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import gradio as gr
import PyPDF2
import tempfile
from transformers import pipeline
# Step 1: Summarizer class using HuggingFace directly
class TextSummarizer:
def __init__(self):
self.summarizer = pipeline(
"summarization",
model="facebook/bart-large-cnn"
)
def summarize_text(self, article_text, max_length=150, min_length=30):
# Truncate very long inputs
article_text = article_text.strip()
if len(article_text) > 1024:
article_text = article_text[:1024]
summary = self.summarizer(
article_text,
max_length=max_length,
min_length=min_length,
do_sample=False
)
return summary[0]['summary_text'] if summary else "No summary generated."
# Step 2: PDF text extraction
def pdf_to_text(pdf_file):
try:
with tempfile.NamedTemporaryFile(delete=False) as tmp:
tmp.write(pdf_file)
tmp.flush()
reader = PyPDF2.PdfReader(tmp.name)
text = "\n".join(page.extract_text() or "" for page in reader.pages)
return text.strip() if text.strip() else "No extractable text found in the PDF."
except Exception as e:
return f"Error reading PDF: {str(e)}"
# Step 3: Summarization function for Gradio
summarizer = TextSummarizer()
def summarize_input(text, max_words):
if not text.strip():
return "Please enter or extract some text first."
try:
max_length = int(max_words)
min_length = max(30, max_length // 4)
return summarizer.summarize_text(text, max_length=max_length, min_length=min_length)
except Exception as e:
return f"Error during summarization: {str(e)}"
# Step 4: Gradio UI setup
with gr.Blocks() as demo:
gr.Markdown("## 📝 Text & PDF Summarizer")
with gr.Row():
text_input = gr.Textbox(label="Enter text to summarize", lines=15, placeholder="Paste your text here...")
pdf_file = gr.File(label="Or upload a PDF", file_types=[".pdf"], type="binary")
max_words = gr.Number(label="Max summary word count", value=150, precision=0)
with gr.Row():
convert_btn = gr.Button("Convert PDF to Text")
summarize_btn = gr.Button("Summarize Text")
output_text = gr.Textbox(label="Summary", lines=10)
convert_btn.click(fn=pdf_to_text, inputs=pdf_file, outputs=text_input)
summarize_btn.click(fn=summarize_input, inputs=[text_input, max_words], outputs=output_text)
# Step 5: Launch the app
if __name__ == "__main__":
demo.launch()
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