app.py

import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, GenerationConfig

# Define the URL of your model on Hugging Face Spaces
model_url = 'https://huggingface.co/your-username/your-model-name/resolve/main/'

# Load the model and tokenizer directly from the URL
try:
    tokenizer = AutoTokenizer.from_pretrained(model_url)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_url)
except Exception as e:
    st.error(f"Failed to load model: {e}")

# Streamlit UI
st.title("Text Summarizer")
text = st.text_area("Enter the text to generate its Summary:")

# Configuration for generation
generation_config = GenerationConfig(max_new_tokens=100, do_sample=True, temperature=0.7)

if text:
    try:
        # Encode input
        inputs_encoded = tokenizer(text, return_tensors='pt')

        # Generate output
        with torch.no_grad():
            model_output = model.generate(inputs_encoded["input_ids"], generation_config=generation_config)[0]

        # Decode output
        output = tokenizer.decode(model_output, skip_special_tokens=True)

        # Display results in a box with a title
        with st.expander("Output", expanded=True):
            st.write(output)

    except Exception as e:
        st.error(f"An error occurred during summarization: {e}")