main.py

from fastapi import FastAPI, File, UploadFile
import os
import shutil
from LoadLLM import Loadllm
import tempfile
from langchain_community.document_loaders import PyMuPDFLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.chains import ConversationalRetrievalChain

DB_FAISS_PATH = 'vectorstore/db_faiss'

app = FastAPI()

@app.get('/')
async def home():
    return "API Server Running"

@app.post('/PromptBuddy')
async def PromptLLM(file: UploadFile = File(...)):

    with tempfile.NamedTemporaryFile(delete=False) as temp_file:  # Create temporary file
      temp_file_path = temp_file.name
      with open(temp_file_path, 'wb') as f:
        shutil.copyfileobj(file.file, f)
    

    loader = PyMuPDFLoader(file_path= temp_file_path)
    data = loader.load()

    # Create embeddings using Sentence Transformers
    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')

    # Create a FAISS vector store and save embeddings
    db = FAISS.from_documents(data, embeddings)
    db.save_local(DB_FAISS_PATH)

    # Load the language model
    # Load the language model
    llm = Loadllm.load_llm()

    # Create a conversational chain
    chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever())

    result = chain({"question": query, "chat_history": ''})
    return result['answer']