Spaces:
Paused
Paused
Commit ·
e0bc5c6
1
Parent(s): 0a33686
streaming
Browse files
app.py
CHANGED
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@@ -1,7 +1,9 @@
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from fastapi import FastAPI, HTTPException
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from
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from huggingface_hub import InferenceClient
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from typing import List, Tuple
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# Initialisation du client Hugging Face
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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@@ -18,12 +20,11 @@ class PredictionRequest(BaseModel):
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temperature: float = 0.7
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top_p: float = 0.95
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async def
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"""
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"""
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# Préparer les messages pour l'inférence
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messages = [{"role": "system", "content": request.system_message}]
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for user_input, assistant_response in request.history:
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if user_input:
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@@ -32,21 +33,30 @@ async def predict(request: PredictionRequest):
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messages.append({"role": "assistant", "content": assistant_response})
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messages.append({"role": "user", "content": request.message})
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# Appel de l'API Hugging Face
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try:
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for message in client.chat_completion(
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messages,
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max_tokens=request.max_tokens,
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stream=True,
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temperature=request.temperature,
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top_p=request.top_p,
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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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# Pour le test en local
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if __name__ == "__main__":
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import uvicorn
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from fastapi import FastAPI, HTTPException
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from fastapi.responses import StreamingResponse
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from huggingface_hub import InferenceClient
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from pydantic import BaseModel
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from typing import List, Tuple
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import asyncio
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# Initialisation du client Hugging Face
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
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temperature: float = 0.7
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top_p: float = 0.95
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async def generate_stream(request: PredictionRequest):
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"""
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Générateur asynchrone pour le streaming de réponse.
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"""
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messages = [{"role": "system", "content": request.system_message}]
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for user_input, assistant_response in request.history:
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if user_input:
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messages.append({"role": "assistant", "content": assistant_response})
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messages.append({"role": "user", "content": request.message})
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try:
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async for message in client.chat_completion(
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messages,
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max_tokens=request.max_tokens,
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stream=True,
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temperature=request.temperature,
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top_p=request.top_p,
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):
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token = message.choices[0].delta.content
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yield token
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/predict")
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async def predict(request: PredictionRequest):
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"""
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Endpoint REST avec réponse en streaming.
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"""
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return StreamingResponse(
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generate_stream(request),
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media_type="text/plain" # Peut être changé en JSON si besoin
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)
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# Pour le test en local
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if __name__ == "__main__":
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import uvicorn
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