from flask import Flask,render_template,jsonify,request from src.helper import * from src.prompt import * from langchain_groq import ChatGroq from langchain_community.document_loaders import WebBaseLoader from langchain_community.embeddings import OllamaEmbeddings from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.chains.combine_documents import create_stuff_documents_chain from langchain_core.prompts import ChatPromptTemplate from langchain.chains import create_retrieval_chain from langchain_community.vectorstores import FAISS from langchain_community.document_loaders import PyPDFLoader from langchain_community.document_loaders import PyPDFDirectoryLoader from langchain_community.embeddings import HuggingFaceBgeEmbeddings # from langchain.vectorstores.cassandra import Cassandra from langchain_community.vectorstores import Cassandra from langchain.prompts import PromptTemplate from langchain_community.llms import Ollama from cassandra.auth import PlainTextAuthProvider import tempfile import cassio from PyPDF2 import PdfReader from cassandra.cluster import Cluster import warnings warnings.filterwarnings("ignore") import os from dotenv import load_dotenv import time load_dotenv() app = Flask(__name__) groq_api_key=os.getenv('GROQ_API_KEY') LANGCHAIN_TRACING_V2="true" LANGCHAIN_API_KEY=os.getenv('LANGCHAIN_API_KEY') LANGCHAIN_PROJECT="medical_bot" LANGCHAIN_ENDPOINT="https://api.smith.langchain.com" prompt=PromptTemplate(template=prompt_template, input_variables=["context", "question"]) # print(PROMPT) llm=ChatGroq(groq_api_key=groq_api_key,model_name="mixtral-8x7b-32768") # file_path="data/Medical_book.pdf" # file_path='https://github.com/SrinidDev/Medical_GPT/blob/main/data/Medical_book.pdf' file_path='Medical_book.pdf' pinecone_vector_store=doc_loader(file_path) # print(type(pinecone_vector_store)) def generate_response(llm,prompt,pinecone_vector_store,question): # print('HELLO!Im from gen reponse fn') document_chain=create_stuff_documents_chain(llm,prompt) # print('document chain:',prompt) retriever=pinecone_vector_store.as_retriever(search_type="similarity",search_kwargs={"k":5}) # print('HELLO!Im after retriever') retrieval_chain=create_retrieval_chain(retriever,document_chain) # print('HELLO!Im after retrieval chain') response=retrieval_chain.invoke({"input":question}) # print('im response from fn',response) return response @app.route("/") def index(): print('Hello before chat html') # return "

Hello, Team!

" return render_template('chat.html') @app.route("/get", methods=["GET", "POST"]) def chat(): msg = request.form["msg"] question = msg print(question) result=generate_response(llm,prompt,pinecone_vector_store,question) # print("Response : ", result['answer']) return result['answer'] if __name__ == '__main__': app.run(debug= True)