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| # import streamlit as st | |
| # from transformers import pipeline | |
| # from PyPDF2 import PdfReader | |
| # # Initialize the summarizer | |
| # summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
| # def extract_text_from_pdf(pdf_file): | |
| # """Extract text from an uploaded PDF file.""" | |
| # try: | |
| # reader = PdfReader(pdf_file) | |
| # text = "" | |
| # for page in reader.pages: | |
| # page_text = page.extract_text() | |
| # if page_text: # Skip pages with no text | |
| # text += page_text + "\n" | |
| # return text | |
| # except Exception as e: | |
| # raise ValueError(f"Error extracting text from PDF: {e}") | |
| # def split_text_into_chunks(text, max_chunk_size=1024): | |
| # """Split the text into smaller chunks for summarization.""" | |
| # chunks = [] | |
| # while len(text) > max_chunk_size: | |
| # split_point = text.rfind(". ", 0, max_chunk_size) + 1 # Split at the last sentence boundary | |
| # if split_point == 0: # No sentence boundary found, split arbitrarily | |
| # split_point = max_chunk_size | |
| # chunks.append | |
| # # Streamlit Dashboard | |
| # st.title("PDF Summarizer") | |
| # st.write("Upload a PDF file to get a summarized version of its content.") | |
| # uploaded_file = st.file_uploader("Upload your PDF", type=["pdf"]) | |
| # if uploaded_file is not None: | |
| # # Extract text from the PDF | |
| # st.write("Processing your PDF...") | |
| # try: | |
| # pdf_text = extract_text_from_pdf(uploaded_file) | |
| # st.write("PDF content extracted successfully.") | |
| # # Display extracted text (optional) | |
| # with st.expander("View Extracted Text"): | |
| # st.text_area("Extracted Text", pdf_text, height=300) | |
| # # Summarize the extracted text | |
| # if st.button("Summarize"): | |
| # st.write("Generating summary...") | |
| # summary = summarizer(pdf_text, max_length=130, min_length=30, do_sample=False) | |
| # st.subheader("Summary") | |
| # st.write(summary[0]["summary_text"]) | |
| # except Exception as e: | |
| # st.error(f"An error occurred while processing the PDF: {str(e)}") | |
| import streamlit as st | |
| from transformers import pipeline | |
| import pdfplumber | |
| # Initialize the summarizer | |
| summarizer = pipeline("summarization", model="t5-small") | |
| def extract_text_from_pdf(pdf_file): | |
| """Extract text from an uploaded PDF file using pdfplumber.""" | |
| try: | |
| text = "" | |
| with pdfplumber.open(pdf_file) as pdf: | |
| for page in pdf.pages: | |
| text += page.extract_text() + "\n" | |
| if not text.strip(): | |
| raise ValueError("No extractable text found in the PDF.") | |
| return text | |
| except Exception as e: | |
| raise ValueError(f"Error extracting text from PDF: {e}") | |
| def split_text_into_chunks(text, max_chunk_size=1024): | |
| """Split the text into smaller chunks for summarization.""" | |
| chunks = [] | |
| while len(text) > max_chunk_size: | |
| split_point = text.rfind(". ", 0, max_chunk_size) + 1 # Find the last full sentence | |
| if split_point == 0: # No sentence boundary found, split arbitrarily | |
| split_point = max_chunk_size | |
| chunks.append(text[:split_point]) | |
| text = text[split_point:] | |
| if text: | |
| chunks.append(text) | |
| return chunks | |
| def summarize_text(chunks): | |
| """Summarize each chunk of text with dynamic max_length.""" | |
| summaries = [] | |
| for chunk in chunks: | |
| input_length = len(chunk.split()) # Approximate token count | |
| max_length = max(48, int(input_length * 0.8)) # Set max_length to 80% of input length | |
| summary = summarizer(chunk, max_length=max_length, min_length=10, do_sample=False) | |
| summaries.append(summary[0]["summary_text"]) | |
| return summaries | |
| # Streamlit Dashboard | |
| st.title("PDF Summarizer") | |
| st.write("Upload a PDF file to get a summarized version of its content.") | |
| uploaded_file = st.file_uploader("Upload your PDF", type=["pdf"]) | |
| if uploaded_file is not None: | |
| try: | |
| # Extract text from the PDF | |
| st.write("Processing your PDF...") | |
| pdf_text = extract_text_from_pdf(uploaded_file) | |
| st.write("PDF content extracted successfully.") | |
| # Display extracted text (optional) | |
| with st.expander("View Extracted Text"): | |
| st.text_area("Extracted Text", pdf_text, height=300) | |
| # Summarize the extracted text | |
| if st.button("Summarize"): | |
| st.write("Generating summary...") | |
| chunks = split_text_into_chunks(pdf_text) | |
| summaries = summarize_text(chunks) | |
| full_summary = " ".join(summaries) | |
| st.subheader("Summary") | |
| st.write(full_summary) | |
| except Exception as e: | |
| st.error(f"An error occurred while processing the PDF: {str(e)}") | |