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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
image = Image.open('Wipro logo.png')
st.image(image)
st.title("Wipro impact | The inquisitive sustainability leader: Learn some of the best practices in sustainability from success stories of leading companies.. ")
st.header("Welcome!. Today, What company's sustainability story is inspiring you ?.. ")
myurl = st.text_input("Give the URL to find a sustainability or annual report", "https://www.wipro.com/content/dam/nexus/en/sustainability/sustainability_reports/wipro-sustainability-report-fy-2021-22.pdf")
yourquestion = st.text_input('Ask your question on best practices', 'What is Wipro plans for Biodiversity in 2024?')
st.write('Your input is ', yourquestion)
aimethod = st.radio(
"Choose a AI brain",
('GPT3', 'GPT3.5' ), index=1)
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" )
if myurl:
index = None
loader1 = PyPDFLoader(myurl)
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("loaded")
if yourquestion:
answer = index.query(llm=llmgpt3, question=yourquestion, chain_type="map_reduce")
st.write(answer)
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