# Load model directly
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("12345deena/t5-small-ilct5")
model = AutoModelForSeq2SeqLM.from_pretrained("12345deena/t5-small-ilct5")

def summarize(text):
    # Tokenize input text
    inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=512, truncation=True)

    # Generate summary
    summary_ids = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)

    # Decode the summary
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary

# Create Gradio interface
iface = gr.Interface(
    fn=summarize,
    inputs="text",
    outputs="text",
    title="Abstractive Text Summarization",
    description="Enter a piece of text to summarize it."
)

# Launch the interface on port 8888
iface.launch()