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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

requirements.txt

@@ -0,0 +1,10 @@
+streamlit==1.28.0
+tensorflow==2.20.0
+numpy==1.26.4
+pandas==2.2.3
+Pillow==10.4.0
+opencv-python-headless==4.10.0.84
+matplotlib==3.10.1
+seaborn==0.13.2
+plotly==5.24.1
+scikit-learn==1.6.1