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laporgub / src /streamlit_app.py
Update src/streamlit_app.py
d9b6560 verified 11 months ago
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#!/usr/bin/env python3
"""
Streamlit App for Government Complaint Classification
Author: Based on XLM-RoBERTa implementation by Farrikh Alzami
"""

import streamlit as st
import pandas as pd
import numpy as np
import time
import io
from typing import List, Dict, Tuple
import os
from pathlib import Path

# Custom imports
from utils.model_loader import ModelLoader
from utils.text_preprocessor import TextPreprocessor
from utils.visualization import Visualizer

# Page configuration
st.set_page_config(
    page_title="Government Complaint Classifier",
    page_icon="🏛️",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS for warm color scheme
st.markdown("""
<style>
    .main-header {
        background: linear-gradient(90deg, #FF6B35 0%, #F7931E 100%);
        padding: 1rem;
        border-radius: 10px;
        margin-bottom: 2rem;
        text-align: center;
        color: white;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
    }
    
    .metric-container {
        background: linear-gradient(135deg, #FFF5E6 0%, #FFE5CC 100%);
        padding: 1rem;
        border-radius: 10px;
        border-left: 4px solid #FF6B35;
        margin: 0.5rem 0;
    }
    
    .prediction-container {
        background: linear-gradient(135deg, #FFF9F5 0%, #FFEDE6 100%);
        padding: 1.5rem;
        border-radius: 15px;
        border: 2px solid #FFB366;
        margin: 1rem 0;
        box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05);
    }
    
    .stProgress > div > div > div > div {
        background-color: #FF6B35;
    }
    
    div[data-testid="metric-container"] {
        background-color: #FFF5E6;
        border: 1px solid #FFD4A3;
        padding: 1rem;
        border-radius: 10px;
        box-shadow: 0 2px 4px rgba(255, 107, 53, 0.1);
    }
</style>
""", unsafe_allow_html=True)

class StreamlitApp:
    def __init__(self):
        self.model_loader = ModelLoader()
        self.text_preprocessor = TextPreprocessor()
        self.visualizer = Visualizer()
        
        # Initialize session state
        if 'model_type' not in st.session_state:
            st.session_state.model_type = 'cross_entropy'
        if 'model_loaded' not in st.session_state:
            st.session_state.model_loaded = False
        if 'predictions_history' not in st.session_state:
            st.session_state.predictions_history = []
        if 'last_analyzed_text' not in st.session_state:
            st.session_state.last_analyzed_text = ""
        if 'current_results' not in st.session_state:
            st.session_state.current_results = None
        if 'batch_results' not in st.session_state:
            st.session_state.batch_results = None

    def render_header(self):
        """Render application header"""
        st.markdown("""
        <div class="main-header">
            <h1>🏛️ Government Complaint Classifier</h1>
            <p>Klasifikasi Otomatis Keluhan Masyarakat menggunakan XLM-RoBERTa</p>
        </div>
        """, unsafe_allow_html=True)

    def render_sidebar(self):
        """Render sidebar with model selection"""
        with st.sidebar:
            st.header("⚙️ Model Configuration")
            
            # Model selection toggle
            model_options = {
                'cross_entropy': '🎯 Cross Entropy Loss',
                'focal_loss': '🔥 Focal Loss'
            }
            
            selected_model = st.radio(
                "Pilih Model:",
                options=list(model_options.keys()),
                format_func=lambda x: model_options[x],
                index=0 if st.session_state.model_type == 'cross_entropy' else 1
            )
            
            # Update session state if model changed
            if selected_model != st.session_state.model_type:
                st.session_state.model_type = selected_model
                st.session_state.model_loaded = False
                st.rerun()
            
            st.markdown("---")
            
            # Model availability check
            st.subheader("📁 Model Files Status")
            available_models = self.model_loader.get_available_models()
            
            for model_type in ['cross_entropy', 'focal_loss']:
                if model_type in available_models:
                    # Check if this model is currently loaded
                    is_current_loaded = (
                        hasattr(self.model_loader, 'current_model_type') and
                        self.model_loader.current_model_type == model_type and
                        hasattr(self.model_loader, 'classifier_pipeline') and
                        self.model_loader.classifier_pipeline is not None
                    )
                    
                    if is_current_loaded and model_type == st.session_state.model_type:
                        st.success(f"✅ {model_type.replace('_', ' ').title()} (Currently Loaded)")
                    else:
                        st.success(f"✅ {model_type.replace('_', ' ').title()}")
                else:
                    st.error(f"❌ {model_type.replace('_', ' ').title()}")
            
            if not available_models:
                st.warning("⚠️ No models found! Please check model directory.")
                st.info("""
                Expected structure:
                ```
                models/
                ├── cross_entropy/
                │   ├── model.safetensors
                │   ├── config.json
                │   └── ...
                └── focal_loss/
                    ├── model.safetensors
                    ├── config.json
                    └── ...
                ```
                """)
            
            st.markdown("---")
            
            # Model info
            st.subheader("📊 Model Information")
            
            # Real-time check model status
            is_model_actually_loaded = (
                hasattr(self.model_loader, 'classifier_pipeline') and 
                self.model_loader.classifier_pipeline is not None and
                self.model_loader.current_model_type == st.session_state.model_type
            )
            
            if is_model_actually_loaded:
                model_info = self.model_loader.get_model_info()
                st.success(f"**Status:** ✅ {model_info['status']}")
                st.info(f"**Current Model:** {model_info['model_type'].replace('_', ' ').title()}")
                st.info(f"**Device:** {model_info['device']}")
                st.info(f"**Categories:** {model_info['num_labels']}")
                
                # Show some model details
                with st.expander("🔍 Model Details"):
                    st.write(f"**Model Size:** {model_info['model_size']}")
                    st.write(f"**Available Categories:**")
                    categories = model_info.get('categories', [])
                    if categories:
                        # Show first 10 categories
                        display_categories = categories[:10]
                        st.write(", ".join(display_categories))
                        if len(categories) > 10:
                            st.write(f"... and {len(categories) - 10} more categories")
                    else:
                        st.write("Categories not available")
            else:
                st.info(f"""
                **Current Model:** {model_options[st.session_state.model_type]}
                
                **Architecture:** XLM-RoBERTa Base
                
                **Max Length:** 256 tokens
                
                **Languages:** Multilingual (ID, EN, etc.)
                
                **Status:** ⏳ Not loaded (will load on first use)
                """)
                
                # Show loading hint
                if not st.session_state.model_loaded:
                    st.info("💡 Model will be loaded automatically when you analyze text.")
            
            st.markdown("---")
            
            # Global reset button
            st.subheader("🔄 Reset Application")
            if st.button("🧹 Clear All & Reset Models", use_container_width=True, type="secondary"):
                # Clear all session states
                for key in list(st.session_state.keys()):
                    if key.startswith(('model_', 'predictions_', 'last_', 'current_', 'batch_')):
                        del st.session_state[key]
                
                # Reinitialize essential states
                st.session_state.model_type = 'cross_entropy'
                st.session_state.model_loaded = False
                st.session_state.predictions_history = []
                st.session_state.last_analyzed_text = ""
                st.session_state.current_results = None
                st.session_state.batch_results = None
                
                # Clear model loader state
                self.model_loader.model = None
                self.model_loader.tokenizer = None
                self.model_loader.label_mappings = None
                self.model_loader.classifier_pipeline = None
                self.model_loader.current_model_type = None
                
                # Clear cache
                st.cache_resource.clear()
                st.success("✅ Application reset complete!")
                st.rerun()
            
            st.markdown("---")
            
            # Prediction history
            if st.session_state.predictions_history:
                st.subheader("📈 Recent Predictions")
                for i, pred in enumerate(st.session_state.predictions_history[-3:]):
                    with st.expander(f"Prediction {len(st.session_state.predictions_history) - i}"):
                        st.write(f"**Text:** {pred['text'][:100]}...")
                        st.write(f"**Category:** {pred['category']}")
                        st.write(f"**Confidence:** {pred['confidence']:.2%}")

    def predict_single_text(self, text: str) -> Dict:
        """Predict single text with timing"""
        start_time = time.time()
        
        # Preprocess text
        cleaned_text = self.text_preprocessor.clean_text(text)
        
        # Force reload if model type changed or model not available
        force_reload = (
            not st.session_state.model_loaded or 
            self.model_loader.current_model_type != st.session_state.model_type or
            self.model_loader.classifier_pipeline is None
        )
        
        # Load model if needed
        try:
            if force_reload:
                with st.spinner("Loading model..."):
                    # Clear existing model first
                    self.model_loader.model = None
                    self.model_loader.tokenizer = None
                    self.model_loader.label_mappings = None
                    self.model_loader.classifier_pipeline = None
                    self.model_loader.current_model_type = None
                    
                    # Load fresh model
                    self.model_loader.load_model(st.session_state.model_type)
                    
                    # Update session state explicitly
                    st.session_state.model_loaded = True
                    
        except Exception as e:
            st.error(f"Failed to load model: {str(e)}")
            return {
                'predicted_category': 'Error: Model Loading Failed',
                'confidence': 0.0,
                'predicted_id': -1,
                'all_predictions': {'Error': 1.0},
                'processing_time': 0.0,
                'original_text': text,
                'cleaned_text': cleaned_text
            }
        
        # Make prediction
        try:
            result = self.model_loader.predict(cleaned_text)
        except Exception as e:
            st.error(f"Failed to make prediction: {str(e)}")
            return {
                'predicted_category': 'Error: Prediction Failed',
                'confidence': 0.0,
                'predicted_id': -1,
                'all_predictions': {'Error': 1.0},
                'processing_time': 0.0,
                'original_text': text,
                'cleaned_text': cleaned_text
            }
        
        processing_time = time.time() - start_time
        result['processing_time'] = processing_time
        result['original_text'] = text
        result['cleaned_text'] = cleaned_text
        
        return result

    def predict_batch_texts(self, texts: List[str]) -> List[Dict]:
        """Predict batch of texts"""
        # Force reload if model type changed or model not available
        force_reload = (
            not st.session_state.model_loaded or 
            self.model_loader.current_model_type != st.session_state.model_type or
            self.model_loader.classifier_pipeline is None
        )
        
        # Load model once for batch
        try:
            if force_reload:
                with st.spinner("Loading model for batch processing..."):
                    # Clear existing model first
                    self.model_loader.model = None
                    self.model_loader.tokenizer = None
                    self.model_loader.label_mappings = None
                    self.model_loader.classifier_pipeline = None
                    self.model_loader.current_model_type = None
                    
                    # Load fresh model
                    self.model_loader.load_model(st.session_state.model_type)
                    
                    # Update session state explicitly
                    st.session_state.model_loaded = True
                    
        except Exception as e:
            st.error(f"Failed to load model for batch processing: {str(e)}")
            # Return error results for all texts
            error_result = {
                'predicted_category': 'Error: Model Loading Failed',
                'confidence': 0.0,
                'predicted_id': -1,
                'all_predictions': {'Error': 1.0}
            }
            return [error_result] * len(texts)
        
        results = []
        progress_bar = st.progress(0)
        
        for i, text in enumerate(texts):
            try:
                # Preprocess
                cleaned_text = self.text_preprocessor.clean_text(text)
                
                # Predict
                result = self.model_loader.predict(cleaned_text)
                result['original_text'] = text
                result['cleaned_text'] = cleaned_text
                
                results.append(result)
                
            except Exception as e:
                st.warning(f"Failed to process text {i+1}: {str(e)}")
                # Add error result for this specific text
                error_result = {
                    'predicted_category': 'Error: Prediction Failed',
                    'confidence': 0.0,
                    'predicted_id': -1,
                    'all_predictions': {'Error': 1.0},
                    'original_text': text,
                    'cleaned_text': self.text_preprocessor.clean_text(text)
                }
                results.append(error_result)
            
            # Update progress
            progress_bar.progress((i + 1) / len(texts))
        
        return results

    def render_single_text_tab(self):
        """Render single text analysis tab"""
        st.header("📝 Single Text Analysis")
        
        # Show current model status at top
        is_model_loaded = (
            hasattr(self.model_loader, 'classifier_pipeline') and 
            self.model_loader.classifier_pipeline is not None and
            self.model_loader.current_model_type == st.session_state.model_type
        )
        
        if is_model_loaded:
            st.success(f"🎯 Current Model: **{st.session_state.model_type.replace('_', ' ').title()} - READY**")
        else:
            st.info(f"⏳ Current Model: **{st.session_state.model_type.replace('_', ' ').title()} - Will load on first use**")
        
        # Text input
        user_text = st.text_area(
            "Masukkan teks keluhan masyarakat:",
            height=150,
            placeholder="Contoh: Saya ingin melaporkan jalan rusak di daerah saya yang sudah lama tidak diperbaiki...",
            key="main_text_input"
        )
        
        # Analysis button
        col1, col2, col3, col4 = st.columns([2, 1, 1, 2])
        with col2:
            analyze_button = st.button(
                "🔍 Analyze Text",
                type="primary",
                use_container_width=True
            )
        
        with col3:
            clear_button = st.button(
                "🧹 Clear",
                type="secondary",
                use_container_width=True,
                help="Clear results and reset model state"
            )
        
        if clear_button:
            # Clear all states
            st.session_state.model_loaded = False
            st.session_state.predictions_history = []
            # Clear model loader state
            self.model_loader.model = None
            self.model_loader.tokenizer = None
            self.model_loader.label_mappings = None
            self.model_loader.classifier_pipeline = None
            self.model_loader.current_model_type = None
            # Clear cache
            st.cache_resource.clear()
            st.success("✅ Cleared all states and model cache!")
            st.rerun()
        
        if 'last_analyzed_text' not in st.session_state:
            st.session_state.last_analyzed_text = ""
        if 'current_results' not in st.session_state:
            st.session_state.current_results = None
        
        # Check if text has changed since last analysis
        text_changed = user_text.strip() != st.session_state.last_analyzed_text
        
        if clear_button:
            # Clear all states
            st.session_state.model_loaded = False
            st.session_state.predictions_history = []
            st.session_state.last_analyzed_text = ""
            st.session_state.current_results = None
            # Clear model loader state
            self.model_loader.model = None
            self.model_loader.tokenizer = None
            self.model_loader.label_mappings = None
            self.model_loader.classifier_pipeline = None
            self.model_loader.current_model_type = None
            # Clear cache
            st.cache_resource.clear()
            st.success("✅ Cleared all states and model cache!")
            st.rerun()
        
        if analyze_button and user_text.strip():
            try:
                with st.spinner("Analyzing text..."):
                    result = self.predict_single_text(user_text)
                
                # Store in history and session state
                st.session_state.predictions_history.append({
                    'text': user_text,
                    'category': result['predicted_category'],
                    'confidence': result['confidence']
                })
                st.session_state.last_analyzed_text = user_text.strip()
                st.session_state.current_results = result
                
                # Display results
                self.display_single_prediction_results(result)
                
            except Exception as e:
                st.error(f"❌ Error during analysis: {str(e)}")
                st.info("💡 Try clicking the 'Clear' button to reset the model state.")
                
        elif analyze_button and not user_text.strip():
            st.warning("⚠️ Please enter some text to analyze!")
        
        # Display previous results if available and text hasn't changed
        elif st.session_state.current_results and not text_changed and not analyze_button:
            st.info("📋 Showing previous analysis results. Click 'Analyze Text' to update or 'Clear' to reset.")
            self.display_single_prediction_results(st.session_state.current_results)
        
        # Show hint if text has changed
        elif text_changed and st.session_state.current_results:
            st.info("✏️ Text has been modified. Click 'Analyze Text' to get new predictions or 'Clear' to reset.")

    def display_single_prediction_results(self, result: Dict):
        """Display single prediction results"""
        st.markdown("## 📊 Analysis Results")
        
        # Main prediction container
        st.markdown(f"""
        <div class="prediction-container">
            <h3>🎯 Predicted Category</h3>
            <h2 style="color: #FF6B35; margin: 0;">{result['predicted_category']}</h2>
        </div>
        """, unsafe_allow_html=True)
        
        # Metrics
        col1, col2, col3 = st.columns(3)
        
        with col1:
            st.metric(
                label="🎯 Confidence Score",
                value=f"{result['confidence']:.2%}",
                delta=f"Top prediction"
            )
        
        with col2:
            st.metric(
                label="⏱️ Processing Time",
                value=f"{result['processing_time']:.3f}s",
                delta="Real-time"
            )
        
        with col3:
            st.metric(
                label="📝 Text Length",
                value=f"{len(result['cleaned_text'])} chars",
                delta="After cleaning"
            )
        
        # Confidence visualization
        st.markdown("### 📈 Confidence Scores by Category")
        fig = self.visualizer.plot_confidence_scores(result['all_predictions'])
        st.plotly_chart(fig, use_container_width=True)
        
        # Top predictions table
        st.markdown("### 🏆 Top 5 Predictions")
        top_predictions = sorted(
            result['all_predictions'].items(), 
            key=lambda x: x[1], 
            reverse=True
        )[:5]
        
        df_top = pd.DataFrame([
            {
                'Rank': i+1,
                'Category': category,
                'Confidence': f"{confidence:.2%}",
                'Confidence_Score': confidence
            }
            for i, (category, confidence) in enumerate(top_predictions)
        ])
        
        # Style the dataframe
        styled_df = df_top.style.format({
            'Confidence_Score': '{:.4f}'
        }).hide(['Confidence_Score'], axis=1).background_gradient(
            subset=['Confidence_Score'], 
            cmap='Oranges'
        )
        
        st.dataframe(styled_df, use_container_width=True)
        
        # Show preprocessing details
        with st.expander("🔧 Preprocessing Details"):
            col1, col2 = st.columns(2)
            
            with col1:
                st.markdown("**Original Text:**")
                st.text_area(
                    "Original Text", 
                    value=result['original_text'], 
                    height=100, 
                    disabled=True, 
                    key="original_text_display",
                    label_visibility="collapsed"
                )
            
            with col2:
                st.markdown("**Cleaned Text:**")
                st.text_area(
                    "Cleaned Text", 
                    value=result['cleaned_text'], 
                    height=100, 
                    disabled=True, 
                    key="cleaned_text_display",
                    label_visibility="collapsed"
                )

    def render_batch_processing_tab(self):
        """Render batch processing tab"""
        st.header("📊 Batch Processing")
        
        # Show current model status at top
        is_model_loaded = (
            hasattr(self.model_loader, 'classifier_pipeline') and 
            self.model_loader.classifier_pipeline is not None and
            self.model_loader.current_model_type == st.session_state.model_type
        )
        
        if is_model_loaded:
            st.success(f"🎯 Current Model: **{st.session_state.model_type.replace('_', ' ').title()} - READY**")
        else:
            st.info(f"⏳ Current Model: **{st.session_state.model_type.replace('_', ' ').title()} - Will load on first use**")
        
        # File upload
        st.markdown("### 📁 Upload CSV File")
        uploaded_file = st.file_uploader(
            "Choose a CSV file containing texts to classify",
            type=['csv'],
            help="CSV should have a column named 'text' containing the texts to classify"
        )
        
        if uploaded_file is not None:
            try:
                # Read uploaded file
                df = pd.read_csv(uploaded_file)
                
                # Show preview
                st.markdown("### 👀 Data Preview")
                st.dataframe(df.head(10))
                
                # Column selection
                text_columns = df.columns.tolist()
                selected_column = st.selectbox(
                    "Select the text column to classify:",
                    options=text_columns,
                    index=0 if 'text' not in text_columns else text_columns.index('text')
                )
                
                # Batch processing button
                col1, col2, col3, col4 = st.columns([2, 1, 1, 2])
                with col2:
                    process_button = st.button(
                        "🚀 Process Batch",
                        type="primary",
                        use_container_width=True
                    )
                
                with col3:
                    clear_batch_button = st.button(
                        "🧹 Clear Batch",
                        type="secondary", 
                        use_container_width=True,
                        help="Clear batch results and reset model"
                    )
                
                if clear_batch_button:
                    # Clear batch-specific states
                    st.session_state.batch_results = None
                    st.session_state.model_loaded = False
                    # Clear model loader state
                    self.model_loader.model = None
                    self.model_loader.tokenizer = None
                    self.model_loader.label_mappings = None
                    self.model_loader.classifier_pipeline = None
                    self.model_loader.current_model_type = None
                    # Clear cache
                    st.cache_resource.clear()
                    st.success("✅ Cleared batch results and model cache!")
                    st.rerun()
                
                if process_button:
                    texts = df[selected_column].astype(str).tolist()
                    
                    st.markdown("### ⚡ Processing Batch...")
                    start_time = time.time()
                    
                    try:
                        results = self.predict_batch_texts(texts)
                        total_time = time.time() - start_time
                        
                        # Store results in session state
                        st.session_state.batch_results = {
                            'original_df': df,
                            'results': results,
                            'selected_column': selected_column,
                            'total_time': total_time
                        }
                        
                        # Display batch results
                        self.display_batch_results(df, results, selected_column, total_time)
                        
                    except Exception as e:
                        st.error(f"❌ Error during batch processing: {str(e)}")
                        st.info("💡 Try clicking the 'Clear Batch' button to reset the model state.")
                
                # Display previous batch results if available
                elif st.session_state.batch_results:
                    st.info("📋 Showing previous batch results. Upload new file to process again or click 'Clear Batch' to reset.")
                    batch_data = st.session_state.batch_results
                    self.display_batch_results(
                        batch_data['original_df'], 
                        batch_data['results'], 
                        batch_data['selected_column'], 
                        batch_data['total_time']
                    )
                    
            except Exception as e:
                st.error(f"Error reading CSV file: {str(e)}")
        
        else:
            # Show example CSV format
            st.markdown("### 📋 Expected CSV Format")
            example_df = pd.DataFrame({
                'id': [1, 2, 3],
                'text': [
                    'Jalan di depan rumah saya rusak parah',
                    'Pelayanan di kantor kelurahan lambat',
                    'Lingkungan sekitar kotor dan tidak terawat'
                ]
            })
            st.dataframe(example_df)

    def display_batch_results(self, original_df: pd.DataFrame, results: List[Dict], 
                            text_column: str, total_time: float):
        """Display batch processing results"""
        st.markdown("## 📊 Batch Processing Results")
        
        # Summary metrics
        col1, col2, col3, col4 = st.columns(4)
        
        with col1:
            st.metric("📄 Total Texts", len(results))
        
        with col2:
            avg_confidence = np.mean([r['confidence'] for r in results])
            st.metric("🎯 Avg Confidence", f"{avg_confidence:.2%}")
        
        with col3:
            st.metric("⏱️ Total Time", f"{total_time:.2f}s")
        
        with col4:
            st.metric("🚀 Speed", f"{len(results)/total_time:.1f} texts/sec")
        
        # Create results dataframe
        results_df = original_df.copy()
        results_df['predicted_category'] = [r['predicted_category'] for r in results]
        results_df['confidence'] = [r['confidence'] for r in results]
        results_df['cleaned_text'] = [r['cleaned_text'] for r in results]
        
        # Category distribution
        st.markdown("### 📈 Category Distribution")
        category_counts = results_df['predicted_category'].value_counts()
        fig = self.visualizer.plot_category_distribution(category_counts)
        st.plotly_chart(fig, use_container_width=True)
        
        # Results table
        st.markdown("### 📋 Detailed Results")
        display_df = results_df[[text_column, 'predicted_category', 'confidence']].copy()
        display_df['confidence'] = display_df['confidence'].apply(lambda x: f"{x:.2%}")
        
        st.dataframe(display_df, use_container_width=True)
        
        # Download results
        st.markdown("### 💾 Download Results")
        
        # Prepare Excel data with all predictions
        excel_data = []
        for i, result in enumerate(results):
            row = original_df.iloc[i].to_dict()
            row['predicted_category'] = result['predicted_category']
            row['confidence'] = result['confidence']
            row['cleaned_text'] = result['cleaned_text']
            
            # Add top 3 predictions
            top_3 = sorted(result['all_predictions'].items(), key=lambda x: x[1], reverse=True)[:3]
            for j, (cat, conf) in enumerate(top_3, 1):
                row[f'top_{j}_category'] = cat
                row[f'top_{j}_confidence'] = conf
            
            excel_data.append(row)
        
        excel_df = pd.DataFrame(excel_data)
        
        # Create Excel file
        output = io.BytesIO()
        with pd.ExcelWriter(output, engine='openpyxl') as writer:
            excel_df.to_excel(writer, sheet_name='Results', index=False)
            
            # Add summary sheet
            summary_df = pd.DataFrame([
                ['Total Texts Processed', len(results)],
                ['Average Confidence', f"{avg_confidence:.2%}"],
                ['Processing Time', f"{total_time:.2f} seconds"],
                ['Model Used', st.session_state.model_type.replace('_', ' ').title()],
                ['Processing Speed', f"{len(results)/total_time:.1f} texts/second"]
            ], columns=['Metric', 'Value'])
            
            summary_df.to_excel(writer, sheet_name='Summary', index=False)
        
        # Download button
        col1, col2, col3 = st.columns([2, 1, 2])
        with col2:
            st.download_button(
                label="📥 Download Excel Report",
                data=output.getvalue(),
                file_name=f"complaint_classification_results_{st.session_state.model_type}.xlsx",
                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
                use_container_width=True
            )

    def render_about_tab(self):
        """Render about/help tab"""
        st.header("ℹ️ About This Application")
        
        st.markdown("""
        ### 🎯 Purpose
        This application automatically classifies government complaints using state-of-the-art 
        XLM-RoBERTa transformer models. It supports both Cross Entropy and Focal Loss variants 
        for handling imbalanced datasets.
        
        ### 🔧 Technical Details
        - **Model Architecture:** XLM-RoBERTa Base (Multi-lingual)
        - **Framework:** Hugging Face Transformers + PyTorch
        - **Preprocessing:** HTML cleaning, emoji removal, text normalization
        - **Maximum Input Length:** 256 tokens
        - **Languages Supported:** Indonesian, English, and more
        
        ### 📊 Model Comparison
        - **Cross Entropy Loss:** Traditional classification loss with class weights
        - **Focal Loss:** Specialized for imbalanced datasets, focuses on hard examples
        
        ### 🚀 Usage Guide
        
        #### Single Text Analysis:
        1. Select your preferred model from the sidebar
        2. Enter text in the textarea
        3. Click "Analyze Text"
        4. View predictions and confidence scores
        
        #### Batch Processing:
        1. Prepare a CSV file with text data
        2. Upload the file in the Batch Processing tab
        3. Select the text column to classify
        4. Click "Process Batch"
        5. Download results as Excel file
        
        ### 📝 CSV Format for Batch Processing
        Your CSV should contain at least one column with text data:
        ```
        id,text,other_columns...
        1,"Jalan rusak perlu diperbaiki",metadata
        2,"Pelayanan lambat di kantor",metadata
        ```
        
        ### ⚠️ Limitations
        - Maximum text length: 256 tokens (approximately 200-300 words)
        - Model performance depends on training data quality
        - Processing time varies with text length and batch size
        
        ### 👨‍💻 Credits
        Based on research implementation by Farrikh Alzami using XLM-RoBERTa for 
        government complaint classification with focal loss optimization.
        """)

    def run(self):
        """Main application runner"""
        self.render_header()
        self.render_sidebar()
        
        # Main content tabs
        tab1, tab2, tab3 = st.tabs(["📝 Single Text", "📊 Batch Processing", "ℹ️ About"])
        
        with tab1:
            self.render_single_text_tab()
        
        with tab2:
            self.render_batch_processing_tab()
        
        with tab3:
            self.render_about_tab()

def main():
    """Main function"""
    try:
        app = StreamlitApp()
        app.run()
    except Exception as e:
        st.error(f"Application error: {str(e)}")
        st.info("Please ensure all model files are properly placed in the models/ directory.")

if __name__ == "__main__":
    main()