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app.py

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+import gradio as gr
+from transformers import pipeline
+
+model_name = "sshleifer/distilbart-cnn-12-6"
+
+def summarize_text(text, max_length, min_length):
+    summarizer = pipeline('summarization', model=model_name, use_auth_token=None)
+    result = summarizer(text, max_length=int(max_length), min_length=int(min_length), do_sample=False)
+    return result[0]['summary_text']
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Text Summarizer")
+    gr.Markdown("Powered by sshleifer/distilbart-cnn-12-6")
+    text_input = gr.Textbox(label="Text to Summarize", placeholder="Enter long text...", lines=5)
+    max_len = gr.Slider(minimum=10, maximum=200, value=100, label="Max Summary Length")
+    min_len = gr.Slider(minimum=5, maximum=50, value=10, label="Min Summary Length")
+    output = gr.Textbox(label="Summary", lines=3)
+    btn = gr.Button("Summarize")
+    btn.click(fn=summarize_text, inputs=[text_input, max_len, min_len], outputs=output)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+transformers
+torch