app.py

import os
import streamlit as st
from transformers import pipeline
import traceback

# Use Hugging Face Spaces' recommended persistent storage
CACHE_DIR = os.path.join(os.getcwd(), "model_cache")

def ensure_cache_dir():
    """
    Ensure the cache directory exists.
    
    Returns:
        str: Path to the cache directory
    """
    os.makedirs(CACHE_DIR, exist_ok=True)
    return CACHE_DIR

def load_pipelines():
    """
    Load summarization pipelines with persistent caching.
    
    Returns:
        dict: Dictionary of model pipelines
    """
    try:
        # Ensure cache directory exists
        cache_dir = ensure_cache_dir()
        
        # Define model paths within the cache directory
        bart_cache = os.path.join(cache_dir, "bart-large-cnn")
        t5_cache = os.path.join(cache_dir, "t5-large")
        pegasus_cache = os.path.join(cache_dir, "pegasus-cnn_dailymail")
        
        # Load pipelines with explicit cache directories
        bart_pipeline = pipeline(
            "summarization", 
            model="facebook/bart-large-cnn", 
            cache_dir=bart_cache
        )
        t5_pipeline = pipeline(
            "summarization", 
            model="t5-large", 
            cache_dir=t5_cache
        )
        pegasus_pipeline = pipeline(
            "summarization", 
            model="google/pegasus-cnn_dailymail", 
            cache_dir=pegasus_cache
        )
        
        return {
            'BART': bart_pipeline, 
            'T5': t5_pipeline, 
            'Pegasus': pegasus_pipeline
        }
    except Exception as e:
        st.error(f"Error loading models: {str(e)}")
        st.error(traceback.format_exc())
        return {}

def generate_summary(pipeline, text, model_name):
    """
    Generate summary for a specific model with error handling.
    
    Args:
        pipeline: Hugging Face summarization pipeline
        text (str): Input text to summarize
        model_name (str): Name of the model
    
    Returns:
        str: Generated summary or error message
    """
    try:
        prompt = "Summarize the below paragraph"
        summary = pipeline(f"{prompt}\n{text}", 
                           max_length=150, 
                           min_length=50, 
                           length_penalty=2.0, 
                           num_beams=4, 
                           early_stopping=True)[0]['summary_text']
        return summary
    except Exception as e:
        error_msg = f"Error in {model_name} summarization: {str(e)}"
        st.error(error_msg)
        return error_msg

def main():
    st.title("Text Summarization with Pre-trained Models")
    
    # Display cache directory info (optional)
    st.info(f"Models will be cached in: {CACHE_DIR}")
    
    # Text input
    text_input = st.text_area("Enter text to summarize:")
    
    # Generate button
    if st.button("Generate Summary"):
        if not text_input:
            st.error("Please enter text to summarize.")
            return
        
        # Load pipelines
        pipelines = load_pipelines()
        if not pipelines:
            st.error("Failed to load models. Please check your internet connection or try again later.")
            return
        
        # Create columns for progressive display
        bart_col, t5_col, pegasus_col = st.columns(3)
        
        # BART Summary
        with bart_col:
            with st.spinner('Generating BART Summary...'):
                bart_progress = st.progress(0)
                bart_progress.progress(50)
                bart_summary = generate_summary(pipelines['BART'], text_input, 'BART')
                bart_progress.progress(100)
                st.subheader("BART Summary")
                st.write(bart_summary)
        
        # T5 Summary
        with t5_col:
            with st.spinner('Generating T5 Summary...'):
                t5_progress = st.progress(0)
                t5_progress.progress(50)
                t5_summary = generate_summary(pipelines['T5'], text_input, 'T5')
                t5_progress.progress(100)
                st.subheader("T5 Summary")
                st.write(t5_summary)
        
        # Pegasus Summary
        with pegasus_col:
            with st.spinner('Generating Pegasus Summary...'):
                pegasus_progress = st.progress(0)
                pegasus_progress.progress(50)
                pegasus_summary = generate_summary(pipelines['Pegasus'], text_input, 'Pegasus')
                pegasus_progress.progress(100)
                st.subheader("Pegasus Summary")
                st.write(pegasus_summary)

if __name__ == "__main__":
    main()