Create app.py

app.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer
+from transformers import pipeline
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Load models
+model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+question_model = "deepset/tinyroberta-squad2"
+nlp = pipeline('question-answering', model=question_model, tokenizer=question_model)
+
+# Define request models
+class ModifyQueryRequest(BaseModel):
+    query_string: str
+
+class AnswerQuestionRequest(BaseModel):
+    question: str
+    context: str
+
+# Define response models (if needed)
+class ModifyQueryResponse(BaseModel):
+    embeddings: list
+
+class AnswerQuestionResponse(BaseModel):
+    answer: str
+
+# Define API endpoints
+@app.post("/modify_query", response_model=ModifyQueryResponse)
+async def modify_query(request: ModifyQueryRequest):
+    try:
+        binary_embeddings = model.encode([request.query_string], precision="binary")
+        return ModifyQueryResponse(embeddings=binary_embeddings[0].tolist())
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/answer_question", response_model=AnswerQuestionResponse)
+async def answer_question(request: AnswerQuestionRequest):
+    try:
+        QA_input = {
+            'question': request.question,
+            'context': request.context
+        }
+        result = nlp(QA_input)
+        return AnswerQuestionResponse(answer=result['answer'])
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)