app.py

"""

Streamlit UI for Document Classification

Upload PDFs and classify them using SmolVLM.

Optimized with pre-loading and concurrent processing.

"""

import streamlit as st
import pandas as pd
import json
from pathlib import Path
from datetime import datetime
import tempfile
import os
from concurrent.futures import ThreadPoolExecutor, as_completed
import threading

# Import our classifier modules
from pdf_to_image import pdf_to_images
from smolvlm_classifier import SmolVLMClassifier


# Page config
st.set_page_config(
    page_title="Document Classifier",
    page_icon="📄",
    layout="wide"
)

# Custom CSS for better styling
st.markdown("""

<style>

    .main-header {

        font-size: 2.5rem;

        font-weight: bold;

        color: #1f77b4;

        margin-bottom: 1rem;

    }

    .result-box {

        background-color: #f0f8ff;

        padding: 0.8rem 1rem;

        border-radius: 8px;

        border-left: 4px solid #1f77b4;

        margin: 0.5rem 0;

        display: inline-block;

    }

    .doc-type {

        font-size: 1.2rem;

        font-weight: bold;

        color: #2e7d32;

        margin: 0;

    }

    .file-info {

        font-size: 0.9rem;

        color: #555;

        margin: 0.2rem 0;

    }

    .model-status {

        padding: 0.5rem;

        border-radius: 5px;

        margin-bottom: 1rem;

    }

</style>

""", unsafe_allow_html=True)


@st.cache_resource
def load_classifier():
    """Load the classifier once and cache it."""
    return SmolVLMClassifier()


def load_history():
    """Load classification history from JSON file."""
    history_file = Path("classification_history.json")
    if history_file.exists():
        with open(history_file, "r", encoding="utf-8") as f:
            return json.load(f)
    return []


def save_history(history):
    """Save classification history to JSON file."""
    with open("classification_history.json", "w", encoding="utf-8") as f:
        json.dump(history, f, indent=2, ensure_ascii=False)


def add_to_history(filename, doc_type, num_pages):
    """Add a classification result to history."""
    history = load_history()
    history.insert(0, {
        "filename": filename,
        "document_type": doc_type,
        "num_pages": num_pages,
        "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    })
    # Keep only last 100 entries
    history = history[:100]
    save_history(history)
    return history


def convert_pdf_to_images(uploaded_file):
    """Convert a single PDF to images. Used for threading."""
    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
        tmp_file.write(uploaded_file.getvalue())
        tmp_path = tmp_file.name
    try:
        images = pdf_to_images(tmp_path, dpi=100)
        return uploaded_file.name, images
    finally:
        os.unlink(tmp_path)


def main():
    # Header
    st.markdown('<div class="main-header">📄 Document Classifier</div>', unsafe_allow_html=True)
    st.markdown("Upload PDF documents to classify them using SmolVLM AI.")
    
    # PRE-LOAD MODEL AT APP START (not on button click)
    # This runs once when the app starts
    with st.spinner("🔄 Loading AI model (one-time setup)..."):
        classifier = load_classifier()
    st.success("✅ Model ready!")
    
    # Sidebar for history
    with st.sidebar:
        st.header("📋 Classification History")
        history = load_history()
        
        if history:
            # Show as table
            df_history = pd.DataFrame(history)
            st.dataframe(
                df_history[["filename", "document_type", "timestamp"]],
                hide_index=True,
                width="stretch"
            )
            
            # Clear history button
            if st.button("🗑️ Clear History"):
                save_history([])
                st.rerun()
        else:
            st.info("No classification history yet. Upload a document to get started!")
    
    # Main content - two columns
    col1, col2 = st.columns([1, 1])
    
    with col1:
        st.subheader("📤 Upload Documents")
        
        # File uploader - MULTIPLE FILES
        uploaded_files = st.file_uploader(
            "Choose PDF files",
            type=["pdf"],
            accept_multiple_files=True,
            help="Upload one or more PDF documents to classify"
        )
        
        if uploaded_files:
            st.success(f"✅ Uploaded {len(uploaded_files)} file(s)")
            
            # Store images for preview
            if "pdf_previews" not in st.session_state:
                st.session_state["pdf_previews"] = {}
            
            # Show file list with preview option
            for f in uploaded_files:
                with st.expander(f"📄 {f.name} ({f.size / 1024:.1f} KB)", expanded=False):
                    # Generate preview if not cached
                    if f.name not in st.session_state["pdf_previews"]:
                        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
                            tmp_file.write(f.getvalue())
                            tmp_path = tmp_file.name
                        try:
                            images = pdf_to_images(tmp_path, dpi=100)
                            st.session_state["pdf_previews"][f.name] = images
                        finally:
                            os.unlink(tmp_path)
                    
                    # Show preview
                    images = st.session_state["pdf_previews"].get(f.name, [])
                    if images:
                        if len(images) > 1:
                            page_num = st.selectbox(
                                f"Page", 
                                range(1, len(images) + 1),
                                key=f"page_{f.name}"
                            )
                            st.image(images[page_num - 1], caption=f"Page {page_num} of {len(images)}", width="stretch")
                        else:
                            st.image(images[0], caption="Page 1", width="stretch")
                    else:
                        st.error("Could not load PDF preview")
            
            # Classify button
            if st.button("🔍 Classify All Documents", type="primary", width="stretch"):
                import time
                
                all_results = []
                progress_bar = st.progress(0)
                status_text = st.empty()
                
                total_start_time = time.time()
                
                # STEP 1: Pre-convert all PDFs to images using threading
                status_text.text("📄 Converting PDFs to images (parallel)...")
                pdf_conversion_start = time.time()
                
                pdf_images = {}
                
                # Use ThreadPoolExecutor for parallel PDF conversion
                with ThreadPoolExecutor(max_workers=4) as executor:
                    # Submit all PDF conversion tasks
                    future_to_file = {
                        executor.submit(convert_pdf_to_images, f): f 
                        for f in uploaded_files 
                        if f.name not in st.session_state.get("pdf_previews", {})
                    }
                    
                    # Also add cached previews
                    for f in uploaded_files:
                        if f.name in st.session_state.get("pdf_previews", {}):
                            pdf_images[f.name] = st.session_state["pdf_previews"][f.name]
                    
                    # Collect results
                    for future in as_completed(future_to_file):
                        filename, images = future.result()
                        pdf_images[filename] = images
                
                pdf_conversion_time = time.time() - pdf_conversion_start
                print(f"\n📄 PDF Conversion: {pdf_conversion_time:.2f}s (parallel)")
                
                progress_bar.progress(0.2)
                status_text.text("🤖 Classifying documents...")
                
                # STEP 2: Classify each document with timing
                classification_start = time.time()
                
                for idx, uploaded_file in enumerate(uploaded_files):
                    doc_start_time = time.time()
                    images = pdf_images.get(uploaded_file.name, [])
                    
                    if not images:
                        result = {
                            "filename": uploaded_file.name,
                            "document_type": "Error: Could not extract pages",
                            "num_pages": 0,
                            "classify_time": 0
                        }
                    else:
                        status_text.text(f"🤖 Classifying {idx + 1}/{len(uploaded_files)}: {uploaded_file.name}")
                        
                        # Classify with timing
                        classify_start = time.time()
                        classification = classifier.classify_document(images)
                        classify_time = time.time() - classify_start
                        
                        result = {
                            "filename": uploaded_file.name,
                            "document_type": classification["document_type"],
                            "num_pages": classification["num_pages"],
                            "classify_time": round(classify_time, 2)
                        }
                        
                        # Terminal output
                        print(f"  📄 {uploaded_file.name}")
                        print(f"     Pages: {classification['num_pages']}")
                        print(f"     Type: {classification['document_type']}")
                        print(f"     Classification time: {classify_time:.2f}s")
                        
                        # Add to history
                        add_to_history(
                            uploaded_file.name,
                            classification["document_type"],
                            classification["num_pages"]
                        )
                    
                    all_results.append(result)
                    
                    # Update progress
                    progress_bar.progress(0.2 + 0.8 * (idx + 1) / len(uploaded_files))
                
                total_classification_time = time.time() - classification_start
                total_time = time.time() - total_start_time
                
                # Print summary to terminal
                print(f"\n{'='*50}")
                print("TIMING SUMMARY")
                print(f"{'='*50}")
                print(f"Documents processed: {len(all_results)}")
                print(f"PDF conversion (parallel): {pdf_conversion_time:.2f}s")
                print(f"Classification (sequential): {total_classification_time:.2f}s")
                print(f"Average per document: {total_classification_time/len(all_results):.2f}s")
                print(f"Total time: {total_time:.2f}s ({total_time/60:.1f} min)")
                print(f"{'='*50}\n")
                
                # Store timing info
                st.session_state["timing"] = {
                    "pdf_conversion": round(pdf_conversion_time, 2),
                    "classification": round(total_classification_time, 2),
                    "total": round(total_time, 2),
                    "total_min": round(total_time / 60, 2),
                    "avg_per_doc": round(total_classification_time / len(all_results), 2)
                }
                
                status_text.text(f"✅ Complete! Total: {total_time:.1f}s ({total_time/60:.1f} min)")
                st.session_state["results"] = all_results
    
    with col2:
        st.subheader("📊 Classification Results")
        
        # Show results
        if "results" in st.session_state and st.session_state["results"]:
            results = st.session_state["results"]
            
            # Show as compact table with timing
            df_results = pd.DataFrame(results)
            st.dataframe(
                df_results,
                hide_index=True,
                width="stretch",
                column_config={
                    "filename": st.column_config.TextColumn("File", width="medium"),
                    "document_type": st.column_config.TextColumn("Type", width="medium"),
                    "num_pages": st.column_config.NumberColumn("Pages", width="small"),
                    "classify_time": st.column_config.NumberColumn("Time (s)", width="small")
                }
            )
            
            # Show timing summary if available
            if "timing" in st.session_state:
                timing = st.session_state["timing"]
                st.markdown("---")
                st.markdown("**⏱️ Timing Summary**")
                col_t1, col_t2, col_t3 = st.columns(3)
                with col_t1:
                    st.metric("PDF Conversion", f"{timing['pdf_conversion']}s")
                with col_t2:
                    st.metric("Classification", f"{timing['classification']}s")
                with col_t3:
                    st.metric("Avg per Doc", f"{timing['avg_per_doc']}s")
                
                st.info(f"**Total Time:** {timing['total']}s ({timing['total_min']} min)")
            
            # Summary
            st.success(f"✅ Classified {len(results)} document(s)")
            
            # Show individual result boxes (compact)
            for result in results:
                st.markdown(f"""

                <div class="result-box">

                    <p class="file-info"><strong>{result['filename']}</strong> ({result['num_pages']} pages)</p>

                    <p class="doc-type">📑 {result['document_type']}</p>

                </div>

                """, unsafe_allow_html=True)
            
        else:
            st.info("👆 Upload and classify documents to see results here.")


if __name__ == "__main__":
    main()