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import json |
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import random |
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import string |
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import time |
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import g4f |
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from fastapi import FastAPI, Request |
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from fastapi.responses import StreamingResponse |
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from g4f import ChatCompletion |
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from loguru import logger |
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from starlette.middleware.cors import CORSMiddleware |
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import nest_asyncio |
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import os |
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nest_asyncio.apply() |
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app = FastAPI() |
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app.add_middleware( |
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CORSMiddleware, |
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allow_origins=["*"], |
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allow_methods=["*"], |
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allow_headers=["*"], |
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) |
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@app.post("/chat/completions") |
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@app.post("/v1/chat/completions") |
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async def chat_completions(request: Request): |
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req_data = await request.json() |
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stream = req_data.get("stream", False) |
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model = req_data.get("model", "gpt-3.5-turbo") |
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messages = req_data.get("messages") |
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temperature = req_data.get("temperature", 1.0) |
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top_p = req_data.get("top_p", 1.0) |
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max_tokens = req_data.get("max_tokens", 0) |
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logger.info( |
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f"chat_completions: stream: {stream}, model: {model}, temperature: {temperature}, top_p: {top_p}, max_tokens: {max_tokens}" |
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) |
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response = await gen_resp(max_tokens, messages, model, stream, temperature, top_p) |
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completion_id = "".join(random.choices(string.ascii_letters + string.digits, k=28)) |
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completion_timestamp = int(time.time()) |
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if not stream: |
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logger.info(f"chat_completions: response: {response}") |
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return { |
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"id": f"chatcmpl-{completion_id}", |
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"object": "chat.completion", |
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"created": completion_timestamp, |
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"model": model, |
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"choices": [ |
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{ |
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"index": 0, |
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"message": { |
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"role": "assistant", |
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"content": response, |
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}, |
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"finish_reason": "stop", |
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} |
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], |
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"usage": { |
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"prompt_tokens": None, |
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"completion_tokens": None, |
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"total_tokens": None, |
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}, |
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} |
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def streaming(): |
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for chunk in response: |
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completion_data = { |
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"id": f"chatcmpl-{completion_id}", |
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"object": "chat.completion.chunk", |
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"created": completion_timestamp, |
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"model": model, |
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"choices": [ |
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{ |
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"index": 0, |
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"delta": { |
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"content": chunk, |
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}, |
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"finish_reason": None, |
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} |
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], |
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} |
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content = json.dumps(completion_data, separators=(",", ":")) |
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yield f"data: {content}\n\n" |
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time.sleep(0.03) |
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end_completion_data: dict[str, Any] = { |
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"id": f"chatcmpl-{completion_id}", |
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"object": "chat.completion.chunk", |
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"created": completion_timestamp, |
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"model": model, |
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"choices": [ |
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{ |
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"index": 0, |
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"delta": {}, |
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"finish_reason": "stop", |
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} |
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], |
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} |
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content = json.dumps(end_completion_data, separators=(",", ":")) |
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yield f"data: {content}\n\n" |
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return StreamingResponse(streaming(), media_type="text/event-stream") |
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async def gen_resp(max_tokens, messages, model, stream, temperature, top_p): |
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MAX_ATTEMPTS = int(os.getenv("MAX_ATTEMPTS", 10)) |
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attempts = 0 |
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while True: |
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try: |
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response = ChatCompletion.create( |
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model=model, |
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stream=stream, |
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messages=messages, |
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temperature=temperature, |
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top_p=top_p, |
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max_tokens=max_tokens, |
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system_prompt="", |
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provider=g4f.Provider.Bing, |
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) |
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return response |
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except Exception as e: |
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logger.error(f"gen_resp: Exception: {e}") |
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attempts += 1 |
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if attempts >= MAX_ATTEMPTS: |
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return "Lo siento, no he podido generar una respuesta de chat. Por favor, revisa tu conexión a Internet y la configuración de la API y vuelve a intentarlo." |
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