import streamlit as st
import numpy as np
import os
import sys
from PIL import Image

# Set environment variables to fix permission issues
os.environ['MPLCONFIGDIR'] = '/tmp/matplotlib'
os.environ['STREAMLIT_SERVER_HEADLESS'] = 'true'

# Minimal imports to avoid conflicts
try:
    import tensorflow as tf
    TF_AVAILABLE = True
except ImportError:
    TF_AVAILABLE = False
    st.error("TensorFlow not available")

try:
    import matplotlib
    matplotlib.use('Agg')  # Use non-interactive backend
    import matplotlib.pyplot as plt
    import matplotlib.cm as cm
    MPL_AVAILABLE = True
except ImportError:
    MPL_AVAILABLE = False

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

# Simple styling
st.markdown("""
<style>
    .main-header {
        font-size: 2.5rem;
        color: #1f77b4;
        text-align: center;
        margin-bottom: 2rem;
    }
    .prediction-box {
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        color: white;
        padding: 2rem;
        border-radius: 1rem;
        text-align: center;
        margin: 1rem 0;
    }
    .status-box {
        padding: 1rem;
        border-radius: 0.5rem;
        margin: 1rem 0;
    }
    .success { background-color: #d4edda; border: 1px solid #c3e6cb; color: #155724; }
    .error { background-color: #f8d7da; border: 1px solid #f5c6cb; color: #721c24; }
    .info { background-color: #d1ecf1; border: 1px solid #bee5eb; color: #0c5460; }
    .warning { background-color: #fff3cd; border: 1px solid #ffeaa7; color: #856404; }
    .debug { background-color: #f8f9fa; border: 1px solid #dee2e6; color: #495057; font-family: monospace; }
</style>""", unsafe_allow_html=True)

# Initialize session state
if 'model_loaded' not in st.session_state:
    st.session_state.model_loaded = False
    st.session_state.model = None
    st.session_state.model_status = "Not loaded"

STROKE_LABELS = ["Hemorrhagic Stroke", "Ischemic Stroke", "No Stroke"]

def find_model_file():
    """Find the model file in various possible locations."""
    possible_paths = [
        "stroke_classification_model.h5",
        "./stroke_classification_model.h5",
        "/app/stroke_classification_model.h5",
        "src/stroke_classification_model.h5",
        os.path.join(os.getcwd(), "stroke_classification_model.h5")
    ]
    
    # Also check all .h5 files in current directory and subdirectories
    for root, dirs, files in os.walk('.'):
        for file in files:
            if file.endswith('.h5'):
                possible_paths.append(os.path.join(root, file))
    
    for path in possible_paths:
        if os.path.exists(path):
            return path
    
    return None

@st.cache_resource
def load_stroke_model():
    """Load model with caching."""
    if not TF_AVAILABLE:
        return None, "❌ TensorFlow not available"
    
    try:
        # Find the model file
        model_path = find_model_file()
        
        if model_path is None:
            # List all files to help debug
            current_files = []
            for root, dirs, files in os.walk('.'):
                for file in files:
                    current_files.append(os.path.join(root, file))
            
            return None, f"❌ Model file not found. Available files: {current_files[:10]}"
        
        st.info(f"Found model at: {model_path}")
        
        # Load model with minimal custom objects
        model = tf.keras.models.load_model(model_path, compile=False)
        
        return model, f"✅ Model loaded successfully from: {model_path}"
    
    except Exception as e:
        return None, f"❌ Model loading failed: {str(e)}"

def analyze_model_architecture(model):
    """Comprehensive analysis of model architecture."""
    if model is None:
        return {"error": "No model loaded"}
    
    layer_analysis = {
        'total_layers': len(model.layers),
        'conv_layers': [],
        'dense_layers': [],
        'other_layers': [],
        'all_layers_detailed': [],
        'model_type': 'Unknown'
    }
    
    for i, layer in enumerate(model.layers):
        layer_type = type(layer).__name__
        
        # Get more detailed layer information
        layer_info = {
            'index': i,
            'name': layer.name,
            'type': layer_type,
            'output_shape': getattr(layer, 'output_shape', 'Unknown'),
            'trainable': getattr(layer, 'trainable', 'Unknown'),
            'activation': getattr(layer, 'activation', None)
        }
        
        # Try to get activation function name
        if hasattr(layer, 'activation') and layer.activation:
            try:
                layer_info['activation'] = layer.activation.__name__
            except:
                layer_info['activation'] = str(layer.activation)
        
        layer_analysis['all_layers_detailed'].append(layer_info)
        
        # Categorize layers with more comprehensive detection
        if any(conv_type in layer_type for conv_type in [
            'Conv1D', 'Conv2D', 'Conv3D', 'SeparableConv2D', 'DepthwiseConv2D',
            'Convolution1D', 'Convolution2D', 'Convolution3D'
        ]) or 'conv' in layer.name.lower():
            layer_analysis['conv_layers'].append(layer_info)
        
        elif 'Dense' in layer_type or 'Linear' in layer_type:
            layer_analysis['dense_layers'].append(layer_info)
        
        else:
            layer_analysis['other_layers'].append(layer_info)
    
    # Determine model type
    if layer_analysis['conv_layers']:
        layer_analysis['model_type'] = 'CNN (Convolutional Neural Network)'
    elif layer_analysis['dense_layers']:
        layer_analysis['model_type'] = 'MLP (Multi-Layer Perceptron)'
    else:
        layer_analysis['model_type'] = 'Custom Architecture'
    
    return layer_analysis

def debug_gradcam_step_by_step(img_array, model, layer_name, pred_index):
    """Debug Grad-CAM computation step by step."""
    debug_info = {
        'step': 'Starting',
        'error': None,
        'layer_output_shape': None,
        'gradients_shape': None,
        'gradients_stats': None,
        'heatmap_stats': None
    }
    
    try:
        debug_info['step'] = 'Getting target layer'
        target_layer = model.get_layer(layer_name)
        debug_info['target_layer_type'] = type(target_layer).__name__
        
        debug_info['step'] = 'Creating grad model'
        grad_model = tf.keras.Model(
            inputs=[model.inputs],
            outputs=[target_layer.output, model.output]
        )
        
        debug_info['step'] = 'Computing forward pass'
        with tf.GradientTape() as tape:
            layer_output, preds = grad_model(img_array)
            debug_info['layer_output_shape'] = layer_output.shape.as_list()
            debug_info['predictions_shape'] = preds.shape.as_list()
            
            if pred_index is None:
                pred_index = tf.argmax(preds[0])
            debug_info['pred_index'] = int(pred_index)
            debug_info['pred_confidence'] = float(preds[0][pred_index])
            
            class_channel = preds[:, pred_index]
            debug_info['class_channel_shape'] = class_channel.shape.as_list()
        
        debug_info['step'] = 'Computing gradients'
        grads = tape.gradient(class_channel, layer_output)
        
        if grads is None:
            debug_info['error'] = "Gradients are None - no backpropagation path"
            return None, debug_info
        
        debug_info['gradients_shape'] = grads.shape.as_list()
        debug_info['gradients_stats'] = {
            'min': float(tf.reduce_min(grads)),
            'max': float(tf.reduce_max(grads)),
            'mean': float(tf.reduce_mean(grads)),
            'std': float(tf.math.reduce_std(grads))
        }
        
        debug_info['step'] = 'Processing gradients based on layer type'
        
        if len(layer_output.shape) == 4:  # Conv layer
            debug_info['processing_type'] = 'Convolutional layer (4D)'
            pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
            layer_output = layer_output[0]
            heatmap = layer_output @ pooled_grads[..., tf.newaxis]
            heatmap = tf.squeeze(heatmap)
            
        elif len(layer_output.shape) == 2:  # Dense layer
            debug_info['processing_type'] = 'Dense layer (2D)'
            # For dense layers, create spatial heatmap from gradient magnitude
            grads_magnitude = tf.reduce_mean(tf.abs(grads))
            # Create a simple spatial pattern
            heatmap = tf.ones((14, 14)) * grads_magnitude
            
        else:
            debug_info['error'] = f"Unsupported layer shape: {layer_output.shape}"
            return None, debug_info
        
        debug_info['step'] = 'Normalizing heatmap'
        debug_info['raw_heatmap_stats'] = {
            'min': float(tf.reduce_min(heatmap)),
            'max': float(tf.reduce_max(heatmap)),
            'mean': float(tf.reduce_mean(heatmap)),
            'std': float(tf.math.reduce_std(heatmap))
        }
        
        # Apply ReLU (remove negative values)
        heatmap = tf.maximum(heatmap, 0)
        
        # Normalize
        heatmap_max = tf.reduce_max(heatmap)
        if heatmap_max > 0:
            heatmap = heatmap / heatmap_max
        else:
            debug_info['error'] = "All heatmap values are zero or negative"
            return None, debug_info
        
        debug_info['final_heatmap_stats'] = {
            'min': float(tf.reduce_min(heatmap)),
            'max': float(tf.reduce_max(heatmap)),
            'mean': float(tf.reduce_mean(heatmap)),
            'std': float(tf.math.reduce_std(heatmap))
        }
        
        debug_info['step'] = 'Complete'
        return heatmap.numpy(), debug_info
        
    except Exception as e:
        debug_info['error'] = f"Exception in step '{debug_info['step']}': {str(e)}"
        return None, debug_info

def create_robust_gradcam_heatmap(img, model, predictions):
    """Create Grad-CAM with comprehensive debugging."""
    try:
        # Preprocess image
        img_resized = img.resize((224, 224))
        img_array = np.array(img_resized, dtype=np.float32)
        
        # Handle grayscale
        if len(img_array.shape) == 2:
            img_array = np.stack([img_array] * 3, axis=-1)
        
        # Normalize and add batch dimension
        img_array = np.expand_dims(img_array, axis=0) / 255.0
        
        # Get model analysis
        analysis = analyze_model_architecture(model)
        
        # Try different layers in order of preference
        layer_candidates = []
        
        # Add conv layers first
        for layer in analysis['conv_layers']:
            layer_candidates.append((layer['name'], f"Conv layer: {layer['name']}"))
        
        # Add other potentially suitable layers
        for layer in analysis['all_layers_detailed']:
            if (layer['type'] in ['Activation', 'BatchNormalization'] and 
                isinstance(layer['output_shape'], (list, tuple)) and 
                len(layer['output_shape']) == 4):
                layer_candidates.append((layer['name'], f"4D layer: {layer['name']} ({layer['type']})"))
        
        # Try dense layers as last resort
        if not layer_candidates:
            for layer in analysis['dense_layers']:
                layer_candidates.append((layer['name'], f"Dense layer: {layer['name']} (experimental)"))
        
        if not layer_candidates:
            return None, "❌ No suitable layers found", None
        
        # Try each candidate layer
        for layer_name, layer_desc in layer_candidates:
            pred_index = np.argmax(predictions)
            
            heatmap, debug_info = debug_gradcam_step_by_step(
                img_array, model, layer_name, pred_index
            )
            
            if heatmap is not None:
                # Resize heatmap to match input image size
                if heatmap.shape[0] != 224 or heatmap.shape[1] != 224:
                    heatmap_resized = tf.image.resize(
                        heatmap[..., tf.newaxis], 
                        (224, 224)
                    ).numpy()[:, :, 0]
                else:
                    heatmap_resized = heatmap
                
                # Final statistics
                stats = {
                    'min': float(np.min(heatmap_resized)),
                    'max': float(np.max(heatmap_resized)),
                    'mean': float(np.mean(heatmap_resized)),
                    'std': float(np.std(heatmap_resized))
                }
                
                return heatmap_resized, f"✅ Grad-CAM successful using {layer_desc}", stats, debug_info
            else:
                # Continue to next layer if this one failed
                continue
        
        # If all layers failed, return debug info from the last attempt
        return None, f"❌ All layers failed. Last error: {debug_info.get('error', 'Unknown')}", None, debug_info
        
    except Exception as e:
        return None, f"❌ Grad-CAM error: {str(e)}", None, {'error': str(e)}

def predict_stroke(img, model):
    """Predict stroke type from image."""
    if model is None:
        return None, "Model not loaded"
    
    try:
        # Preprocess image
        img_resized = img.resize((224, 224))
        img_array = np.array(img_resized, dtype=np.float32)
        
        # Handle grayscale
        if len(img_array.shape) == 2:
            img_array = np.stack([img_array] * 3, axis=-1)
        
        # Normalize and add batch dimension
        img_array = np.expand_dims(img_array, axis=0) / 255.0
        
        # Predict
        predictions = model.predict(img_array, verbose=0)
        
        return predictions[0], None
    
    except Exception as e:
        return None, f"Prediction error: {str(e)}"

def create_enhanced_simulated_heatmap(img, predictions):
    """Create a more realistic simulated heatmap."""
    try:
        confidence = np.max(predictions)
        predicted_class = np.argmax(predictions)
        
        # Create different patterns based on predicted class
        if predicted_class == 0:  # Hemorrhagic
            # Focus on center-left region
            center_x, center_y = 80, 112
        elif predicted_class == 1:  # Ischemic
            # Focus on right side
            center_x, center_y = 150, 112
        else:  # No stroke
            # Diffuse, low-intensity pattern
            center_x, center_y = 112, 112
        
        # Create base pattern
        y, x = np.ogrid[:224, :224]
        
        # Primary focus area
        mask1 = np.exp(-((x - center_x)**2 + (y - center_y)**2) / (2 * (40**2)))
        
        # Secondary areas
        mask2 = np.exp(-((x - center_x + 30)**2 + (y - center_y + 20)**2) / (2 * (25**2)))
        mask3 = np.exp(-((x - center_x - 20)**2 + (y - center_y - 30)**2) / (2 * (30**2)))
        
        # Combine patterns
        heatmap = (mask1 * 0.8 + mask2 * 0.4 + mask3 * 0.3) * confidence
        
        # Add some noise for realism
        np.random.seed(42)
        noise = np.random.normal(0, 0.05, heatmap.shape)
        heatmap = np.maximum(heatmap + noise, 0)
        
        # Normalize
        if np.max(heatmap) > 0:
            heatmap = heatmap / np.max(heatmap)
        
        stats = {
            'min': float(np.min(heatmap)),
            'max': float(np.max(heatmap)),
            'mean': float(np.mean(heatmap)),
            'std': float(np.std(heatmap))
        }
        
        return heatmap, "⚠️ Using enhanced simulated heatmap", stats
    except Exception as e:
        return None, f"❌ Simulated heatmap error: {str(e)}", None

def create_comprehensive_visualization(img, predictions, model, force_gradcam=True, colormap='hot'):
    """Create comprehensive visualization with debugging."""
    if not MPL_AVAILABLE:
        return None, "❌ Matplotlib not available"
    
    try:
        # Resize image to 224x224
        img_resized = img.resize((224, 224))
        img_array = np.array(img_resized)
        
        heatmap = None
        status_message = ""
        stats = None
        debug_info = None
        
        # Try Grad-CAM first
        if force_gradcam and model is not None:
            result = create_robust_gradcam_heatmap(img, model, predictions)
            if result and len(result) >= 3:
                heatmap, gradcam_status, stats = result[0], result[1], result[2]
                if len(result) > 3:
                    debug_info = result[3]
                status_message = gradcam_status
        
        # Fallback to enhanced simulated if Grad-CAM failed
        if heatmap is None:
            result = create_enhanced_simulated_heatmap(img, predictions)
            if result and len(result) == 3:
                heatmap, sim_status, stats = result
                if status_message:
                    status_message += f" | {sim_status}"
                else:
                    status_message = sim_status
        
        if heatmap is None:
            return None, "❌ Could not generate any heatmap", None, None
        
        # Create visualization
        fig, axes = plt.subplots(1, 3, figsize=(15, 5))
        
        # 1. Original image
        axes[0].imshow(img_array)
        axes[0].set_title("Original Image", fontsize=12, fontweight='bold')
        axes[0].axis('off')
        
        # 2. Heatmap only
        im1 = axes[1].imshow(heatmap, cmap=colormap, vmin=0, vmax=1)
        axes[1].set_title(f"Attention Heatmap ({colormap})", fontsize=12, fontweight='bold')
        axes[1].axis('off')
        plt.colorbar(im1, ax=axes[1], fraction=0.046, pad=0.04)
        
        # 3. Overlay
        axes[2].imshow(img_array)
        im2 = axes[2].imshow(heatmap, cmap=colormap, alpha=0.6, vmin=0, vmax=1, interpolation='bilinear')
        
        # Determine title based on success
        if "✅ Grad-CAM successful" in status_message:
            title = "🎯 Real AI Attention Overlay"
            title_color = 'green'
        else:
            title = "🎨 Simulated Attention Overlay"
            title_color = 'orange'
        
        axes[2].set_title(title, fontsize=12, fontweight='bold', color=title_color)
        axes[2].axis('off')
        plt.colorbar(im2, ax=axes[2], fraction=0.046, pad=0.04)
        
        plt.tight_layout()
        
        return fig, status_message, stats, debug_info
    
    except Exception as e:
        return None, f"❌ Visualization error: {str(e)}", None, None

# Main App
def main():
    # Header
    st.markdown('<h1 class="main-header">🧠 AI-Powered Stroke Classification System</h1>', unsafe_allow_html=True)
    
    # Auto-load model on startup
    if not st.session_state.model_loaded:
        with st.spinner("Loading AI model..."):
            st.session_state.model, st.session_state.model_status = load_stroke_model()
            st.session_state.model_loaded = True

    # System status
    st.markdown("### 🔧 System Status")
    col1, col2, col3 = st.columns(3)
    
    with col1:
        if TF_AVAILABLE:
            st.markdown('<div class="status-box success">✅ TensorFlow Ready</div>', unsafe_allow_html=True)
            st.write(f"TF Version: {tf.__version__}")
        else:
            st.markdown('<div class="status-box error">❌ TensorFlow Error</div>', unsafe_allow_html=True)

    with col2:
        if MPL_AVAILABLE:
            st.markdown('<div class="status-box success">✅ Matplotlib Ready</div>', unsafe_allow_html=True)
        else:
            st.markdown('<div class="status-box error">❌ Matplotlib Error</div>', unsafe_allow_html=True)

    with col3:
        if "✅" in st.session_state.model_status:
            st.markdown('<div class="status-box success">✅ Model Loaded</div>', unsafe_allow_html=True)
        else:
            st.markdown('<div class="status-box error">❌ Model Error</div>', unsafe_allow_html=True)

    # Model status details
    st.markdown(f'<div class="status-box info"><strong>Model Status:</strong> {st.session_state.model_status}</div>', unsafe_allow_html=True)

    # Enhanced model architecture analysis
    if st.session_state.model is not None:
        with st.expander("🔍 Detailed Model Architecture Analysis"):
            analysis = analyze_model_architecture(st.session_state.model)
            
            st.write("**📊 Model Summary:**")
            st.write(f"- **Model Type:** {analysis['model_type']}")
            st.write(f"- **Total Layers:** {analysis['total_layers']}")
            st.write(f"- **Convolutional Layers:** {len(analysis['conv_layers'])}")
            st.write(f"- **Dense Layers:** {len(analysis['dense_layers'])}")
            st.write(f"- **Other Layers:** {len(analysis['other_layers'])}")
            
            # Show detailed layer information
            st.write("**🔍 All Layers (Detailed):**")
            for layer in analysis['all_layers_detailed']:
                activation_info = f" | Activation: {layer['activation']}" if layer['activation'] else ""
                st.code(f"{layer['index']:2d}: {layer['name']} ({layer['type']}) | Shape: {layer['output_shape']}{activation_info}")

    # Manual reload button
    if st.button("🔄 Reload Model", help="Try to reload the model"):
        st.session_state.model_loaded = False
        st.rerun()

    # Sidebar
    with st.sidebar:
        st.header("📤 Upload Brain Scan")
        uploaded_file = st.file_uploader(
            "Choose a brain scan image...",
            type=['png', 'jpg', 'jpeg', 'bmp', 'tiff'],
            help="Upload a brain scan image for stroke classification"
        )
        
        st.markdown("---")
        st.header("🎨 Visualization Options")
        
        force_gradcam = st.checkbox(
            "Attempt Grad-CAM", 
            value=True,
            help="Try Grad-CAM with comprehensive debugging"
        )
        
        colormap = st.selectbox(
            "Color Scheme",
            ['hot', 'jet', 'viridis', 'plasma', 'inferno', 'magma', 'coolwarm'],
            index=0,
            help="Choose color scheme for heatmap visualization"
        )
        
        show_probabilities = st.checkbox("Show All Probabilities", value=True)
        show_debug = st.checkbox("Show Debug Info", value=True)
        show_stats = st.checkbox("Show Heatmap Statistics", value=True)
        show_detailed_debug = st.checkbox("Show Detailed Debug Info", value=False)

    if uploaded_file is not None:
        # Load image
        image = Image.open(uploaded_file)
        
        # Main content area
        col1, col2 = st.columns([1, 2])
        
        with col1:
            st.subheader("📋 Classification Results")
            
            if st.session_state.model is not None:
                # Predict
                with st.spinner("🔍 Analyzing brain scan..."):
                    predictions, error = predict_stroke(image, st.session_state.model)
                
                if error:
                    st.error(error)
                else:
                    # Get top prediction
                    class_idx = np.argmax(predictions)
                    confidence = predictions[class_idx] * 100
                    predicted_class = STROKE_LABELS[class_idx]
                    
                    # Display main result
                    st.markdown(f"""
                    <div class="prediction-box">
                        <h2>{predicted_class}</h2>
                        <h3>Confidence: {confidence:.1f}%</h3>
                    </div>
                    """, unsafe_allow_html=True)
                    
                    # Show all probabilities
                    if show_probabilities:
                        st.write("**📊 All Probabilities:**")
                        for i, (label, prob) in enumerate(zip(STROKE_LABELS, predictions)):
                            st.write(f"• {label}: {prob*100:.1f}%")
            else:
                st.error("❌ Model not loaded. Check the debug information above to see available files.")

        with col2:
            st.subheader("🎯 Comprehensive AI Attention Visualization")
            
            if st.session_state.model is not None and 'predictions' in locals() and predictions is not None:
                # Create comprehensive visualization
                with st.spinner("🎨 Generating comprehensive attention visualization..."):
                    result = create_comprehensive_visualization(
                        image, 
                        predictions, 
                        st.session_state.model, 
                        force_gradcam,
                        colormap
                    )
                
                if result and len(result) >= 2:
                    overlay_fig, status_message = result[0], result[1]
                    stats = result[2] if len(result) > 2 else None
                    debug_info = result[3] if len(result) > 3 else None
                    
                    if overlay_fig is not None:
                        st.pyplot(overlay_fig)
                        plt.close()
                        
                        # Show detailed status
                        if show_debug:
                            if "✅ Grad-CAM successful" in status_message:
                                st.success(f"✅ {status_message}")
                            elif "⚠️" in status_message:
                                st.warning(f"⚠️ {status_message}")
                            else:
                                st.error(f"❌ {status_message}")
                        
                        # Show heatmap statistics
                        if show_stats and stats:
                            st.write("**📈 Heatmap Statistics:**")
                            if any(np.isnan([stats['min'], stats['max'], stats['mean'], stats['std']])):
                                st.error("⚠️ NaN values detected in heatmap - this indicates a computation error")
                            else:
                                col_stats1, col_stats2 = st.columns(2)
                                with col_stats1:
                                    st.write(f"• Min: {stats['min']:.3f}")
                                    st.write(f"• Max: {stats['max']:.3f}")
                                with col_stats2:
                                    st.write(f"• Mean: {stats['mean']:.3f}")
                                    st.write(f"• Std: {stats['std']:.3f}")
                        
                        # Show detailed debug information
                        if show_detailed_debug and debug_info:
                            with st.expander("🔧 Detailed Debug Information"):
                                st.json(debug_info)
                    else:
                        st.error(f"Could not generate visualization: {status_message}")
                        if debug_info:
                            st.error(f"Debug info: {debug_info.get('error', 'No additional info')}")
                else:
                    st.error("Could not generate attention visualization")
            else:
                st.info("Upload an image and run classification to see AI attention visualization")
    
    else:
        # Welcome message
        st.markdown("""
        ## 👋 Welcome to the Comprehensive Stroke Classification System
        
        This system now includes **step-by-step debugging** to identify why Grad-CAM might be failing.
        
        ### 🔧 New Debugging Features:
        - **Step-by-step Grad-CAM debugging** - See exactly where it fails
        - **Multiple layer attempts** - Tries different layers automatically
        - **Enhanced error messages** - Clear explanations of what went wrong
        - **NaN detection** - Identifies computation errors
        
        ### 🎯 What to Look For:
        - **Green success messages** - Grad-CAM is working
        - **Orange warnings** - Using fallback methods
        - **Red errors** - Something is broken
        - **NaN statistics** - Computation failure
        
        **Upload an image to see detailed debugging! 👈**
        """)

    # Medical disclaimer
    st.markdown("---")
    st.warning("⚠️ **Medical Disclaimer:** This AI system is for educational and research purposes only. It should not be used for actual medical diagnosis. Always consult qualified healthcare professionals for medical decisions.")

if __name__ == "__main__":
    main()