamChat({
                    "model": model_name,
                    "messages": messages,
                    "max_tokens": max_tokens,
                    "temperature": temperature,
                    "top_p": top_p,
                    "user": rnd(),
                    "stream": True
                }), f"{prefix}{response}\n\n", image_count, didSearchedAlready):
                    yield res
    async for res in handleResponse(streamChat({
        "model": model_name,
        "messages": messages,
        "max_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p,
        "user": rnd(),
        "stream": True
    })):
        yield res
        

handleApiKeys();loadModels();checkModels();
demo = gr.ChatInterface(
    respond,
    title="gpt-4o-mini-small",
    description=f"This is the smaller version of quardo/gpt-4o-small space.<br/>Mainly exists when the main space is down.",
    additional_inputs=[
        gr.Dropdown(choices=models, value="gpt-4o-mini", label="Model"),
        gr.Slider(minimum=1, maximum=4096, value=4096, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.05, label="Temperature"),
        gr.Slider(minimum=0.05, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
    ],
    css="footer{display:none !important}",
    head="""<script>if(!confirm("By using our application, which integrates with OpenAI's API, you acknowledge and agree to the following terms regarding the data you provide:\\n\\n1. Data Collection: This application may log the following data through the Gradio endpoint or the API endpoint: message requests (including messages, responses, model settings, and images sent along with the messages), images that were generated (including only the prompt and the image), search tool calls (including query, search results, summaries, and output responses), and moderation checks (including input and output).\\n2. Data Retention and Removal: Data is retained until further notice or until a specific request for removal is made.\\n3. Data Usage: The collected data may be used for various purposes, including but not limited to, administrative review of logs, AI training, and publication as a dataset.\\n4. Privacy: Please avoid sharing any personal information.\\n\\nBy continuing to use our application, you explicitly consent to the collection, use, and potential sharing of your data as described above. If you disagree with our data collection, usage, and sharing practices, we advise you not to use our application."))location.href="/declined";</script>"""
)

app = FastAPI()

@app.get("/declined")
def test():
    return HTMLResponse(content="""
        <html>
            <head>
                <title>Declined</title>
            </head>
            <body>
                <p>Ok, you can go back to Hugging Face. I just didn't have any idea how to handle decline so you are redirected here.</p><br/>
                <a href="/">Go back</button>
            </body>
        </html>
    """)

app = gr.mount_gradio_app(app, demo, path="/")

class ArgParser(argparse.ArgumentParser):
    def __init__(self, *args, **kwargs):
        super(ArgParser, self).__init__(*args, **kwargs)

        self.add_argument("-s", "--server", type=str, default="0.0.0.0")
        self.add_argument("-p", "--port", type=int, default=7860)
        self.add_argument("-d", "--dev", default=False, action="store_true")

        self.args = self.parse_args(sys.argv[1:])

if __name__ == "__main__":
    args = ArgParser().args
    if args.dev:
        uvicorn.run("__main__:app", host=args.server, port=args.port, reload=True)
    else:
        uvicorn.run("__main__:app", host=args.server, port=args.port, reload=False)