File size: 1,808 Bytes
7314527 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.prompts import ChatPromptTemplate
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain.chains import create_retrieval_chain
from src.helper import download_embeding
from langchain_pinecone import PineconeVectorStore
from src.prompt import system_prompt
import os
from flask import Flask, render_template, jsonify, request
from dotenv import load_dotenv
from pinecone import Pinecone
load_dotenv()
model = ChatGoogleGenerativeAI(model="gemini-2.5-flash", google_api_key=os.getenv("GOOGLE_API_KEY"))
app=Flask(__name__)
PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
pc = Pinecone(api_key=PINECONE_API_KEY)
index_name='medicalchatbot'
embedding=download_embeding()
docsearch=PineconeVectorStore.from_existing_index(
index_name=index_name,
embedding=embedding
)
retiver=docsearch.as_retriever(search_type="similarity", search_kwargs={"k": 3})
prompt = ChatPromptTemplate.from_messages(
[
("system", system_prompt),
("human", "{input}"),
]
)
question_answer_chain=create_stuff_documents_chain(model,prompt)
rag_chain=create_retrieval_chain(retiver,question_answer_chain)
@app.route("/")
def index():
return render_template('index.html')
@app.route("/get", methods=["GET", "POST"])
def chat():
msg = request.form.get("msg", "").strip()
if not msg:
return "Please enter a question.", 400
response = rag_chain.invoke({"input": msg})
answer = response.get("answer", "Sorry, I couldn't generate a response.")
print("Response:", answer)
return answer
if __name__ == '__main__':
port = int(os.environ.get("PORT", 7860))
app.run(host="0.0.0.0", port=port) |