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.",
)