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| import gradio as gr | |
| from transformers import pipeline | |
| from newspaper import Article | |
| import fitz # PyMuPDF | |
| from summarizer import Summarizer | |
| # --------- UTILITY FUNCTIONS --------- | |
| def extract_text_from_pdf(pdf_file): | |
| doc = fitz.open(stream=pdf_file, filetype="pdf") | |
| text = "" | |
| for page in doc: | |
| text += page.get_text() | |
| return text | |
| def extract_text_from_url(url): | |
| article = Article(url) | |
| article.download() | |
| article.parse() | |
| return article.text | |
| abstractive_summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
| extractive_summarizer = Summarizer() | |
| def generate_abstractive_summary(text, max_length=130, min_length=30): | |
| summary = abstractive_summarizer(text, max_length=max_length, min_length=min_length, do_sample=False) | |
| return summary[0]['summary_text'] | |
| def generate_extractive_summary(text, ratio=0.3): | |
| return extractive_summarizer(text, ratio=ratio) | |
| def summarize_text(source_type, text, pdf, url, max_length, min_length, ratio): | |
| input_text = "" | |
| try: | |
| if source_type == "Text" and text: | |
| input_text = text | |
| elif source_type == "PDF" and pdf is not None: | |
| input_text = extract_text_from_pdf(pdf) | |
| elif source_type == "URL" and url: | |
| input_text = extract_text_from_url(url) | |
| else: | |
| return "❗Please provide a valid input.", "" | |
| if len(input_text.strip()) == 0: | |
| return "❗Input is empty after extraction.", "" | |
| # Bart/T5 models handle ~1024 tokens (~2000 characters) | |
| input_text = input_text[:2000] | |
| abstractive = generate_abstractive_summary(input_text, max_length, min_length) | |
| extractive = generate_extractive_summary(input_text, ratio) | |
| return abstractive, extractive | |
| except Exception as e: | |
| return f"⚠️ Error: {str(e)}", "" | |
| # --------- GRADIO UI --------- | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## 🧠 AI Text Summarizer\nChoose input type and get both **abstractive** and **extractive** summaries.") | |
| source_type = gr.Radio(["Text", "PDF", "URL"], label="Select Input Source") | |
| text_input = gr.Textbox(lines=8, label="Enter Text", visible=False) | |
| pdf_input = gr.File(label="Upload PDF", type="binary", visible=False) | |
| url_input = gr.Textbox(label="Enter URL", visible=False) | |
| max_length = gr.Slider(50, 300, step=10, value=130, label="Max Length (Abstractive)") | |
| min_length = gr.Slider(20, 100, step=10, value=30, label="Min Length (Abstractive)") | |
| ratio = gr.Slider(0.1, 1.0, step=0.1, value=0.3, label="Summary Ratio (Extractive)") | |
| btn = gr.Button("Generate Summaries") | |
| output_ab = gr.Textbox(label="Abstractive Summary") | |
| output_ex = gr.Textbox(label="Extractive Summary") | |
| def toggle_inputs(src): | |
| return { | |
| text_input: gr.update(visible=(src == "Text")), | |
| pdf_input: gr.update(visible=(src == "PDF")), | |
| url_input: gr.update(visible=(src == "URL")) | |
| } | |
| source_type.change(fn=toggle_inputs, inputs=source_type, outputs=[text_input, pdf_input, url_input]) | |
| btn.click( | |
| summarize_text, | |
| inputs=[source_type, text_input, pdf_input, url_input, max_length, min_length, ratio], | |
| outputs=[output_ab, output_ex] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |