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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 langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
from operator import itemgetter
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')
GEMINI_API_KEY=os.environ.get('GEMINI_API_KEY')
os.environ["PINECONE_API_KEY"] = PINECONE_API_KEY
os.environ["GEMINI_API_KEY"] = GEMINI_API_KEY
embeddings = download_hugging_face_embeddings()
index_name = "medchatbot"
# 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})
llm = ChatGoogleGenerativeAI(
model="gemini-2.5-flash-lite",
google_api_key=GEMINI_API_KEY,
temperature=0.4,
max_output_tokens=4069
)
prompt = ChatPromptTemplate.from_messages(
[
("system", system_prompt),
("human", "{input}"),
]
)
# legacy chain
# question_answer_chain = create_stuff_documents_chain(llm, prompt)
# rag_chain = create_retrieval_chain(retriever, question_answer_chain)
translate_vi_to_en_chain = translate_vi_to_en_prompt | llm | StrOutputParser()
translate_en_to_vi_chain = translate_en_to_vi_prompt | llm | StrOutputParser()
rag_chain = (
RunnableLambda(lambda x: translate_vi_to_en_chain.invoke({"text": x["text"]}))
| RunnableLambda(lambda x: {"input": x})
| {
"context": lambda x: retriever.invoke(x["input"]),
"input": itemgetter("input"),
}
| prompt
| llm
| StrOutputParser()
| RunnableLambda(lambda x: translate_en_to_vi_chain.invoke({"text": x}))
)
@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)
response = rag_chain.invoke({"text": msg})
print("Response : ", response)
return response
if __name__ == '__main__':
app.run(host="0.0.0.0", port= 7860, debug= True) |