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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 | CSRD | Advisor") | |
| st.header("Enable clients to prepare for CSRD.. ") | |
| genre = st.radio( | |
| "Choose a CSRD focus area for AI question answering", | |
| ('E1-Climate Change', 'E4-Biodiversity and ecosystem', 'S1-Own Workforce'), index=0) | |
| url = "https://www.efrag.org/Assets/Download?assetUrl=%2Fsites%2Fwebpublishing%2FSiteAssets%2F13%2520Draft%2520ESRS%2520S1%2520Own%2520workforce%2520November%25202022.pdf" | |
| if genre == 'E1-Climate Change': | |
| url = "https://www.efrag.org/Assets/Download?assetUrl=%2Fsites%2Fwebpublishing%2FSiteAssets%2F08%2520Draft%2520ESRS%2520E1%2520Climate%2520Change%2520November%25202022.pdf" | |
| samplequestions = ["What are the climate related indicators?", "List all the disclosure requirments listed in page 3?", "Policies related to climate change mitigation and adaptation?", | |
| "What should the company disclose regarding Actions and resources in relation to climate change policies?" , "How should the GHG emission reduction targets be reported?" , | |
| "Total energy consumption from non-renewable sources for high climate impact sectors should be disaggregated as ?", | |
| "Total energy consumption from renewable sources should be disaggregated as ?" , | |
| "What should be disclosed on GHG removals and GHG mitigation projects financed through carbon credits ?" , | |
| "Is it wise to invest on carbon credits? ", | |
| "What is Climate change adaptation? ", "What are Decarbonisation levers?" ] | |
| if genre == 'E4-Biodiversity and ecosystem': | |
| url = "https://www.efrag.org/Assets/Download?assetUrl=%2Fsites%2Fwebpublishing%2FSiteAssets%2F11%2520Draft%2520ESRS%2520E4%2520Biodiversity%2520and%2520ecosystems%2520November%25202022.pdf" | |
| samplequestions = ["What are the Biodiversity related indicators?", "List all the disclosure requirments listed in page 3?"] | |
| if genre == 'S1-Own Workforce': | |
| url = "https://www.efrag.org/Assets/Download?assetUrl=%2Fsites%2Fwebpublishing%2FSiteAssets%2F13%2520Draft%2520ESRS%2520S1%2520Own%2520workforce%2520November%25202022.pdf" | |
| samplequestions = ["What are the social related indicators?", "List all the disclosure requirments listed in page 3?"] | |
| sampleselectedquestion = st.selectbox( | |
| 'Just ask your question or start with a one of these example questions...', samplequestions ) | |
| st.write(" :green[ Ask any thing on your mind...just type your question here...]") | |
| yourquestion = st.text_input('Your question', sampleselectedquestion) | |
| st.write('Your typed .. ', yourquestion) | |
| 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" ) | |
| chain = load_qa_chain(llm=llmgpt3, chain_type="map_reduce") | |
| 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" | |
| chain = load_qa_chain(llm=llmgpt3, chain_type=mychain_type) | |
| 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() | |