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Text-Summarization
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Saket Chaudhari commited on
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e118626
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Create app.py

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  1. app.py +106 -0
app.py ADDED
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+ import gradio as gr
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+ from transformers import pipeline
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+ import torch
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+ from fpdf import FPDF
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+ import pandas as pd
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+ import json
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+ import csv
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+
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+ # Load the summarization pipeline
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+ text_summary = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", torch_dtype=torch.float32)
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+
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+ def chunk_text(input_text, max_chunk_size=1024):
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+ """
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+ Splits the input text into smaller chunks of size `max_chunk_size` or smaller.
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+ """
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+ words = input_text.split()
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+ chunks = []
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+ current_chunk = []
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+
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+ for word in words:
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+ if len(" ".join(current_chunk + [word])) <= max_chunk_size:
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+ current_chunk.append(word)
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+ else:
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+ chunks.append(" ".join(current_chunk))
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+ current_chunk = [word]
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+
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+ if current_chunk:
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+ chunks.append(" ".join(current_chunk))
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+
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+ return chunks
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+
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+ def summary(input_text, max_length=130, min_length=30, output_format="Plain Text"):
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+ """
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+ Summarizes the input text, handling cases where the text exceeds the model's maximum sequence length.
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+ Supports different output formats (Plain Text, JSON, HTML, CSV, Markdown, PDF, Excel).
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+ """
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+ chunks = chunk_text(input_text)
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+ summarized_chunks = []
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+
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+ for chunk in chunks:
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+ output = text_summary(chunk, max_length=max_length, min_length=min_length)
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+ summarized_chunks.append(output[0]['summary_text'])
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+
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+ summary_text = " ".join(summarized_chunks)
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+
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+ # Return the output in the selected format
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+ if output_format == "Plain Text":
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+ return summary_text
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+
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+ elif output_format == "JSON":
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+ result = {
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+ "summary": summary_text,
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+ "chunk_count": len(chunks),
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+ "original_length": len(input_text.split()),
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+ "summary_length": len(summary_text.split())
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+ }
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+ return json.dumps(result, indent=4)
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+
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+ elif output_format == "HTML":
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+ html_output = f"<html><body><h2>Summary</h2><p>{summary_text}</p></body></html>"
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+ return html_output
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+
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+ elif output_format == "CSV":
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+ csv_output = "Original Text, Summary\n"
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+ for chunk, summary in zip(chunks, summarized_chunks):
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+ csv_output += f'"{chunk}", "{summary}"\n'
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+ return csv_output
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+
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+ elif output_format == "Markdown":
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+ markdown_output = f"## Summary\n\n{summary_text}"
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+ return markdown_output
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+
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+ elif output_format == "PDF":
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+ pdf = FPDF()
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+ pdf.set_auto_page_break(auto=True, margin=15)
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+ pdf.add_page()
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+ pdf.set_font("Arial", size=12)
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+ pdf.multi_cell(0, 10, summary_text)
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+ pdf_output = "summary.pdf"
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+ pdf.output(pdf_output)
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+ return f"PDF generated: {pdf_output}"
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+
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+ elif output_format == "Excel":
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+ data = {
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+ "Original Text": chunks,
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+ "Summary": summarized_chunks
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+ }
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+ df = pd.DataFrame(data)
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+ excel_output = "summary.xlsx"
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+ df.to_excel(excel_output, index=False)
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+ return f"Excel file generated: {excel_output}"
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+
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+ # Create a Gradio interface with an additional output format selection
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+ iface = gr.Interface(
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+ fn=summary,
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+ inputs=[
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+ gr.Textbox(label="Input Text", lines=10),
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+ gr.Slider(label="Max Length", minimum=30, maximum=300, step=10, value=130),
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+ gr.Slider(label="Min Length", minimum=20, maximum=100, step=10, value=30),
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+ gr.Dropdown(label="Output Format", choices=["Plain Text", "JSON", "HTML", "CSV", "Markdown", "PDF", "Excel"], value="Plain Text")
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+ ],
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+ outputs=gr.Textbox(label="Summarized Output"),
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+ title="Text Summarization with Advanced Output Formats"
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+ )
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+
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+ iface.launch()