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import gradio as gr |
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from fastapi import FastAPI, Request |
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from fastapi.responses import JSONResponse |
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from fastapi.middleware.cors import CORSMiddleware |
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import os |
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from huggingface_hub import InferenceClient |
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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_credentials=True, |
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allow_methods=["*"], |
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allow_headers=["*"], |
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) |
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client = InferenceClient(token=os.getenv("HF_TOKEN")) |
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@app.post("/api/chat") |
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async def chat_api(request: Request): |
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try: |
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data = await request.json() |
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user_msg = data.get("message", "") |
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history = data.get("history", []) |
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SYSTEM_PROMPT = """Sen ZenkaMind 1.1'sin. Tek Yaratıcın: Berkay Adıgül. |
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TÜRKÇE konuş. Mahallenin ağır abisi karakterindesin.""" |
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messages = [{"role": "system", "content": SYSTEM_PROMPT}] |
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for msg in history: |
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if len(msg) >= 2: |
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messages.append({"role": "user", "content": str(msg[0])}) |
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messages.append({"role": "assistant", "content": str(msg[1])}) |
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messages.append({"role": "user", "content": user_msg}) |
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full_response = "" |
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stream = client.chat_completion( |
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model="Qwen/Qwen2.5-7B-Instruct", |
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messages=messages, |
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max_tokens=500, |
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stream=True, |
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temperature=0.7 |
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) |
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for chunk in stream: |
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if chunk.choices and chunk.choices[0].delta.content: |
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full_response += chunk.choices[0].delta.content |
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return JSONResponse({"response": full_response}) |
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except Exception as e: |
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return JSONResponse({"response": f"⚠️ Sistem meşgul: {str(e)}"}) |
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def simple_chat(message, history): |
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return "HTML arayüzü için /api/chat endpoint'ini kullanın" |
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demo = gr.ChatInterface( |
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simple_chat, |
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title="ZenkaMind", |
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description="Ana uygulama için HTML frontend kullanın" |
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) |
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app = gr.mount_gradio_app(app, demo, path="/") |
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@app.get("/") |
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async def home(): |
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return {"status": "ZenkaMind API çalışıyor", "endpoint": "/api/chat"} |
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if __name__ == "__main__": |
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import uvicorn |
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uvicorn.run(app, host="0.0.0.0", port=7860) |
