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import gradio as gr
from transformers import pipeline
from PyPDF2 import PdfReader
# Load summarization model
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
def summarize_pdf(pdf_file, summary_length):
if pdf_file is None:
return "Please upload a PDF file."
# Read PDF text
reader = PdfReader(pdf_file.name)
text = ""
for page in reader.pages:
page_text = page.extract_text()
if page_text:
text += page_text + "\n"
if not text.strip():
return "No readable text found in this PDF."
# Define summary length settings
if summary_length == "Short":
max_len, min_len = 60, 20
elif summary_length == "Medium":
max_len, min_len = 130, 40
else: # Long
max_len, min_len = 200, 60
# Split text into manageable chunks
max_chunk = 1000
chunks = [text[i:i + max_chunk] for i in range(0, len(text), max_chunk)]
summaries = []
for chunk in chunks:
summary = summarizer(
chunk,
max_length=max_len,
min_length=min_len,
do_sample=False
)[0]["summary_text"]
summaries.append(summary)
final_summary = " ".join(summaries)
return final_summary
# Gradio Interface
iface = gr.Interface(
fn=summarize_pdf,
inputs=[
gr.File(label="Upload your PDF"),
gr.Radio(
["Short", "Medium", "Long"],
label="Select Summary Length",
value="Medium"
)
],
outputs=gr.Textbox(label="Generated Summary", lines=10),
title="📘 PDF Summarizer",
description="Upload a PDF and choose summary length (Short / Medium / Long). Powered by Hugging Face transformers."
)
iface.launch()
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