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