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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| # Load the model and tokenizer | |
| model_name = "t5-small" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| def summarize_text(text): | |
| # Prepend "summarize:" as required by T5 | |
| input_text = "summarize: " + text.strip() | |
| inputs = tokenizer.encode( | |
| input_text, | |
| return_tensors="pt", | |
| max_length=512, | |
| truncation=True | |
| ) | |
| # Generate summary | |
| summary_ids = model.generate( | |
| inputs, | |
| max_length=150, | |
| min_length=30, | |
| length_penalty=2.0, | |
| num_beams=4, | |
| early_stopping=True | |
| ) | |
| summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| return summary | |
| # Create the Gradio UI | |
| iface = gr.Interface( | |
| fn=summarize_text, | |
| inputs=gr.Textbox(lines=10, placeholder="Enter your note here..."), | |
| outputs="text", | |
| title=None, | |
| description=None, | |
| flagging_mode="never" # ✅ Updated from allow_flagging | |
| ) | |
| if __name__ == "__main__": # ✅ Fixed underscores | |
| iface.launch() | |