app.py

import streamlit as st
from haystack.document_stores import InMemoryDocumentStore
from haystack.nodes import TransformersSummarizer, PreProcessor, PDFToTextConverter, Crawler
from haystack.schema import Document
import logging
import base64
from PIL import Image
import validators

@st.cache(hash_funcs={"builtins.SwigPyObject": lambda _: None},allow_output_mutation=True)
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(content):
    pdf_to_document_store(content)
    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.title('TL;DR with Haystack')
image = Image.open('header-image.png')
st.image(image)

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 whole 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. For best results, upload a document that has minimal intro and tables at the top. 
""", 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)