Delete app.py

app.py

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-"""
-♻️ RecycleVision - Garbage Image Classification System
-Streamlit Application — Deep Learning based Waste Classification & Recycling Assistant
-"""
-
-import streamlit as st
-import tensorflow as tf
-from tensorflow.keras.models import load_model
-from tensorflow.keras.applications.efficientnet import preprocess_input
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import plotly.express as px
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots 
-from PIL import Image
-import cv2
-import os
-from datetime import datetime
-import time
-import random
-import warnings
-warnings.filterwarnings('ignore')
-
-# Page configuration
-st.set_page_config(
-    page_title="♻️ RecycleVision - Garbage Classifier",
-    page_icon="♻️",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-# Custom CSS for professional styling
-st.markdown("""
-<style>
-    /* Main header styling */
-    .main-header {
-        font-size: 3rem;
-        background: linear-gradient(135deg, #2E8B57, #4CAF50);
-        -webkit-background-clip: text;
-        -webkit-text-fill-color: transparent;
-        text-align: center;
-        margin-bottom: 0.5rem;
-        font-weight: bold;
-    }
-    
-    .sub-header {
-        font-size: 1.8rem;
-        color: #264653;
-        margin-top: 1rem;
-        margin-bottom: 1rem;
-        font-weight: 600;
-    }
-    
-    /* Card styling */
-    .card {
-        background-color: #ffffff;
-        border-radius: 15px;
-        padding: 25px;
-        box-shadow: 0 8px 16px rgba(0,0,0,0.1);
-        margin-bottom: 25px;
-        border-left: 5px solid #4CAF50;
-    }
-    
-    .metric-card {
-        background: linear-gradient(135deg, #f8f9fa, #ffffff);
-        border-radius: 12px;
-        padding: 20px;
-        box-shadow: 0 4px 8px rgba(0,0,0,0.05);
-        text-align: center;
-        border: 1px solid #e9ecef;
-        transition: transform 0.3s;
-    }
-    
-    .metric-card:hover {
-        transform: translateY(-5px);
-        box-shadow: 0 8px 16px rgba(0,0,0,0.1);
-    }
-    
-    /* Class-specific styling */
-    .plastic {
-        background-color: #FF6B6B;
-        color: white;
-        padding: 8px;
-        border-radius: 8px;
-        text-align: center;
-    }
-    
-    .paper {
-        background-color: #4ECDC4;
-        color: white;
-        padding: 8px;
-        border-radius: 8px;
-        text-align: center;
-    }
-    
-    .glass {
-        background-color: #45B7D1;
-        color: white;
-        padding: 8px;
-        border-radius: 8px;
-        text-align: center;
-    }
-    
-    .metal {
-        background-color: #96CEB4;
-        color: white;
-        padding: 8px;
-        border-radius: 8px;
-        text-align: center;
-    }
-    
-    .cardboard {
-        background-color: #FFEEAD;
-        color: #333;
-        padding: 8px;
-        border-radius: 8px;
-        text-align: center;
-    }
-    
-    .trash {
-        background-color: #D4A5A5;
-        color: white;
-        padding: 8px;
-        border-radius: 8px;
-        text-align: center;
-    }
-    
-    /* Button styling */
-    .classify-btn {
-        background: linear-gradient(135deg, #4CAF50, #45a049);
-        color: white;
-        padding: 12px 30px;
-        font-size: 1.2rem;
-        border-radius: 25px;
-        border: none;
-        cursor: pointer;
-        transition: all 0.3s;
-        width: 100%;
-        font-weight: bold;
-    }
-    
-    .classify-btn:hover {
-        background: linear-gradient(135deg, #45a049, #4CAF50);
-        transform: scale(1.02);
-        box-shadow: 0 5px 15px rgba(76, 175, 80, 0.3);
-    }
-    
-    /* Info boxes */
-    .info-box {
-        background-color: #e3f2fd;
-        border-left: 5px solid #2196F3;
-        padding: 15px;
-        border-radius: 8px;
-        margin: 10px 0;
-    }
-    
-    .warning-box {
-        background-color: #fff3cd;
-        border-left: 5px solid #ffc107;
-        padding: 15px;
-        border-radius: 8px;
-        margin: 10px 0;
-    }
-    
-    .success-box {
-        background-color: #d4edda;
-        border-left: 5px solid #28a745;
-        padding: 15px;
-        border-radius: 8px;
-        margin: 10px 0;
-    }
-    
-    /* Footer */
-    .footer {
-        text-align: center;
-        padding: 20px;
-        background: linear-gradient(135deg, #2c3e50, #3498db);
-        color: white;
-        border-radius: 10px;
-        margin-top: 30px;
-    }
-    
-    /* Progress bar */
-    .stProgress > div > div > div > div {
-        background-color: #4CAF50;
-    }
-</style>
-""", unsafe_allow_html=True)
-
-# Title with animation
-st.markdown('<h1 class="main-header">♻️ RecycleVision</h1>', unsafe_allow_html=True)
-st.markdown('<p style="text-align: center; font-size: 1.2rem; color: #666;">AI-Powered Waste Classification System for Smart Recycling</p>', unsafe_allow_html=True)
-
-# Sidebar navigation
-st.sidebar.markdown("## 📊 Navigation")
-page = st.sidebar.radio(
-    "Go to:",
-    ["🏠 Home & Classification", "📈 Model Performance", "ℹ️ About & Info", "⚙️ Settings"]
-)
-
-# ========== FIXED FUNCTION: load_model_and_classes ==========
-@st.cache_resource(show_spinner=False)
-def load_model_and_classes():
-    """Load the trained model and class labels"""
-    try:
-        # Try to load from current directory
-        model_paths = [
-            "RecycleVision_EfficientNetB0_Final.keras",
-            "best_efficientnet.keras",
-            "model.keras",
-            "RecycleVision_Final_Model.keras"
-        ]
-        
-        model = None
-        loaded_path = None
-        for path in model_paths:
-            if os.path.exists(path):
-                try:
-                    model = load_model(path)
-                    loaded_path = path
-                    break
-                except Exception as e:
-                    st.sidebar.warning(f"Failed to load {path}: {str(e)}")
-                    continue
-        
-        # Class labels (always return these)
-        class_labels = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
-        
-        if model is None:
-            st.sidebar.warning("⚠️ Model file not found. Running in DEMO MODE.")
-            return None, class_labels
-        
-        st.sidebar.success(f"✅ Model loaded from: {loaded_path}")
-        return model, class_labels
-        
-    except Exception as e:
-        st.sidebar.error(f"Error loading model: {str(e)}")
-        class_labels = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
-        return None, class_labels
-
-# ========== LOAD MODEL AND HANDLE RETURN VALUES ==========
-load_result = load_model_and_classes()
-
-# Default values (in case model not found)
-class_colors = {
-    'cardboard': '#FFEEAD',
-    'glass': '#45B7D1',
-    'metal': '#96CEB4',
-    'paper': '#4ECDC4',
-    'plastic': '#FF6B6B',
-    'trash': '#D4A5A5'
-}
-
-recycling_info = {
-    'cardboard': {
-        'type': '📦 Paper/Cardboard',
-        'recyclable': '✅ Fully Recyclable',
-        'bin_color': 'Blue Bin',
-        'processing': 'Pulped and remanufactured into paper products',
-        'facts': 'Cardboard can be recycled 5-7 times before fibers become too short',
-        'icon': '📦',
-        'tips': 'Flatten boxes before recycling to save space'
-    },
-    'glass': {
-        'type': '🥃 Glass',
-        'recyclable': '✅ 100% Recyclable',
-        'bin_color': 'Green Bin',
-        'processing': 'Crushed, melted, and molded into new glass products',
-        'facts': 'Glass takes 1 million years to decompose in landfill',
-        'icon': '🥃',
-        'tips': 'Rinse containers; labels can stay on'
-    },
-    'metal': {
-        'type': '🔩 Metal',
-        'recyclable': '✅ Highly Recyclable',
-        'bin_color': 'Yellow Bin',
-        'processing': 'Shredded, melted, and purified',
-        'facts': 'Recycling aluminum saves 95% of energy needed to make new metal',
-        'icon': '🔩',
-        'tips': 'Cans can be crushed to save space'
-    },
-    'paper': {
-        'type': '📄 Paper',
-        'recyclable': '✅ Recyclable',
-        'bin_color': 'Blue Bin',
-        'processing': 'Mixed with water to create pulp, then pressed and dried',
-        'facts': 'Each ton of recycled paper saves 17 trees',
-        'icon': '📄',
-        'tips': 'Keep paper dry and clean'
-    },
-    'plastic': {
-        'type': '🧴 Plastic',
-        'recyclable': '⚠️ Depends on type',
-        'bin_color': 'White/Clear Bin',
-        'processing': 'Sorted by type, shredded, melted, and pelletized',
-        'facts': 'Plastic takes 450+ years to decompose',
-        'icon': '🧴',
-        'tips': 'Check recycling number on bottom'
-    },
-    'trash': {
-        'type': '🗑️ General Waste',
-        'recyclable': '❌ Not Recyclable',
-        'bin_color': 'Black Bin',
-        'processing': 'Landfill or incineration',
-        'facts': 'Reduce waste by choosing reusable products',
-        'icon': '🗑️',
-        'tips': 'Consider if items can be reused or repaired'
-    }
-}
-
-# Check what was returned from load_model_and_classes
-if load_result is None:
-    st.error("Failed to initialize application")
-    st.stop()
-elif len(load_result) == 2:
-    model, class_labels = load_result
-    # class_colors and recycling_info already defined above
-else:
-    st.error("Unexpected return value from load_model_and_classes()")
-    st.stop()
-
-# Check if model is None (demo mode)
-if model is None:
-    st.sidebar.info("🔧 Running in DEMO MODE - Showing sample predictions")
-    st.sidebar.warning("Train the model and place it in the project folder for real predictions")
-
-# ========== FIXED FUNCTION: predict_image ==========
-def predict_image(model, image):
-    """Make prediction on image"""
-    try:
-        if model is None:
-            # Dummy prediction for UI demonstration
-            # Return realistic-looking predictions
-            # Random but biased towards certain classes for demo
-            pred = np.random.random(6)
-            pred = pred / pred.sum()  # Normalize to sum to 1
-            return pred
-            
-        processed_img = preprocess_image(image)
-        if processed_img is not None:
-            predictions = model.predict(processed_img, verbose=0)[0]
-            return predictions
-        return None
-    except Exception as e:
-        st.error(f"Error making prediction: {str(e)}")
-        return None
-
-# Helper functions (unchanged)
-def preprocess_image(image):
-    """Preprocess image for model prediction"""
-    try:
-        # Convert to RGB if necessary
-        if image.mode != 'RGB':
-            image = image.convert('RGB')
-        
-        # Resize
-        image = image.resize((224, 224))
-        
-        # Convert to array
-        img_array = np.array(image)
-        
-        # Add batch dimension
-        img_array = np.expand_dims(img_array, axis=0)
-        
-        # Preprocess for EfficientNet
-        img_array = preprocess_input(img_array)
-        
-        return img_array
-    except Exception as e:
-        st.error(f"Error preprocessing image: {str(e)}")
-        return None
-
-def get_top_predictions(predictions, class_labels, top_n=3):
-    """Get top N predictions"""
-    top_indices = np.argsort(predictions)[-top_n:][::-1]
-    return [(class_labels[i], predictions[i] * 100) for i in top_indices]
-
-def create_confidence_chart(predictions, class_labels):
-    """Create confidence bar chart"""
-    df = pd.DataFrame({
-        'Class': class_labels,
-        'Confidence': predictions * 100
-    }).sort_values('Confidence', ascending=True)
-    
-    fig = px.bar(
-        df,
-        x='Confidence',
-        y='Class',
-        orientation='h',
-        title='Classification Confidence Scores',
-        color='Confidence',
-        color_continuous_scale='Greens',
-        text=df['Confidence'].round(1).astype(str) + '%'
-    )
-    
-    fig.update_layout(
-        xaxis_title='Confidence (%)',
-        yaxis_title='Waste Class',
-        height=400,
-        showlegend=False,
-        plot_bgcolor='rgba(0,0,0,0)',
-        paper_bgcolor='rgba(0,0,0,0)',
-        font=dict(size=12)
-    )
-    
-    fig.update_traces(textposition='outside')
-    
-    return fig
-
-def create_donut_chart(confidence, class_name):
-    """Create donut chart for main prediction"""
-    fig = go.Figure(data=[go.Pie(
-        values=[confidence, 100-confidence],
-        labels=[class_name, 'Other'],
-        hole=.7,
-        marker_colors=['#4CAF50', '#E0E0E0'],
-        textinfo='none'
-    )])
-    
-    fig.update_layout(
-        annotations=[dict(text=f'{confidence:.1f}%', x=0.5, y=0.5, font_size=20, showarrow=False)],
-        height=200,
-        showlegend=False,
-        margin=dict(t=0, b=0, l=0, r=0)
-    )
-    
-    return fig
-
-# ==================== PAGE 1: HOME & CLASSIFICATION ====================
-if page == "🏠 Home & Classification":
-    # Create two columns for layout
-    col1, col2 = st.columns([1, 1])
-    
-    with col1:
-        st.markdown('<div class="card">', unsafe_allow_html=True)
-        st.markdown("### 📤 Upload Waste Image")
-        
-        # File uploader with camera option
-        uploaded_file = st.file_uploader(
-            "Choose an image...",
-            type=['jpg', 'jpeg', 'png', 'bmp', 'webp'],
-            help="Upload a clear image of the waste item"
-        )
-        
-        # Camera input option
-        camera_image = st.camera_input("Or take a photo")
-        
-        # Use camera image if available, otherwise use uploaded file
-        if camera_image is not None:
-            image = Image.open(camera_image)
-            st.success("✅ Photo captured successfully!")
-        elif uploaded_file is not None:
-            image = Image.open(uploaded_file)
-            st.success("✅ Image uploaded successfully!")
-        else:
-            image = None
-            st.info("👆 Please upload an image or take a photo to begin")
-        
-        st.markdown('</div>', unsafe_allow_html=True)
-        
-        # Display uploaded image
-        if image is not None:
-            st.markdown('<div class="card">', unsafe_allow_html=True)
-            st.markdown("### 🖼️ Preview")
-            
-            # Create columns for image display
-            img_col1, img_col2, img_col3 = st.columns([1, 2, 1])
-            with img_col2:
-                st.image(image, caption="Uploaded Image", use_column_width=True)
-            
-            # Classify button
-            if st.button("🔍 Classify Waste", use_container_width=True):
-                with st.spinner("🔄 Analyzing image..."):
-                    time.sleep(1)  # Simulate processing
-                    
-                    # Make prediction
-                    predictions = predict_image(model, image)
-                    
-                    if predictions is not None:
-                        # Store predictions in session state
-                        st.session_state['predictions'] = predictions
-                        st.session_state['image'] = image
-                        st.session_state['classified'] = True
-                        
-                        st.success("✅ Classification complete!")
-                        st.rerun()
-                    else:
-                        st.error("❌ Classification failed. Please try again.")
-            
-            st.markdown('</div>', unsafe_allow_html=True)
-    
-    with col2:
-        # Display results if classified
-        if 'classified' in st.session_state and st.session_state['classified']:
-            predictions = st.session_state['predictions']
-            image = st.session_state['image']
-            
-            # Main prediction
-            pred_class_idx = np.argmax(predictions)
-            pred_class = class_labels[pred_class_idx]
-            confidence = predictions[pred_class_idx] * 100
-            
-            # Get top 3 predictions
-            top_3 = get_top_predictions(predictions, class_labels)
-            
-            st.markdown('<div class="card">', unsafe_allow_html=True)
-            st.markdown("### 🎯 Classification Results")
-            
-            # Main result with styling
-            result_col1, result_col2 = st.columns([1, 1])
-            
-            with result_col1:
-                # Donut chart for main prediction
-                fig_donut = create_donut_chart(confidence, pred_class)
-                st.plotly_chart(fig_donut, use_container_width=True)
-            
-            with result_col2:
-                # Main prediction details
-                st.markdown(f"### **Predicted Class:**")
-                class_color = class_colors.get(pred_class, '#E0E0E0')
-                st.markdown(f"<div style='background-color: {class_color}; padding: 15px; border-radius: 10px; text-align: center;'>"
-                          f"<h2>{recycling_info[pred_class]['icon']} {pred_class.upper()}</h2>"
-                          f"<h3>Confidence: {confidence:.2f}%</h3>"
-                          f"</div>", unsafe_allow_html=True)
-            
-            st.markdown("---")
-            
-            # Top 3 predictions
-            st.markdown("### 📊 Top 3 Predictions")
-            for i, (class_name, conf) in enumerate(top_3):
-                st.markdown(f"""
-                <div style='margin: 5px 0; padding: 10px; background-color: #f8f9fa; border-radius: 8px;'>
-                    <strong>{i+1}. {recycling_info[class_name]['icon']} {class_name.capitalize()}</strong>
-                    <div style='margin-top: 5px;'>
-                        <div style='background-color: #e0e0e0; border-radius: 10px; height: 10px; width: 100%;'>
-                            <div style='background-color: #4CAF50; border-radius: 10px; height: 10px; width: {conf}%;'></div>
-                        </div>
-                        <span style='float: right;'>{conf:.1f}%</span>
-                    </div>
-                </div>
-                """, unsafe_allow_html=True)
-            
-            st.markdown('</div>', unsafe_allow_html=True)
-            
-            # Recycling Information
-            st.markdown('<div class="card">', unsafe_allow_html=True)
-            st.markdown(f"### {recycling_info[pred_class]['icon']} Recycling Information")
-            
-            info = recycling_info[pred_class]
-            
-            # Info boxes
-            col_a, col_b, col_c = st.columns(3)
-            with col_a:
-                st.markdown(f"""
-                <div class="info-box">
-                    <strong>♻️ Recyclability</strong><br>
-                    {info['recyclable']}
-                </div>
-                """, unsafe_allow_html=True)
-            
-            with col_b:
-                st.markdown(f"""
-                <div class="info-box">
-                    <strong>🗑️ Bin Color</strong><br>
-                    {info['bin_color']}
-                </div>
-                """, unsafe_allow_html=True)
-            
-            with col_c:
-                st.markdown(f"""
-                <div class="info-box">
-                    <strong>🔧 Processing</strong><br>
-                    {info['processing'][:50]}...
-                </div>
-                """, unsafe_allow_html=True)
-            
-            # Facts and tips
-            st.markdown("---")
-            st.markdown(f"**📌 Did you know?** {info['facts']}")
-            st.markdown(f"**💡 Tip:** {info['tips']}")
-            
-            st.markdown('</div>', unsafe_allow_html=True)
-            
-            # Confidence chart
-            st.markdown('<div class="card">', unsafe_allow_html=True)
-            fig = create_confidence_chart(predictions, class_labels)
-            st.plotly_chart(fig, use_container_width=True)
-            st.markdown('</div>', unsafe_allow_html=True)
-            
-            # Environmental impact
-            st.markdown('<div class="card">', unsafe_allow_html=True)
-            st.markdown("### 🌍 Environmental Impact")
-            
-            impact_data = {
-                'cardboard': {'trees': 0.5, 'co2': 2.0, 'water': 100},
-                'glass': {'trees': 0, 'co2': 3.5, 'water': 50},
-                'metal': {'trees': 0, 'co2': 4.0, 'water': 75},
-                'paper': {'trees': 1.0, 'co2': 2.5, 'water': 150},
-                'plastic': {'trees': 0, 'co2': 1.5, 'water': 25},
-                'trash': {'trees': 0, 'co2': 0.5, 'water': 10}
-            }
-            
-            impact = impact_data[pred_class]
-            
-            col_imp1, col_imp2, col_imp3 = st.columns(3)
-            with col_imp1:
-                st.metric("Trees Saved", f"{impact['trees']} per kg" if impact['trees'] > 0 else "N/A")
-            with col_imp2:
-                st.metric("CO₂ Reduced", f"{impact['co2']} kg per kg")
-            with col_imp3:
-                st.metric("Water Saved", f"{impact['water']} L per kg")
-            
-            st.markdown('</div>', unsafe_allow_html=True)
-            
-            # Download report
-            st.markdown('<div class="card">', unsafe_allow_html=True)
-            st.markdown("### 📥 Download Analysis Report")
-            
-            report_content = f"""
-            RECYCLEVISION - WASTE CLASSIFICATION REPORT
-            ============================================
-            
-            ANALYSIS DATE: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
-            
-            CLASSIFICATION RESULTS:
-            - Primary Class: {pred_class.upper()}
-            - Confidence: {confidence:.2f}%
-            
-            TOP 3 PREDICTIONS:
-            1. {top_3[0][0].upper()}: {top_3[0][1]:.2f}%
-            2. {top_3[1][0].upper()}: {top_3[1][1]:.2f}%
-            3. {top_3[2][0].upper()}: {top_3[2][1]:.2f}%
-            
-            RECYCLING INFORMATION:
-            - Recyclability: {info['recyclable']}
-            - Bin Color: {info['bin_color']}
-            - Processing: {info['processing']}
-            
-            ENVIRONMENTAL IMPACT:
-            - CO₂ Reduction: {impact['co2']} kg per kg
-            - Water Saved: {impact['water']} L per kg
-            - Trees Saved: {impact['trees']} per kg
-            
-            EDUCATIONAL TIP:
-            {info['tips']}
-            
-            INTERESTING FACT:
-            {info['facts']}
-            
-            -- Generated by RecycleVision AI System --
-            """
-            
-            st.download_button(
-                label="📄 Download Report (TXT)",
-                data=report_content,
-                file_name=f"recyclevision_report_{pred_class}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt",
-                mime="text/plain",
-                use_container_width=True
-            )
-            st.markdown('</div>', unsafe_allow_html=True)
-            
-        else:
-            # Welcome message when no image classified
-            st.markdown('<div class="card">', unsafe_allow_html=True)
-            st.markdown("### 👋 Welcome to RecycleVision!")
-            st.markdown("""
-            **Get started by:**
-            1. 📤 Uploading an image of your waste item
-            2. 📸 Taking a photo using your camera
-            3. 🔍 Clicking "Classify Waste" button
-            
-            **Supported waste types:**
-            - 📦 Cardboard
-            - 🥃 Glass  
-            - 🔩 Metal
-            - 📄 Paper
-            - 🧴 Plastic
-            - 🗑️ Trash
-            """)
-            st.markdown('</div>', unsafe_allow_html=True)
-            
-            # Sample classifications
-            st.markdown('<div class="card">', unsafe_allow_html=True)
-            st.markdown("### 🎯 Sample Classifications")
-            
-            sample_col1, sample_col2, sample_col3 = st.columns(3)
-            with sample_col1:
-                st.markdown("""
-                <div style='text-align: center; padding: 15px; background-color: #f0f0f0; border-radius: 10px;'>
-                    <h3>📦</h3>
-                    <p>Cardboard</p>
-                    <small>98% confidence</small>
-                </div>
-                """, unsafe_allow_html=True)
-            
-            with sample_col2:
-                st.markdown("""
-                <div style='text-align: center; padding: 15px; background-color: #f0f0f0; border-radius: 10px;'>
-                    <h3>🥃</h3>
-                    <p>Glass</p>
-                    <small>95% confidence</small>
-                </div>
-                """, unsafe_allow_html=True)
-            
-            with sample_col3:
-                st.markdown("""
-                <div style='text-align: center; padding: 15px; background-color: #f0f0f0; border-radius: 10px;'>
-                    <h3>🧴</h3>
-                    <p>Plastic</p>
-                    <small>92% confidence</small>
-                </div>
-                """, unsafe_allow_html=True)
-            
-            st.markdown('</div>', unsafe_allow_html=True)
-            
-            # Quick stats
-            st.markdown('<div class="card">', unsafe_allow_html=True)
-            st.markdown("### 📊 Quick Stats")
-            
-            stat_col1, stat_col2, stat_col3, stat_col4 = st.columns(4)
-            with stat_col1:
-                st.metric("Model Accuracy", "84%", "+2.5%")
-            with stat_col2:
-                st.metric("Classes", "6", "")
-            with stat_col3:
-                st.metric("Training Images", "2,027", "")
-            with stat_col4:
-                st.metric("Inference Time", "<1s", "Fast")
-            
-            st.markdown('</div>', unsafe_allow_html=True)
-
-# ==================== PAGE 2: MODEL PERFORMANCE ====================
-elif page == "📈 Model Performance":
-    st.markdown('<div class="card">', unsafe_allow_html=True)
-    st.markdown("### 📈 Model Performance Metrics")
-    
-    # Model metrics
-    col1, col2, col3, col4 = st.columns(4)
-    with col1:
-        st.metric("Accuracy", "83.2%", "↑ 2.1%")
-    with col2:
-        st.metric("Precision", "84.0%", "↑ 1.8%")
-    with col3:
-        st.metric("Recall", "83.0%", "↑ 1.5%")
-    with col4:
-        st.metric("F1-Score", "83.0%", "↑ 1.6%")
-    
-    st.markdown('</div>', unsafe_allow_html=True)
-    
-    # Confusion Matrix
-    st.markdown('<div class="card">', unsafe_allow_html=True)
-    st.markdown("### 🔍 Confusion Matrix")
-    
-    # Confusion matrix data from validation
-    cm_data = np.array([
-        [63, 0, 2, 9, 0, 7],
-        [0, 86, 9, 0, 4, 1],
-        [0, 7, 70, 1, 4, 1],
-        [2, 1, 2, 111, 1, 2],
-        [1, 2, 4, 4, 68, 17],
-        [0, 2, 1, 2, 2, 20]
-    ])
-    
-    fig_cm = px.imshow(
-        cm_data,
-        x=class_labels,
-        y=class_labels,
-        text_auto=True,
-        aspect="auto",
-        color_continuous_scale='Blues',
-        title="Confusion Matrix - Validation Set"
-    )
-    
-    fig_cm.update_layout(
-        xaxis_title="Predicted Label",
-        yaxis_title="True Label",
-        height=500
-    )
-    
-    st.plotly_chart(fig_cm, use_container_width=True)
-    st.markdown('</div>', unsafe_allow_html=True)
-    
-    # Class-wise Performance
-    st.markdown('<div class="card">', unsafe_allow_html=True)
-    st.markdown("### 📊 Class-wise Performance")
-    
-    performance_data = pd.DataFrame({
-        'Class': class_labels,
-        'Precision': [0.97, 0.80, 0.80, 0.87, 0.87, 0.50],
-        'Recall': [0.78, 0.86, 0.85, 0.93, 0.71, 0.74],
-        'F1-Score': [0.86, 0.83, 0.82, 0.90, 0.78, 0.60],
-        'Support': [81, 100, 82, 119, 96, 27]
-    })
-    
-    fig_perf = px.bar(
-        performance_data.melt(id_vars=['Class'], value_vars=['Precision', 'Recall', 'F1-Score']),
-        x='Class',
-        y='value',
-        color='variable',
-        barmode='group',
-        title="Class-wise Performance Metrics",
-        labels={'value': 'Score', 'variable': 'Metric'}
-    )
-    
-    st.plotly_chart(fig_perf, use_container_width=True)
-    st.markdown('</div>', unsafe_allow_html=True)
-    
-    # Training History
-    st.markdown('<div class="card">', unsafe_allow_html=True)
-    st.markdown("### 📈 Training History")
-    
-    # Simulated training history
-    epochs = list(range(1, 13))
-    train_acc = [0.47, 0.76, 0.78, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.87, 0.88]
-    val_acc = [0.76, 0.77, 0.80, 0.80, 0.82, 0.84, 0.82, 0.83, 0.83, 0.84, 0.83, 0.84]
-    train_loss = [1.86, 0.72, 0.70, 0.56, 0.55, 0.47, 0.46, 0.45, 0.38, 0.35, 0.36, 0.33]
-    val_loss = [0.70, 0.62, 0.53, 0.51, 0.51, 0.48, 0.50, 0.56, 0.55, 0.51, 0.49, 0.50]
-    
-    history_df = pd.DataFrame({
-        'Epoch': epochs,
-        'Training Accuracy': train_acc,
-        'Validation Accuracy': val_acc,
-        'Training Loss': train_loss,
-        'Validation Loss': val_loss
-    })
-    
-    fig_history = make_subplots(
-        rows=1, cols=2,
-        subplot_titles=('Model Accuracy', 'Model Loss')
-    )
-    
-    fig_history.add_trace(
-        go.Scatter(x=epochs, y=train_acc, mode='lines+markers', name='Training Accuracy', line=dict(color='#4CAF50')),
-        row=1, col=1
-    )
-    
-    fig_history.add_trace(
-        go.Scatter(x=epochs, y=val_acc, mode='lines+markers', name='Validation Accuracy', line=dict(color='#2196F3')),
-        row=1, col=1
-    )
-    
-    fig_history.add_trace(
-        go.Scatter(x=epochs, y=train_loss, mode='lines+markers', name='Training Loss', line=dict(color='#FF5722')),
-        row=1, col=2
-    )
-    
-    fig_history.add_trace(
-        go.Scatter(x=epochs, y=val_loss, mode='lines+markers', name='Validation Loss', line=dict(color='#9C27B0')),
-        row=1, col=2
-    )
-    
-    fig_history.update_layout(height=400, showlegend=True)
-    fig_history.update_xaxes(title_text="Epoch", row=1, col=1)
-    fig_history.update_xaxes(title_text="Epoch", row=1, col=2)
-    fig_history.update_yaxes(title_text="Accuracy", row=1, col=1)
-    fig_history.update_yaxes(title_text="Loss", row=1, col=2)
-    
-    st.plotly_chart(fig_history, use_container_width=True)
-    st.markdown('</div>', unsafe_allow_html=True)
-
-# ==================== PAGE 3: ABOUT & INFO ====================
-elif page == "ℹ️ About & Info":
-    st.markdown('<div class="card">', unsafe_allow_html=True)
-    st.markdown("### ℹ️ About RecycleVision")
-    
-    st.markdown("""
-    **RecycleVision** is an advanced deep learning system designed to automatically classify 
-    waste materials into six categories, helping streamline recycling processes and promoting 
-    environmental sustainability.
-    
-    #### 🎯 Project Objectives
-    - Automate waste sorting for recycling facilities
-    - Reduce manual sorting time and labor costs
-    - Educate people about proper waste segregation
-    - Provide environmental impact insights
-    
-    #### 🤖 Technology Stack
-    - **Deep Learning Framework:** TensorFlow 2.15 / Keras
-    - **Model Architecture:** EfficientNetB0 (Transfer Learning)
-    - **Frontend:** Streamlit 1.28
-    - **Data Processing:** NumPy, Pandas, OpenCV
-    - **Visualization:** Plotly, Matplotlib, Seaborn
-    
-    #### 📊 Dataset
-    - **Source:** TrashNet / Garbage Classification Dataset
-    - **Total Images:** 2,532
-    - **Classes:** 6 waste categories
-    - **Split:** 80% Training (2,027) | 20% Validation (505)
-    
-    #### 🏆 Model Performance
-    - **Accuracy:** 83.2%
-    - **Precision:** 84.0%
-    - **Recall:** 83.0%
-    - **F1-Score:** 83.0%
-    """)
-    
-    st.markdown('</div>', unsafe_allow_html=True)
-    
-    # Class Information
-    st.markdown('<div class="card">', unsafe_allow_html=True)
-    st.markdown("### 🗑️ Waste Classes Information")
-    
-    for class_name in class_labels:
-        info = recycling_info[class_name]
-        with st.expander(f"{info['icon']} {class_name.capitalize()} - {info['type']}"):
-            col_a, col_b = st.columns(2)
-            with col_a:
-                st.markdown(f"**♻️ Recyclable:** {info['recyclable']}")
-                st.markdown(f"**🗑️ Bin Color:** {info['bin_color']}")
-                st.markdown(f"**🔧 Processing:** {info['processing']}")
-            with col_b:
-                st.markdown(f"**📌 Fact:** {info['facts']}")
-                st.markdown(f"**💡 Tip:** {info['tips']}")
-    
-    st.markdown('</div>', unsafe_allow_html=True)
-    
-    # Environmental Impact
-    st.markdown('<div class="card">', unsafe_allow_html=True)
-    st.markdown("### 🌍 Environmental Impact")
-    
-    impact_data = {
-        'Category': ['CO₂ Reduction (kg/kg)', 'Energy Savings (%)', 'Water Saved (L/kg)', 'Trees Saved (per kg)'],
-        'Cardboard': [2.0, 75, 100, 0.5],
-        'Glass': [3.5, 60, 50, 0],
-        'Metal': [4.0, 95, 75, 0],
-        'Paper': [2.5, 70, 150, 1.0],
-        'Plastic': [1.5, 80, 25, 0],
-        'Trash': [0.5, 0, 10, 0]
-    }
-    
-    impact_df = pd.DataFrame(impact_data)
-    st.dataframe(impact_df.style.highlight_max(color='lightgreen'), use_container_width=True)
-    
-    st.markdown('</div>', unsafe_allow_html=True)
-    
-    # Team Info
-    st.markdown('<div class="card">', unsafe_allow_html=True)
-    st.markdown("### 👥 Project Team")
-    
-    col1, col2, col3 = st.columns(3)
-    with col1:
-        st.markdown("""
-        <div style='text-align: center; padding: 20px;'>
-            <h3>🧑‍💻 Data Scientist</h3>
-            <p>Model Development & Training</p>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    with col2:
-        st.markdown("""
-        <div style='text-align: center; padding: 20px;'>
-            <h3>🎨 UI/UX Designer</h3>
-            <p>Streamlit App Development</p>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    with col3:
-        st.markdown("""
-        <div style='text-align: center; padding: 20px;'>
-            <h3>🔬 Domain Expert</h3>
-            <p>Waste Management Specialist</p>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    st.markdown('</div>', unsafe_allow_html=True)
-
-# ==================== PAGE 4: SETTINGS ====================
-elif page == "⚙️ Settings":
-    st.markdown('<div class="card">', unsafe_allow_html=True)
-    st.markdown("### ⚙️ Application Settings")
-    
-    # Model settings
-    st.markdown("#### 🤖 Model Configuration")
-    
-    conf_threshold = st.slider(
-        "Confidence Threshold (%)",
-        min_value=0,
-        max_value=100,
-        value=50,
-        help="Minimum confidence for classification"
-    )
-    
-    top_k = st.slider(
-        "Top-K Predictions",
-        min_value=1,
-        max_value=6,
-        value=3,
-        help="Number of top predictions to show"
-    )
-    
-    # Display settings
-    st.markdown("#### 🎨 Display Settings")
-    
-    theme = st.selectbox(
-        "Theme",
-        ["Light", "Dark", "System Default"],
-        help="Choose application theme"
-    )
-    
-    chart_style = st.selectbox(
-        "Chart Style",
-        ["Modern", "Classic", "Minimalist"],
-        help="Choose chart visualization style"
-    )
-    
-    # Notification settings
-    st.markdown("#### 🔔 Notifications")
-    
-    show_tips = st.checkbox("Show recycling tips", value=True)
-    show_facts = st.checkbox("Show environmental facts", value=True)
-    show_impact = st.checkbox("Show impact metrics", value=True)
-    
-    # Advanced settings
-    st.markdown("#### 🔧 Advanced Settings")
-    
-    batch_size = st.number_input("Batch Size", min_value=1, max_value=32, value=1)
-    use_gpu = st.checkbox("Use GPU if available", value=True)
-    
-    # Save settings button
-    if st.button("💾 Save Settings", use_container_width=True):
-        st.success("✅ Settings saved successfully!")
-    
-    st.markdown('</div>', unsafe_allow_html=True)
-    
-    # About the model
-    st.markdown('<div class="card">', unsafe_allow_html=True)
-    st.markdown("### 📊 Model Details")
-    
-    st.markdown("""
-    **Model Architecture:** EfficientNetB0
-    - **Input Shape:** 224 × 224 × 3
-    - **Total Parameters:** 4,384,169
-    - **Trainable Parameters:** 332,038
-    - **Non-trainable Parameters:** 4,052,131
-    
-    **Training Configuration:**
-    - **Optimizer:** Adam (lr=0.001)
-    - **Loss Function:** Categorical Crossentropy
-    - **Batch Size:** 32
-    - **Epochs:** 25 (with early stopping)
-    - **Class Weights:** Balanced
-    
-    **Data Augmentation:**
-    - Rotation Range: 25°
-    - Zoom Range: 20%
-    - Width/Height Shift: 20%
-    - Horizontal Flip: Yes
-    """)
-    
-    st.markdown('</div>', unsafe_allow_html=True)
-
-# Footer
-st.markdown("""
-<div class="footer">
-    <p style='font-size: 1.2rem; margin-bottom: 10px;'>♻️ RecycleVision - Making Waste Management Smarter</p>
-    <p style='font-size: 0.9rem; opacity: 0.9;'>
-        © 2024 | Deep Learning Project | EfficientNetB0 | 84% Accuracy<br>
-        <small>Every classification helps build a cleaner tomorrow</small>
-    </p>
-</div>
-""", unsafe_allow_html=True)
-
-# Sidebar footer
-st.sidebar.markdown("---")
-st.sidebar.markdown("""
-<div style='text-align: center; padding: 10px; background-color: #f0f0f0; border-radius: 10px;'>
-    <p style='margin: 0;'><strong>♻️ RecycleVision v2.0</strong></p>
-    <p style='margin: 0; font-size: 0.8rem;'>AI-Powered Waste Classification</p>
-</div>
-""", unsafe_allow_html=True)
-
-# Initialize session state
-if 'classified' not in st.session_state:
-    st.session_state['classified'] = False