app.py

import torch
import gradio as gr
from transformers import PegasusTokenizer, PegasusForConditionalGeneration

# Define the PEGASUS model and tokenizer
MODEL_NAME = 'VishnuPottabatthini/PEGASUS_Large'  # Change this to the PEGASUS model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load the tokenizer and model
tokenizer = PegasusTokenizer.from_pretrained(MODEL_NAME)
model = PegasusForConditionalGeneration.from_pretrained(MODEL_NAME).to(device)

# Define the summarization function
def summarize(text, state):
    try:
        # Tokenize the input text
        inputs = tokenizer(
            text,
            return_tensors="pt",
            truncation=True,
            max_length=1024  # Adjust max length according to your model's capabilities
        ).to(device)

        # Generate the summary
        summary_ids = model.generate(
            inputs['input_ids'],
            attention_mask=inputs['attention_mask'],
            max_length=150,  # Maximum length of the summary
            min_length=30,   # Minimum length of the summary
            num_beams=4,     # Beam search to improve the quality of generated text
            early_stopping=True
        )

        # Decode the summary
        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
        return state + "\n" + summary, state + "\n" + summary

    except Exception as e:
        return str(e), state

# Create the Gradio interface
mf_summarize = gr.Interface(
    fn=summarize,
    inputs=[
        gr.Textbox(placeholder="Enter text to summarize...", lines=10),
        gr.State(value="")
    ],
    outputs=[
        gr.Textbox(lines=15, label="Summary"),
        gr.State()
    ],
    theme="huggingface",
    title="Article Summarization",
    live=True,
    description=(
        "Enter a long piece of text to generate a concise summary using a PEGASUS model. "
        "This demo uses a custom PEGASUS model from 🤗 Transformers."
    )
)

# Launch the Gradio interface
mf_summarize.launch()