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import gradio as gr
from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
# Load model and tokenizer
model_name = "t5-base"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define pipeline for text summarization
summarizer = pipeline('text2text-generation', model=model, tokenizer=tokenizer)
# Define Gradio interface
def summarize_text(text):
result = summarizer(text, max_length=100, min_length=30, do_sample=False)[0]
summary = result['generated_text'].strip()
return summary
iface = gr.Interface(fn=summarize_text, inputs="text", outputs="text",
title="Text Summarization with Hugging Face and Gradio",
description="Enter text to summarize.")
# Launch Gradio interface
iface.launch()
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