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app.py

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+import faiss
+from langchain_community.docstore.in_memory import InMemoryDocstore
+from langchain_community.vectorstores import FAISS
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_openai import OpenAIEmbeddings, ChatOpenAI
+import openai
+from openai import OpenAI
+from langchain_community.document_loaders.csv_loader import CSVLoader
+from pathlib import Path
+from langchain_openai import ChatOpenAI,OpenAIEmbeddings
+import os
+from dotenv import load_dotenv
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from scripts.rag import get_answer, response_agent
+# Load environment variables from a .env file
+
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Define a request model
+class QuestionRequest(BaseModel):
+    question: str
+
+@app.post("/ask")
+def ask_question(request: QuestionRequest):
+    try:
+        # Use the RAG system to get the answer
+        calculation_data = get_answer(rag_chain, request.question)
+        final_response = response_agent(request.question, calculation_data)
+        return {"answer": final_response}
+    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=5079)