Spaces:
Runtime error
Runtime error
| 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") | |
| st.header("Learn some of the best practices in sustainability from success stories of leading companies.. ") | |
| st.subheader("Welcome!. Today, What company's sustainability story is inspiring you ?.. ") | |
| 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" ) | |
| with st.form("my_form"): | |
| 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) | |
| # 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....") | |
| 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("indexed PDF...AI finding answer....please wait") | |
| if yourquestion: | |
| answer = index.query(llm=llmgpt3, question=yourquestion, chain_type="map_reduce") | |
| st.write(answer) | |