from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import gradio as gr

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("google/pegasus-xsum")
model = AutoModelForSeq2SeqLM.from_pretrained("google/pegasus-xsum")

def summarize(text):
    # Tokenize input text
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024)
    
    # Generate summary
    summary_ids = model.generate(inputs["input_ids"])
    
    # Decode and return the summary
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary

# Create Gradio interface
demo = gr.Interface(
    fn=summarize,
    inputs=gr.Textbox(lines=10, placeholder="Enter text to summarize...", label="Input Text"),
    outputs=gr.Textbox(lines=5, label="Summary"),
    title="Pegasus-XSum Text Summarizer",
    description="Enter text and get an abstractive summary using Google's Pegasus-XSum model."
)

# Launch the app
demo.launch()