app.py

from flask import Flask, render_template, request
from src.helper import download_embeddings
from langchain_pinecone import PineconeVectorStore
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
import os
from src.prompt import system_prompt

app = Flask(__name__)

load_dotenv()
PINECONE_API_KEY = os.environ.get("PINECONE_API_KEY")
GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")

os.environ["PINECONE_API_KEY"] = PINECONE_API_KEY
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY

embeddings = download_embeddings()

index_name = "medbot"

docSearch = PineconeVectorStore.from_existing_index(
    index_name=index_name,
    embedding=embeddings
)

retriever = docSearch.as_retriever(search_type='similarity', search_kwargs = {'k':3})

llm = ChatGoogleGenerativeAI(
    model="gemini-1.5-pro",
    temperature=0.4,  
    max_tokens=4096,
    timeout=60,
    max_retries=3,
    top_p=0.8,
    top_k=40
)

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('index.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)