app.py

from flask import Flask,render_template,jsonify,request
from src.helper import download_hugging_face_embedding
from langchain_pinecone import PineconeVectorStore
# from langchain_openai import OpenAI
from langchain_community.llms import Ollama

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")
# OPENAI_API_KEY=os.environ.get("OPENAI_API_KEY")

os.environ["PINECONE_API_KEY"]=PINECONE_API_KEY
# os.environ["OPENAI_API_KEY"]=OPENAI_API_KEY


embeddings=download_hugging_face_embedding()

index_name ="bot"

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

retriver=docsearch.as_retriever(search_type="similarity",search_kwargs={"k":3})


# llm=OpenAI(temperature=0.4,max_tokens=500)
llm = Ollama(model="gemma3:1b")  # Replace OpenAI with Ollama

prompt =ChatPromptTemplate.from_messages(
    [
        ("system",system_prompt),
        ("human","{input}")
    ]
)

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

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


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

@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=8000, debug=True)