PDF-Summarizer / app.py
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import streamlit as st
from haystack.document_stores import InMemoryDocumentStore
from haystack.nodes import TransformersSummarizer, PreProcessor, PDFToTextConverter, TfidfRetriever
from haystack.schema import Document
import logging
import base64
@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="google/pegasus-newsroom")
return document_store, summarizer, preprocessor
def pdf_to_document_store(pdf_files):
converter = PDFToTextConverter(remove_numeric_tables=True, valid_languages=["en"])
documents = []
for pdf in pdf_files:
with open("temp-path.pdf", 'wb') as temp_file:
base64_pdf = base64.b64encode(pdf.read()).decode('utf-8')
temp_file.write(base64.b64decode(base64_pdf))
doc = converter.convert(file_path="temp-path.pdf", meta=None)[0]
preprocessed_doc=preprocessor.process([doc])
documents.append(preprocessed_doc)
temp_file.close()
document_store.write_documents(documents)
st.write('Document count: ', document_store.get_document_count())
def summarize(files):
pdf_to_document_store(files)
summary = summarizer.predict(documents=document_store.get_all_documents(), generate_single_summary=False)
st.write(summary)
document_store, summarizer, preprocessor = start_haystack()
uploaded_files = st.file_uploader("Choose PDF files", accept_multiple_files=True)
if uploaded_files is not None:
st.write(len(uploaded_files))
if st.button('Summarize Documents'):
summarize(uploaded_files)
if st.button('Calculate num of docs'):
st.write(document_store.get_document_count())
if st.button('Clear DocumentStore'):
document_store.delete_all_documents()