app.py

from flask import Flask,render_template,request
from src.helper import download_hugging_face_embeddings
from langchain.vectorstores import Pinecone
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from dotenv import load_dotenv
from src.prompt import *
import os


app=Flask(__name__)

load_dotenv()

PINECONE_API_KEY=os.environ.get('PINECONE_API_KEY')

embeddings = download_hugging_face_embeddings()

index_name = "medicalbot"

knowledgeBase= Pinecone.from_existing_index(
    index_name=index_name,
    embedding=embeddings
)
retriever = knowledgeBase.as_retriever(search_type="similarity", search_kwargs={"k":3})

llm = ChatGoogleGenerativeAI(model='gemini-1.5-pro',temperature=0.4,max_tokens=500)
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system_prompt),
        ("human", "{input}"),
    ]
)

question_answer_chain = create_stuff_documents_chain(llm, prompt)
rag_chain = create_retrieval_chain(retriever, question_answer_chain)

@app.route('/')
def index():
    return render_template('chat.html')

@app.route('/get',methods=['GET','POST'])
def chat():
    msg = request.form['msg']
    input = msg
    print(input)
    response = rag_chain.invoke({'input':msg})
    print('Response : ',response['answer'])
    return str(response['answer'])


if __name__ == '__main__':
    app.run(host='0.0.0.0',port=8080,debug=True)