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Deploy to HF with corrected config
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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)