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from flask import Flask, render_template, jsonify, request
from src.helper import download_hugging_face_embeddings
from langchain.vectorstores import Pinecone
import pinecone
from langchain.prompts import PromptTemplate
from langchain.llms import CTransformers
from langchain.chains import RetrievalQA
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')
PINECONE_API_ENV = os.environ.get('PINECONE_API_ENV')
embeddings = download_hugging_face_embeddings()
#Initializing the Pinecone
pinecone.init(api_key=PINECONE_API_KEY,
environment=PINECONE_API_ENV)
index_name="geeta-bot"
#Loading the index
docsearch=Pinecone.from_existing_index(index_name, embeddings)
PROMPT=PromptTemplate(template=prompt_template, input_variables=["context", "question"])
chain_type_kwargs={"prompt": PROMPT}
llm=CTransformers(model="model/llama-2-7b-chat.ggmlv3.q4_0.bin",
model_type="llama",
config={'max_new_tokens':512,
'temperature':0.8})
qa=RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=docsearch.as_retriever(search_kwargs={'k': 2}),
return_source_documents=True,
chain_type_kwargs=chain_type_kwargs)
@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)
result=qa({"query": input})
print("Response : ", result["result"])
return str(result["result"])
if __name__ == '__main__':
app.run(host="0.0.0.0", port= 7860, debug= True)
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