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1e7992c 160ed61 1e7992c 160ed61 1e7992c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | import gradio as gr
import PyPDF2
from transformers import pipeline
import re
# Load high-accuracy summarization model
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
def extract_text_from_pdf(pdf_file):
"""Extract text from uploaded PDF file"""
try:
pdf_reader = PyPDF2.PdfReader(pdf_file)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
return text
except Exception as e:
return f"Error reading PDF: {str(e)}"
def extract_key_elements(text, summary):
"""Extract key elements from text and summary"""
summary_sentences = re.split(r'[.!?]+', summary.strip())
key_elements = {
"Main Summary": summary,
"Total Words Original": len(text.split()),
"Total Words Summary": len(summary.split()),
"Compression Ratio": f"{(len(summary.split()) / len(text.split()) * 100):.1f}%",
"Key Points": "\n".join([s.strip() for s in summary_sentences if s.strip()][:5])
}
return key_elements
def summarize_text(text_input, max_length=150, min_length=50):
"""Summarize text with key elements"""
if not text_input or len(text_input.strip()) < 50:
return "Error: Please provide text with at least 50 characters", {}
try:
# Split long text into chunks
words = text_input.split()
chunk_size = 1024
chunks = [' '.join(words[i:i+chunk_size]) for i in range(0, len(words), chunk_size)]
summaries = []
for chunk in chunks:
if len(chunk.split()) > 30:
result = summarizer(chunk, max_length=max_length, min_length=min_length, do_sample=False)
summaries.append(result[0]['summary_text'])
final_summary = " ".join(summaries)
key_elements = extract_key_elements(text_input, final_summary)
return final_summary, key_elements
except Exception as e:
return f"Error: {str(e)}", {}
# Gradio Interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# 📄 PDF & Text Summarizer")
gr.Markdown("Upload PDF or paste text to generate summaries with key elements using BART model.")
with gr.Tabs():
# Text Tab
with gr.Tab("Text Summarizer"):
with gr.Row():
text_input = gr.Textbox(label="Enter Text", placeholder="Paste text here...", lines=8)
with gr.Row():
max_len = gr.Slider(50, 300, value=150, step=10, label="Max Length")
min_len = gr.Slider(20, 100, value=50, step=10, label="Min Length")
summarize_btn = gr.Button("🚀 Summarize", variant="primary")
summary_output = gr.Textbox(label="Summary", lines=6)
key_elements_output = gr.JSON(label="Key Elements")
summarize_btn.click(
fn=summarize_text,
inputs=[text_input, max_len, min_len],
outputs=[summary_output, key_elements_output]
)
# PDF Tab
with gr.Tab("PDF Summarizer"):
pdf_input = gr.File(label="Upload PDF", file_count="single", file_types=[".pdf"])
with gr.Row():
pdf_max_len = gr.Slider(50, 300, value=150, step=10, label="Max Length")
pdf_min_len = gr.Slider(20, 100, value=50, step=10, label="Min Length")
pdf_summarize_btn = gr.Button("📤 Process PDF", variant="primary")
pdf_summary_output = gr.Textbox(label="Summary", lines=6)
pdf_key_elements_output = gr.JSON(label="Key Elements")
pdf_summarize_btn.click(
fn=lambda pdf, max_l, min_l: summarize_text(extract_text_from_pdf(pdf), max_l, min_l),
inputs=[pdf_input, pdf_max_len, pdf_min_len],
outputs=[pdf_summary_output, pdf_key_elements_output]
)
gr.Markdown("---")
gr.Markdown("✨ Features: PDF & Text support | BART-large-cnn model | Key element extraction")
if __name__ == "__main__":
demo.launch() |