app.py

import openai
import numpy as np
import pandas as pd
import os
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain import HuggingFaceHub
from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.chains import VectorDBQA
from langchain.document_loaders import TextLoader
from langchain.document_loaders import UnstructuredFileLoader
from flask import Flask, jsonify, render_template, request
from werkzeug.utils import secure_filename
from werkzeug.datastructures import  FileStorage
import nltk
nltk.download("punkt")
import warnings
warnings.filterwarnings("ignore")



openai.api_key=os.getenv("OPENAI_API_KEY")


import flask
import os
from dotenv import load_dotenv
load_dotenv()


loader = UnstructuredFileLoader('Jio.txt', mode='elements')
documents= loader.load()

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
doc_search = Chroma.from_documents(texts,embeddings)
chain = VectorDBQA.from_chain_type(llm=OpenAI(), chain_type="stuff", vectorstore=doc_search)

app = flask.Flask(__name__, template_folder="./")


@app.route('/')
def index():
    return flask.render_template('index.html')

@app.route('/post_json', methods=['POST'])
def process_json():
    content_type = request.headers.get('Content-Type')
    if (content_type == 'application/json'):
        requestQuery = request.get_json()
        print("Ques:>>>>"+requestQuery['query']+"\n Ans:>>>"+chain.run(requestQuery['query']))
        return jsonify(botMessage=chain.run(requestQuery['query'])); 
    else:
        return 'Content-Type not supported!'

if __name__ == '__main__':
    app.run(host='0.0.0.0',  port=int(os.environ.get('PORT', 7860)))