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

import google.generativeai as genai


GOOGLE_API_KEY = 'AIzaSyDlpiD80XQIPdQV-u1bhBlc0zW3QxZuaEQ'

genai.configure(api_key=GOOGLE_API_KEY)


model = genai.GenerativeModel('gemini-pro')
#response = model.generate_content(query)
#return response.text

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": "Summarise this report", "chat_history": ''})
    summary =  result['answer']

    response = model.generate_content(summary + "\nBased on the information provided, what are the key medical insights and considerations for this patient?(100 words)")

    ans = {"summary": summary, "insights": response.text}

    return ans