| import streamlit as st |
| from langchain import OpenAI, PromptTemplate, LLMChain |
| from langchain.text_splitter import CharacterTextSplitter |
| from langchain.chains.mapreduce import MapReduceChain |
| from langchain.prompts import PromptTemplate |
| from langchain.chat_models import AzureChatOpenAI |
| from langchain.chains.summarize import load_summarize_chain |
| from langchain.chains import AnalyzeDocumentChain |
| from PyPDF2 import PdfReader |
| from langchain.document_loaders import TextLoader |
| from langchain.indexes import VectorstoreIndexCreator |
| from langchain.document_loaders import PyPDFLoader |
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|
| import os |
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| os.environ["OPENAI_API_TYPE"] = "azure" |
| os.environ["OPENAI_API_VERSION"] = "2023-03-15-preview" |
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| |
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|
| llm = AzureChatOpenAI( |
| deployment_name="esujnand", model_name="gpt-35-turbo" |
| ) |
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| st.title("Wipro CSRD AI 1") |
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| |
| st.write("Step 1: Summary of your selected section of CSRD... Sections in this are enviormental topic1, enviornamtal topic2 ") |
| st.write("Step 2: Ask your specfici questions regarding a CSRD disclosure requirments") |
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| |
| pdf_file = st.file_uploader("Upload file", type=["pdf"]) |
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| numberofpages = 100 |
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|
| if st.button("How many pages? "): |
| reader = PdfReader(pdf_file) |
| numberofpages = len(reader.pages) |
| st.write("length is ", numberofpages) |
|
|
| if st.button("table of contents? "): |
| reader = PdfReader(pdf_file) |
| page = reader.pages[2].extract_text() |
| st.write(page) |
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| startpage = st.slider('Which section to look at', 0, numberofpages, 1) |
| st.write("starting section page", startpage) |
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| pagecount = st.slider('How many pages', 1, 5, 1) |
| st.write("pages to read", pagecount) |
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| def extract_text_from_pdf(): |
| reader = PdfReader(pdf_file) |
| |
| text = [reader.pages[i].extract_text() for i in range(startpage, startpage + pagecount )] |
| |
| text = " ".join(text) |
| return text |
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|
| def extract_text_from_pdf2(): |
| reader = PdfReader(pdf_file) |
| |
| text = [reader.pages[i].extract_text() for i in range(len(reader.pages))] |
| |
| text = " ".join(text) |
| return text |
|
|
| if st.button("Summerize "): |
| with st.spinner("Extracting Text..."): |
| summary_chain = load_summarize_chain(llm, chain_type="map_reduce") |
| summarize_document_chain = AnalyzeDocumentChain(combine_docs_chain=summary_chain, verbose=True) |
| text = extract_text_from_pdf() |
| with st.spinner("Summarizing..."): |
| result = summarize_document_chain.run(text) |
| st.write(result) |
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|
