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import os |
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import streamlit as st |
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from langchain_openai import ChatOpenAI |
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from langchain.chains import load_summarize_chain |
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from langchain_community.document_loaders import PyPDFLoader, TextLoader, UnstructuredWordDocumentLoader |
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from langchain.prompts import PromptTemplate |
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from modules import app_page_definitions, app_logger,app_constants,file_utils |
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app_logger = app_logger.app_logger |
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batch_size = app_constants.SUMMARIZER_BATCH |
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WORKSPACE_DIRECTORY = app_constants.WORKSPACE_DIRECTORY |
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def process_file(file_path, file_type): |
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if file_type == "text/plain": |
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loader = TextLoader(file_path) |
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elif file_type == "application/pdf": |
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loader = PyPDFLoader(file_path) |
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elif file_type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document": |
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loader = UnstructuredWordDocumentLoader(file_path) |
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else: |
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raise ValueError(f"Unsupported file type: {file_type}") |
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app_logger.info(f"Processing file {file_path} of type {file_type}") |
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return loader.load_and_split() |
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def app(): |
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app_logger.info("Starting Streamlit app - Summarizer Tool page") |
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page_config = app_page_definitions.PAGE_CONFIG.get("nav_summarize") |
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st.title(page_config["title"]) |
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st.caption(page_config["caption"]) |
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st.session_state.current_page = "nav_summarize" |
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uploaded_file = st.file_uploader("Upload your document here:", type=['txt', 'pdf', 'docx'], key="file_uploader") |
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if uploaded_file is not None: |
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file_path = file_utils.save_uploaded_file(uploaded_file,uploads_path=WORKSPACE_DIRECTORY + "/tmp") |
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docs = process_file(file_path, uploaded_file.type) |
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total_docs = len(docs) |
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app_logger.info(f"Total documents processed: {total_docs}") |
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if total_docs > 1: |
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doc_range = st.slider("Select document range for summarization", 1, total_docs, (1, total_docs)) |
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else: |
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doc_range = (1, 1) |
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progress_bar = st.progress(0) |
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if st.button("Summarize"): |
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with st.spinner('Processing... Please wait'): |
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llm = ChatOpenAI( |
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model_name=app_constants.MODEL_NAME, |
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openai_api_key=app_constants.openai_api_key, |
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base_url=app_constants.local_model_uri, |
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streaming=True |
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) |
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prompt_template = """Write a concise summary of the following: |
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{text} |
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CONCISE SUMMARY:""" |
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prompt = PromptTemplate.from_template(prompt_template) |
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refine_template = ( |
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"You are a content writer and your job is to produce a summary of input\n" |
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"We have provided an existing summary up to a certain point: {existing_answer}\n" |
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"Start and end properly and refine the existing summary " |
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"with some more context below.\n" |
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"------------\n" |
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"{text}\n" |
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"------------\n" |
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"Given the new context, refine the original summary. " |
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"If the context isn't useful, return the original summary." |
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) |
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refine_prompt = PromptTemplate.from_template(refine_template) |
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chain = load_summarize_chain( |
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llm=llm, |
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chain_type="refine", |
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question_prompt=prompt, |
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refine_prompt=refine_prompt, |
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return_intermediate_steps=True, |
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input_key="input_documents", |
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output_key="output_text", |
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) |
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start_doc, end_doc = doc_range |
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for i in range(start_doc - 1, min(end_doc, total_docs), batch_size): |
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batch_docs = docs[i:min(i + batch_size, total_docs)] |
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progress_value = (i + len(batch_docs)) / total_docs |
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progress_bar.progress(progress_value) |
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with st.expander(f"Processing Documents {i + 1} - {i + len(batch_docs)}", expanded=False): |
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intermediate_summary = chain.invoke({"input_documents": batch_docs}, return_only_outputs=True) |
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st.write(intermediate_summary) |
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selected_docs = docs[start_doc - 1:end_doc] |
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final_summary_response = chain.invoke({"input_documents": selected_docs}, return_only_outputs=True) |
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final_summary = final_summary_response['output_text'] if 'output_text' in final_summary_response else "No summary generated." |
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st.text_area("Final Summary", final_summary, height=300) |
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st.success("Summarization Completed!") |
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progress_bar.empty() |
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else: |
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st.warning("Please upload a document to summarize.") |
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app_logger.warning("No document uploaded for summarization") |
