main.py

from fastapi import FastAPI, HTTPException, Header, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
import openai
from typing import List, Optional,Union
import logging
from itertools import cycle
import asyncio

import uvicorn

from app import config
import requests
from datetime import datetime, timezone

# 配置日志
logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)

app = FastAPI()

# 允许跨域
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# API密钥配置
API_KEYS = config.settings.API_KEYS

# 创建一个循环迭代器
key_cycle = cycle(API_KEYS)
key_lock = asyncio.Lock()


class ChatRequest(BaseModel):
    messages: List[dict]
    model: str = "llama-3.2-90b-text-preview"
    temperature: Optional[float] = 0.7
    stream: Optional[bool] = False
    tools: Optional[List[dict]] = []
    tool_choice: Optional[str] = "auto"


class EmbeddingRequest(BaseModel):
    input: Union[str, List[str]]
    model: str = "text-embedding-004"
    encoding_format: Optional[str] = "float"


async def verify_authorization(authorization: str = Header(None)):
    if not authorization:
        logger.error("Missing Authorization header")
        raise HTTPException(status_code=401, detail="Missing Authorization header")
    if not authorization.startswith("Bearer "):
        logger.error("Invalid Authorization header format")
        raise HTTPException(
            status_code=401, detail="Invalid Authorization header format"
        )
    token = authorization.replace("Bearer ", "")
    if token not in config.settings.ALLOWED_TOKENS:
        logger.error("Invalid token")
        raise HTTPException(status_code=401, detail="Invalid token")
    return token


def get_gemini_models(api_key):
    base_url = "https://generativelanguage.googleapis.com/v1beta"
    url = f"{base_url}/models?key={api_key}"
    
    try:
        response = requests.get(url)
        if response.status_code == 200:
            gemini_models = response.json()
            return convert_to_openai_format(gemini_models)
        else:
            print(f"Error: {response.status_code}")
            print(response.text)
            return None
    
    except requests.RequestException as e:
        print(f"Request failed: {e}")
        return None

def convert_to_openai_format(gemini_models):
    openai_format = {
        "object": "list",
        "data": []
    }
    
    for model in gemini_models.get('models', []):
        openai_model = {
            "id": model['name'].split('/')[-1],  # 取最后一部分作为ID
            "object": "model",
            "created": int(datetime.now(timezone.utc).timestamp()),  # 使用当前时间戳
            "owned_by": "google",  # 假设所有Gemini模型都由Google拥有
            "permission": [],  # Gemini API可能没有直接对应的权限信息
            "root": model['name'],
            "parent": None,  # Gemini API可能没有直接对应的父模型信息
        }
        openai_format["data"].append(openai_model)
    
    return openai_format
        

@app.get("/v1/models")
@app.get("/hf/v1/models")
async def list_models(authorization: str = Header(None)):
    await verify_authorization(authorization)
    async with key_lock:
        api_key = next(key_cycle)
        logger.info(f"Using API key: {api_key}")
    try:
        response = get_gemini_models(api_key)
        logger.info("Successfully retrieved models list")
        return response
    except Exception as e:
        logger.error(f"Error listing models: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))


@app.post("/v1/chat/completions")
@app.post("/hf/v1/chat/completions")
async def chat_completion(request: ChatRequest, authorization: str = Header(None)):
    await verify_authorization(authorization)
    async with key_lock:
        api_key = next(key_cycle)
        logger.info(f"Using API key: {api_key}")

    try:
        logger.info(f"Chat completion request - Model: {request.model}")
        client = openai.OpenAI(api_key=api_key, base_url=config.settings.BASE_URL)
        response = client.chat.completions.create(
            model=request.model,
            messages=request.messages,
            temperature=request.temperature,
            stream=request.stream if hasattr(request, "stream") else False,
        )

        if hasattr(request, "stream") and request.stream:
            logger.info("Streaming response enabled")

            async def generate():
                for chunk in response:
                    yield f"data: {chunk.model_dump_json()}\n\n"

            return StreamingResponse(content=generate(), media_type="text/event-stream")

        logger.info("Chat completion successful")
        return response

    except Exception as e:
        logger.error(f"Error in chat completion: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))


@app.post("/v1/embeddings")
@app.post("/hf/v1/embeddings")
async def embedding(request: EmbeddingRequest, authorization: str = Header(None)):
    await verify_authorization(authorization)
    async with key_lock:
        api_key = next(key_cycle)
        logger.info(f"Using API key: {api_key}")

    try:
        client = openai.OpenAI(api_key=api_key, base_url=config.settings.BASE_URL)
        response = client.embeddings.create(input=request.input, model=request.model)
        logger.info("Embedding successful")
        return response
    except Exception as e:
        logger.error(f"Error in embedding: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))


@app.get("/health")
@app.get("/")
async def health_check():
    logger.info("Health check endpoint called")
    return {"status": "healthy"}


if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)