app.py

import os
import gradio as gr
from transformers import BartTokenizer, BartForConditionalGeneration

hf_token = os.getenv("HF_TOKEN")  # optional unless model is private/gated

# Load model and tokenizer from Hugging Face hub using the provided model name
model_name = "iimran/SAM-TheSummariserV2"

# FIX: use `token=` instead of `use_auth_token=`
tokenizer = BartTokenizer.from_pretrained(model_name, token=hf_token)
model = BartForConditionalGeneration.from_pretrained(model_name, token=hf_token)

def summarize(input_text):
    # Tokenize the input text with truncation
    inputs = tokenizer(input_text, max_length=1024, truncation=True, return_tensors="pt")

    # Generate the summary using beam search
    summary_ids = model.generate(
        inputs["input_ids"],
        num_beams=4,        # Use beam search with 4 beams for quality summaries
        max_length=128,     # Set maximum length for the generated summary
        early_stopping=True # Enable early stopping if all beams finish
    )

    # Decode the generated summary tokens to a string
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary

# Create a Gradio interface
iface = gr.Interface(
    fn=summarize,
    inputs=gr.Textbox(
        label="Enter Text to Summarize",
        lines=10,
        placeholder="Paste or type the text you want to summarize here..."
    ),
    outputs=gr.Textbox(
        label="Summary",
        lines=5,
        placeholder="Summary will appear here..."
    ),
    title="SAM - The Summariser",
    description="SAM is a model that summarizes large texts into concise summaries."
)

# Launch the Gradio interface
iface.launch()