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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", "Type URL of any company PDF document here") | |
| index = None | |
| if st.button("Load this report "): | |
| 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") | |
| st.write(langchainembeddings) | |
| st.write(index) | |
| 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 st.button("Ask Best practices "): | |
| answer = index.query(llm=llmchatgpt, question=yourquestion, chain_type="map_reduce") | |
| st.write(answer) | |
| aimethod = st.radio( | |
| "Choose a AI brain or document comprehension method", | |
| ('2 minutes AI method map_reduce', '4 minutes AI method refine' ), index=0) | |
| mychain_type = "map_reduce" | |
| if aimethod == '2 minutes AI method map_reduce': | |
| mychain_type = "map_reduce" | |
| if aimethod == '4 minutes AI method refine': | |
| mychain_type = "refine" | |
| loader1 = PyPDFLoader(url) | |
| def history(): | |
| mycount = 0 | |
| if 'count' not in st.session_state: | |
| st.session_state['count'] = 0 | |
| else: | |
| mycount = st.session_state['count'] | |
| if True: | |
| st.write(mycount) | |
| mycount = mycount + 1 | |
| st.session_state['count'] = mycount | |
| for i in range(mycount): | |
| mystatekeyindex = "element" + str(i) | |
| mystatekeyanswerindex = "elementANS" + str(i) | |
| if mystatekeyindex not in st.session_state: | |
| st.session_state[mystatekeyindex] = yourquestion | |
| st.session_state[mystatekeyanswerindex] = answer | |
| if mystatekeyindex in st.session_state: | |
| with st.expander(st.session_state[mystatekeyindex]): | |
| st.write( st.session_state[mystatekeyanswerindex] ) | |
| def colorizedtext(acolor, astring): | |
| formattedcolor = ":" + acolor + "[" + astring + "]" | |
| return formattedcolor | |
| if st.button("Ask QA "): | |
| documents = loader1.load() | |
| answer = "" | |
| with st.spinner(" Finding answer for your question .... AI will get you answer in 2 more minutes... " ): | |
| with st.expander( "Employing your choice of AI method ... " + aimethod + "..."): | |
| st.write(str(chain)[:700]) | |
| st.write("AI is reading this [link](%s)" % url) | |
| prgpgress = st.progress(0) | |
| st.subheader(colorizedtext("red", yourquestion)) | |
| for i in range(100): | |
| time.sleep(0.9) | |
| prgpgress.progress(i+1) | |
| answer = chain.run(input_documents=documents, question=yourquestion) | |
| st.subheader(colorizedtext("blue", answer)) | |
| history() | |