app.py

import gradio as gr
from transformers import pipeline, logging

# Disable unnecessary warnings from transformers library
logging.set_verbosity_error()

# Load the summarization model
try:
    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
    print("Model loaded successfully!")
except Exception as e:
    print(f"Error loading model: {e}")
    summarizer = None

# Function for summarizing text
def summarize_text(input_text):
    if summarizer:
        # Generate the summary from the input text
        summary = summarizer(input_text, max_length=150, min_length=30, do_sample=False)
        return summary[0]['summary_text']
    else:
        return "Error: Model not loaded."

# Define the Gradio interface
def create_interface():
    # Create a Gradio interface with text input and output
    interface = gr.Interface(
        fn=summarize_text,  # Function to summarize text
        inputs=gr.Textbox(label="Enter Text for Summarization", placeholder="Paste or type your text here..."),
        outputs=gr.Textbox(label="Summary", placeholder="Summary will appear here..."),
        title="Text Summarizer",
        description="This app takes a long text as input and generates a concise summary using a pre-trained BART model.",
        examples=[["Hugging Face is an open-source platform that allows developers and researchers to share and access machine learning models."]]
    )
    return interface

# Launch the Gradio app
if __name__ == "__main__":
    interface = create_interface()
    interface.launch(share=True)  # share=True allows the app to be accessed via a public URL