from flask import Flask, render_template, jsonify, request
from src.helper import download_hugging_face_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
from src.prompt import *
import os


app = Flask(__name__)


load_dotenv(override=True)

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
if GOOGLE_API_KEY:
    os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY


embeddings = download_hugging_face_embeddings()

index_name = "medical-chatbot" 
# Embed each chunk and upsert the embeddings into your Pinecone index.
docsearch = PineconeVectorStore.from_existing_index(
    index_name=index_name,
    embedding=embeddings
)




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

chatModel = ChatGoogleGenerativeAI(model="gemini-2.5-flash")
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system_prompt),
        ("human", "{input}"),
    ]
)

question_answer_chain = create_stuff_documents_chain(chatModel, 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)
    try:
        response = rag_chain.invoke({"input": msg})
        print("Response : ", response["answer"])
        return str(response["answer"])
    except Exception as e:
        print("Error: ", str(e))
        return "Internal server error: Please check your API keys or try again later.", 500



if __name__ == '__main__':
    port = int(os.environ.get('PORT', 8080))
    app.run(host="0.0.0.0", port=port, debug=False)