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| import streamlit as st | |
| from haystack.document_stores import InMemoryDocumentStore | |
| from haystack.nodes import TransformersSummarizer, PreProcessor, PDFToTextConverter | |
| from haystack.schema import Document | |
| import logging | |
| import base64 | |
| def start_haystack(): | |
| document_store = InMemoryDocumentStore() | |
| preprocessor = PreProcessor( | |
| clean_empty_lines=True, | |
| clean_whitespace=True, | |
| clean_header_footer=True, | |
| split_by="word", | |
| split_length=200, | |
| split_respect_sentence_boundary=True, | |
| ) | |
| summarizer = TransformersSummarizer(model_name_or_path="facebook/bart-large-cnn") | |
| return document_store, summarizer, preprocessor | |
| def pdf_to_document_store(pdf_file): | |
| document_store.delete_documents() | |
| converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["en"]) | |
| with open("temp-path.pdf", 'wb') as temp_file: | |
| base64_pdf = base64.b64encode(pdf_file.read()).decode('utf-8') | |
| temp_file.write(base64.b64decode(base64_pdf)) | |
| doc = converter.convert(file_path="temp-path.pdf", meta=None) | |
| preprocessed_docs=preprocessor.process(doc) | |
| document_store.write_documents(preprocessed_docs) | |
| temp_file.close() | |
| def summarize(file): | |
| pdf_to_document_store(file) | |
| summaries = summarizer.predict(documents=document_store.get_all_documents(), generate_single_summary=True) | |
| return summaries | |
| def set_state_if_absent(key, value): | |
| if key not in st.session_state: | |
| st.session_state[key] = value | |
| set_state_if_absent("summaries", None) | |
| document_store, summarizer, preprocessor = start_haystack() | |
| st.markdown( """ | |
| This Summarization demo uses a [Haystack TransformerSummarizer node](https://haystack.deepset.ai/pipeline_nodes/summarizer). You can upload a PDF file, which will be converted to text with the [Haystack PDFtoTextConverter](https://haystack.deepset.ai/reference/file-converters#pdftotextconverter). In this demo, we produce 1 summary for the whole file you upload. So, the TransformerSummarizer treats the while thing as one string, which means along with the model limitations, PDFs that have a lot of unneeded text at the beginning produce poor results. | |
| """, unsafe_allow_html=True) | |
| uploaded_file = st.file_uploader("Choose a PDF file", accept_multiple_files=False) | |
| if uploaded_file is not None: | |
| if st.button('Summarize Document'): | |
| with st.spinner("📚 Please wait while we produce a summary..."): | |
| try: | |
| st. session_state.summaries = summarize(uploaded_file) | |
| except Exception as e: | |
| logging.exception(e) | |
| if st.session_state.summaries: | |
| st.write('## Summary') | |
| for count, summary in enumerate(st.session_state.summaries): | |
| st.write(summary.content) | |