app.py

import streamlit as st
import multiprocessing
from transformers import pipeline
import os
import torch

# Optimize model loading and caching
@st.cache_resource
def load_model(model_name):
    """Efficiently load a summarization model."""
    try:
        # Use GPU if available
        device = 'cuda' if torch.cuda.is_available() else 'cpu'
        return pipeline("summarization", model=model_name, device=device)
    except Exception as e:
        st.error(f"Error loading model {model_name}: {e}")
        return None

def generate_summary(model, text, length_percentage=0.3):
    """
    Generate summary with intelligent length control.
    
    Args:
        model: Hugging Face summarization pipeline
        text: Input text to summarize
        length_percentage: Percentage of original text to use for summary
    
    Returns:
        Generated summary
    """
    # Intelligent length calculation
    word_count = len(text.split())
    max_length = max(50, int(word_count * length_percentage))
    min_length = max(30, int(word_count * 0.1))
    
    try:
        summary = model(
            text, 
            max_length=max_length, 
            min_length=min_length, 
            num_beams=4, 
            early_stopping=True
        )[0]['summary_text']
        return summary
    except Exception as e:
        st.error(f"Summarization error: {e}")
        return "Could not generate summary."

def parallel_summarize(text, length_percentage=0.3):
    """
    Generate summaries in parallel using multiprocessing.
    
    Args:
        text: Input text to summarize
        length_percentage: Percentage of original text to use for summary
    
    Returns:
        Dictionary of summaries from different models
    """
    model_configs = [
        ("facebook/bart-large-cnn", "BART"),
        ("t5-large", "T5"),
        ("google/pegasus-cnn_dailymail", "Pegasus")
    ]
    
    with multiprocessing.Pool(processes=min(len(model_configs), os.cpu_count())) as pool:
        args = [(load_model(model_name), text, length_percentage) 
                for model_name, _ in model_configs]
        
        results = pool.starmap(generate_summary, args)
    
    return {name: summary for (_, name), summary in zip(model_configs, results)}

def main():
    st.set_page_config(
        page_title="Multi-Model Text Summarization", 
        page_icon="📝",
        layout="wide"
    )
    
    # Title and Description
    st.title("🤖 Advanced Text Summarization")
    st.markdown("""
    Generate concise summaries using multiple state-of-the-art models.
    Intelligently adapts summary length based on input text.
    """)
    
    # Text Input
    text_input = st.text_area(
        "Paste your text here:", 
        height=250, 
        help="Enter the text you want to summarize"
    )
    
    # Length Control
    length_control = st.slider(
        "Summary Compression Rate", 
        min_value=0.1, 
        max_value=0.5, 
        value=0.3, 
        step=0.05,
        help="Adjust how much of the original text to keep in the summary"
    )
    
    if st.button("Generate Summaries", type="primary"):
        if not text_input:
            st.warning("Please enter some text to summarize.")
            return
        
        progress_text = st.empty()
        progress_bar = st.progress(0)
        
        stages = ["Initializing Models", "Running BART", "Running T5", "Running Pegasus", "Completed"]
        
        try:
            for i, stage in enumerate(stages[:-1], 1):
                progress_text.info(stage)
                progress_bar.progress(i * 20)
                
                if i == 2:
                    summaries = parallel_summarize(text_input, length_control)
            
            progress_text.success("Summarization Complete!")
            progress_bar.progress(100)
            
            st.subheader("📝 Generated Summaries")
            cols = st.columns(3)
            
            for (col, (model, summary)) in zip(cols, summaries.items()):
                with col:
                    st.markdown(f"### {model} Summary")
                    st.write(summary)
                    st.caption(f"Word Count: {len(summary.split())}")
        
        except Exception as e:
            st.error(f"An error occurred: {e}")
        
        finally:
            progress_text.empty()
            progress_bar.empty()

if __name__ == "__main__":
    main()