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import uvicorn |
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from fastapi import FastAPI, HTTPException |
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from fastapi.middleware.cors import CORSMiddleware |
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from huggingface_hub import InferenceClient |
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from pydantic import BaseModel |
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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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client = InferenceClient("Qwen/Qwen2.5-Coder-32B-Instruct") |
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class ChatRequest(BaseModel): |
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message: str |
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history: list[tuple[str, str]] |
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system_message: str |
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max_tokens: int |
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temperature: float |
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top_p: float |
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def generate_response(messages, max_tokens, temperature, top_p): |
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for chunk in client.chat_completion( |
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messages, |
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max_tokens=max_tokens, |
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stream=True, |
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temperature=temperature, |
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top_p=top_p, |
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): |
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yield chunk.choices[0].delta.content or "" |
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@app.post("/api/chat") |
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async def chat_stream(request: ChatRequest): |
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try: |
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messages = [{"role": "system", "content": request.system_message}] |
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for user_msg, assistant_msg in request.history: |
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messages.extend([ |
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{"role": "user", "content": user_msg}, |
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{"role": "assistant", "content": assistant_msg} |
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]) |
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messages.append({"role": "user", "content": request.message}) |
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return StreamingResponse( |
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generate_response( |
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messages=messages, |
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max_tokens=request.max_tokens, |
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temperature=request.temperature, |
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top_p=request.top_p |
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), |
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media_type="text/event-stream" |
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) |
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except Exception as e: |
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raise HTTPException(status_code=500, detail=str(e)) |
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if __name__ == "__main__": |
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uvicorn.run(app, host="0.0.0.0", port=7860) |
