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, WebBaseLoader, SeleniumURLLoader
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()
global documents




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

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=150)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
vectordb = Chroma.from_documents(texts,embeddings)
chain = RetrievalQA.from_chain_type(llm=OpenAI(temperature=0.0),chain_type="stuff", retriever=vectordb.as_retriever(search_type="mmr"),return_source_documents=True)


app = flask.Flask(__name__, template_folder="./")
# Create a directory in a known location to save files to.
uploads_dir = os.path.join(app.root_path,'static', 'uploads')
os.makedirs(uploads_dir, exist_ok=True)


@app.route('/Home')
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'):
        
        userQuery = request.get_json()['query']
        responseJSON=chain({"query":userQuery});
        pretty_print_docs(responseJSON['source_documents'])
        print("Ques:>>>>"+userQuery+"\n Ans:>>>"+responseJSON["result"])
        return jsonify(botMessage=responseJSON["result"]); 
    else:
        return 'Content-Type not supported!'


@app.route('/file_upload',methods=['POST'])
def file_Upload():
        
        fileprovided=not request.files.getlist('files[]')[0].filename==''
        urlProvided=not request.form.getlist('weburl')[0]==''
        print("*******")
        print("File Provided:"+str(fileprovided))
        print("URL Provided:"+str(urlProvided))
        print("*******")
        print(not ('documents' in vars() or 'documents' in globals()))
        # if not ('documents' in vars() or 'documents' in globals()):
            
        if fileprovided:
            documents = []
            #Delete Files
            for filename in os.listdir(uploads_dir):
                file_path = os.path.join(uploads_dir, filename)
                print("Clearing Doc Directory. Trying to delete"+file_path)
                try:
                    if os.path.isfile(file_path) or os.path.islink(file_path):
                        os.unlink(file_path)
                    elif os.path.isdir(file_path):
                        shutil.rmtree(file_path)
                except Exception as e:
                    print('Failed to delete %s. Reason: %s' % (file_path, e))
            #Read and Embed New Files provided        
            for file in request.files.getlist('files[]'):
                print(file.filename)
                file.save(os.path.join(uploads_dir, secure_filename(file.filename)))
                loader = UnstructuredFileLoader(os.path.join(uploads_dir, secure_filename(file.filename)), mode='elements')
                documents.extend(loader.load())
        # else:
        #     loader = UnstructuredFileLoader('Jio.txt', mode='elements')
        #     documents.extend(loader.load())

        if urlProvided:
            urlList=request.form.getlist('weburl')
            print(urlList)
            urlLoader=SeleniumURLLoader(urlList)
            documents.extend(urlLoader.load())
        
        print(uploads_dir)
        global chain;

        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=150)
        texts = text_splitter.split_documents(documents)
        embeddings = OpenAIEmbeddings()
        vectordb = Chroma.from_documents(texts,embeddings)
        chain = RetrievalQA.from_chain_type(llm=OpenAI(temperature=0.0),chain_type="stuff", retriever=vectordb.as_retriever(search_type="mmr"),return_source_documents=True)
        
        return render_template("index.html")

@app.route('/')
def KBUpload():
    return render_template("KBTrain.html")

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

def pretty_print_docs(docs):
    print(f"\n{'-' * 100}\n".join([f"Document {i+1}:\n\n" + "Document Source>>> "+d.metadata['source']+"\n\nContent>>> "+d.page_content for i, d in enumerate(docs)]))



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