app.py

import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline

# Load the model and tokenizer
model_name = 'IMISLab/GreekT5-umt5-small-greeksum'
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Set up the summarizer pipeline
summarizer = pipeline(
    'summarization',
    device = 'cpu',
    model = model,
    tokenizer = tokenizer,
    max_new_tokens = 128,
    truncation = True
)

# Define the summarization function
def generate_summary(text):
    output = summarizer('summarize: ' + text)
    return output[0]['summary_text']

# Create Gradio interface
iface = gr.Interface(
    fn=generate_summary,  # The function that Gradio will use
    inputs=gr.Textbox(label="Input Text", lines=5, placeholder="Enter the text to summarize..."),
    outputs=gr.Textbox(label="Summary"),
    live=True
)

# Launch the Gradio interface
iface.launch()