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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(temperature=0.0), chain_type="stuff", vectorstore=doc_search)
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'):
requestQuery = request.get_json()
response= chain.run(requestQuery['query'])
print("Ques:>>>>"+requestQuery['query']+"\n Ans:>>>"+response)
return jsonify(botMessage=response);
else:
return 'Content-Type not supported!'
@app.route('/file_upload',methods=['POST'])
def file_Upload():
print(request.files.getlist('files[]'))
print(request.files)
print(request.form)
print(request.form.getlist('weburl'))
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))
documents = []
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())
print(request.form.getlist('weburl'))
for url in request.form.getlist('weburl'):
print(type(url))
urlLoader=WebBaseLoader(url)
documents.extend(urlLoader.load())
print(uploads_dir)
global chain;
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
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())
return render_template("index.html")
@app.route('/')
def KBUpload():
return render_template("KBTrain.html")
@app.route('/aiassist')
def aiassist():
return render_template("index.html")
if __name__ == '__main__':
app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 7860)))
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