Create app.py
Browse files
app.py
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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from sentence_transformers import SentenceTransformer
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from transformers import pipeline
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# Initialize FastAPI app
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app = FastAPI()
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# Load models
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model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
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question_model = "deepset/tinyroberta-squad2"
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nlp = pipeline('question-answering', model=question_model, tokenizer=question_model)
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# Define request models
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class ModifyQueryRequest(BaseModel):
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query_string: str
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class AnswerQuestionRequest(BaseModel):
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question: str
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context: str
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# Define response models (if needed)
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class ModifyQueryResponse(BaseModel):
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embeddings: list
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class AnswerQuestionResponse(BaseModel):
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answer: str
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# Define API endpoints
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@app.post("/modify_query", response_model=ModifyQueryResponse)
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async def modify_query(request: ModifyQueryRequest):
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try:
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binary_embeddings = model.encode([request.query_string], precision="binary")
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return ModifyQueryResponse(embeddings=binary_embeddings[0].tolist())
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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("/answer_question", response_model=AnswerQuestionResponse)
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async def answer_question(request: AnswerQuestionRequest):
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try:
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QA_input = {
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'question': request.question,
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'context': request.context
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}
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result = nlp(QA_input)
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return AnswerQuestionResponse(answer=result['answer'])
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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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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000)
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