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app.py

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+
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-cnn-12-6")
+model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-cnn-12-6")
+
+def summarize_text(text):
+    inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
+    summary_ids = model.generate(
+        inputs["input_ids"],
+        attention_mask=inputs["attention_mask"],
+        max_length=100,
+        min_length=20,
+        length_penalty=2.0,
+        num_beams=4,
+        early_stopping=True,
+        no_repeat_ngram_size=3
+    )
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+iface = gr.Interface(
+    fn=summarize_text,
+    inputs=gr.Textbox(lines=5, label="Input Text"),
+    outputs=gr.Textbox(label="Summary"),
+    title="DistilBART Summarizer",
+    description="Summarize any input text using DistilBART fine-tuned model."
+)
+
+iface.launch()

requirements.txt

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+
+transformers
+torch
+gradio