app.py

import os
from langchain.chains import RetrievalQA
from langchain.llms import AzureOpenAI
from langchain.document_loaders import TextLoader
from langchain.document_loaders import PyPDFLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import AzureOpenAI
from langchain.chains.question_answering import load_qa_chain
import streamlit as st
from PIL import Image
import time
import random

@st.cache_data
def findanswer(Nand_url, Nand_question, randomnumber):
    if True:
      if Nand_url:
          index = None
          loader1 = PyPDFLoader(Nand_url)
          langchainembeddings = OpenAIEmbeddings(deployment="textembedding", chunk_size=1)

          index = VectorstoreIndexCreator(
                  # split the documents into chunks
                  text_splitter=CharacterTextSplitter(chunk_size=1000, chunk_overlap=0),
                  # select which embeddings we want to use
                  embedding=langchainembeddings,
                  # use Chroma as the vectorestore to index and search embeddings
                  vectorstore_cls=Chroma
              ).from_loaders([loader1])             
#          st.write("indexed PDF...AI finding answer....please wait")
      if Nand_question:
        answer = index.query(llm=llmgpt3, question=yourquestion, chain_type="map_reduce")
        return answer


          
image = Image.open('Wipro logo.png')
#st.image(image,   width=100)

st.write("Learn best practices in Data Centre Sustainability")




os.environ['OPENAI_API_TYPE'] = 'azure'
os.environ['OPENAI_API_VERSION'] = '2023-03-15-preview'

llmgpt3 = AzureOpenAI(      deployment_name="testdavanci", model_name="text-davinci-003" )
#llmchatgpt = AzureOpenAI(     deployment_name="esujnand", model_name="gpt-35-turbo" )

samplequestions = ["How can we reduce our carbon footprint to align with Carbon Disclosure Project requirements?",
                   "Is there a certification process for Ecovadis?", 
                   " What are the key components to track for Carbon Disclosure Project (CDP) reporting?", 
                   "How often must we report under GLOBAL REPORTING INITIATIVE (GRI)? Topic: GRI (Global Reporting Initiative)", 
                   "What key performance indicators (KPIs) should we focus on for GLOBAL REPORTING INITIATIVE (GRI) compliance? Topic:  GRI (Global Reporting Initiative) ",
                   "What information must we disclose under CSRD, in topic of Corporate Sustainability Reporting Directive(CSRD)",
                   "In the topic of Corporate Sustainability Reporting Directive (CSRD),  How can we ensure that our reporting under CSRD is consistent and comparable?",
                    ]


with st.form("my_form"):

   myurl = st.text_input("What is the URL?", "https://rajnandr.github.io/CuratedQA.pdf")

   yourquestion = st.selectbox(
    'Select',  samplequestions    )    

   # Every form must have a submit button.
   submitted = st.form_submit_button("Ask question")
   if submitted:
      #st.write("AI is looking for the answer...It will take atleast 2 mintutes... Answers will appear below....")
      randomnumber = random.randint(0, 1)
      Nandanswer = findanswer(myurl, yourquestion , randomnumber  )
      st.write(Nandanswer)