| import gradio as gr | |
| from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM | |
| model_name = "t5-base" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| summarization = pipeline("summarization", model=model, tokenizer=tokenizer) | |
| def generate_summary(text): | |
| summary = summarization(text, max_length=100, min_length=25, do_sample=False) | |
| return summary[0]["summary_text"] | |
| input_text = gr.inputs.Textbox(lines=5, placeholder="Enter your text here...") | |
| output_text = gr.outputs.Textbox(label="Summary") | |
| iface = gr.Interface( | |
| fn=generate_summary, | |
| inputs=input_text, | |
| outputs=output_text, | |
| title="Text Summarization", | |
| description="Enter your text and get a summary using the Hugging Face T5 model.", | |
| ) | |
| iface.launch() | |