app.py

import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration

# Load model and tokenizer
model_name = "t5-small"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

# Function to summarize text
def summarize_text(text, max_length=100):
    input_text = "summarize: " + text
    inputs = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
    
    summary_ids = model.generate(inputs, max_length=max_length, min_length=30, length_penalty=2.0, num_beams=4)
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)

    return summary

# Gradio UI
iface = gr.Interface(
    fn=summarize_text,
    inputs=[gr.Textbox(label="Enter Text to Summarize"), gr.Slider(50, 200, step=10, label="Max Length")],
    outputs="text",
    title="Text Summarization App",
    description="This app summarizes long texts using the T5 Transformer model.",
)

# Launch the app
if __name__ == "__main__":
    iface.launch()