ultra_minimal_deep_learning_demo.py

"""
Ultra Minimal Deep Learning Parkinson's Detection Demo
Only uses Streamlit + NumPy - no other dependencies!
"""

import streamlit as st
import numpy as np

# Page configuration
st.set_page_config(
    page_title="Ultra Minimal Deep Learning Parkinson's Detection",
    page_icon="🧠",
    layout="wide"
)

# Custom CSS
st.markdown("""
<style>
    .main-header {
        font-size: 3rem;
        color: #1f77b4;
        text-align: center;
        margin-bottom: 2rem;
    }
    .prediction-box {
        background-color: #ffffff;
        padding: 1.5rem;
        border-radius: 0.5rem;
        border-left: 5px solid #1f77b4;
        margin: 1rem 0;
        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
    }
    .prediction-box h3 {
        color: #2c3e50 !important;
        font-size: 1.5rem;
        margin-bottom: 1rem;
    }
    .prediction-box p {
        color: #2c3e50 !important;
        font-size: 1.1rem;
        margin-bottom: 0.5rem;
    }
    .confidence-high { color: #27ae60 !important; font-weight: bold; }
    .confidence-medium { color: #f39c12 !important; font-weight: bold; }
    .confidence-low { color: #e74c3c !important; font-weight: bold; }
    .neural-network {
        background-color: #f8f9fa;
        padding: 1rem;
        border-radius: 0.5rem;
        margin: 1rem 0;
        border: 3px solid #1f77b4;
        box-shadow: 0 4px 8px rgba(0,0,0,0.1);
    }
    .layer {
        background-color: #ffffff;
        padding: 1rem;
        margin: 0.5rem 0;
        border-radius: 0.5rem;
        text-align: center;
        font-weight: bold;
        color: #2c3e50 !important;
        font-size: 1.1rem;
        border: 2px solid #1f77b4;
        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
    }
    .layer:hover {
        background-color: #e3f2fd;
        transform: translateY(-2px);
        box-shadow: 0 4px 8px rgba(0,0,0,0.15);
    }
</style>
""", unsafe_allow_html=True)

class NeuralNetwork:
    """Pure Neural Network implementation using only NumPy"""
    
    def __init__(self, input_size, hidden_sizes, output_size):
        self.input_size = input_size
        self.hidden_sizes = hidden_sizes
        self.output_size = output_size
        
        # Initialize weights and biases
        self.weights = []
        self.biases = []
        
        # Input to first hidden layer
        prev_size = input_size
        for hidden_size in hidden_sizes:
            self.weights.append(np.random.randn(prev_size, hidden_size) * 0.1)
            self.biases.append(np.zeros((1, hidden_size)))
            prev_size = hidden_size
        
        # Last hidden to output layer
        self.weights.append(np.random.randn(prev_size, output_size) * 0.1)
        self.biases.append(np.zeros((1, output_size)))
    
    def sigmoid(self, x):
        """Sigmoid activation function"""
        return 1 / (1 + np.exp(-np.clip(x, -500, 500)))
    
    def sigmoid_derivative(self, x):
        """Derivative of sigmoid function"""
        s = self.sigmoid(x)
        return s * (1 - s)
    
    def forward(self, X):
        """Forward propagation"""
        self.activations = [X]
        self.z_values = []
        
        current_input = X
        
        for i in range(len(self.weights)):
            z = np.dot(current_input, self.weights[i]) + self.biases[i]
            self.z_values.append(z)
            
            if i == len(self.weights) - 1:  # Output layer
                current_input = self.sigmoid(z)
            else:  # Hidden layers
                current_input = self.sigmoid(z)
            
            self.activations.append(current_input)
        
        return current_input
    
    def predict(self, X):
        """Make predictions"""
        return self.forward(X)
    
    def train(self, X, y, epochs=1000, learning_rate=0.1):
        """Train the neural network"""
        losses = []
        for epoch in range(epochs):
            # Forward pass
            output = self.forward(X)
            
            # Compute loss (mean squared error)
            loss = np.mean((output - y) ** 2)
            losses.append(loss)
            
            # Backward pass (simplified gradient descent)
            error = output - y
            
            # Update weights (simplified version)
            for i in reversed(range(len(self.weights))):
                if i == len(self.weights) - 1:  # Output layer
                    gradient = error
                else:
                    gradient = np.dot(gradient, self.weights[i + 1].T) * self.sigmoid_derivative(self.z_values[i])
                
                self.weights[i] -= learning_rate * np.dot(self.activations[i].T, gradient) / X.shape[0]
                self.biases[i] -= learning_rate * np.mean(gradient, axis=0, keepdims=True)
            
            if epoch % 100 == 0 and epoch > 0:
                st.write(f"Epoch {epoch}, Loss: {loss:.4f}")
        
        return losses

class HandwritingAnalyzer:
    """Simple handwriting feature extractor using only NumPy"""
    
    def create_sample_handwriting_matrix(self, is_parkinson=False, size=(10, 10)):
        """Create sample handwriting as a simple matrix"""
        matrix = np.ones(size, dtype=float)
        center_x, center_y = size[0] // 2, size[1] // 2
        
        if is_parkinson:
            # Parkinson's characteristics: more irregular
            for i in range(0, 360, 20):
                radius = i * 0.1
                # Add more randomness for Parkinson's effect
                noise_x = np.random.normal(0, 0.5)
                noise_y = np.random.normal(0, 0.5)
                
                x = int(center_x + radius * np.cos(np.radians(i)) + noise_x)
                y = int(center_y + radius * np.sin(np.radians(i)) + noise_y)
                
                if 0 <= x < size[0] and 0 <= y < size[1]:
                    matrix[y, x] = 0.0  # Dark pixel
        else:
            # Healthy characteristics: smooth, regular
            for i in range(0, 360, 15):
                radius = i * 0.1
                x = int(center_x + radius * np.cos(np.radians(i)))
                y = int(center_y + radius * np.sin(np.radians(i)))
                
                if 0 <= x < size[0] and 0 <= y < size[1]:
                    matrix[y, x] = 0.0  # Dark pixel
        
        return matrix
    
    def extract_features(self, matrix):
        """Extract features from handwriting matrix"""
        features = []
        
        # Basic statistics
        features.extend([
            np.mean(matrix),
            np.std(matrix),
            np.var(matrix),
            np.min(matrix),
            np.max(matrix)
        ])
        
        # Edge detection (simple difference)
        if matrix.shape[0] > 1 and matrix.shape[1] > 1:
            diff_h = np.abs(np.diff(matrix, axis=1))
            diff_v = np.abs(np.diff(matrix, axis=0))
            
            features.extend([
                np.mean(diff_h),
                np.mean(diff_v),
                np.sum(diff_h > 0.5),  # Edge count
                np.sum(diff_v > 0.5)
            ])
        else:
            features.extend([0, 0, 0, 0])
        
        return np.array(features)

@st.cache_data
def generate_sample_data():
    """Generate sample voice and handwriting data"""
    np.random.seed(42)
    
    # Generate sample voice features (22 features)
    n_samples = 100
    
    # Healthy samples (class 0)
    healthy_samples = []
    for _ in range(50):
        features = np.random.normal([120, 140, 90, 0.01, 0.005, 0.005, 0.015, 0.05, 0.02, 0.02, 0.03, 0.04, 0.02, 20, 0.4, 0.8, -5.0, 0.2, 2.0, 0.1, 0.1, 0.1], 
                                  [10, 15, 10, 0.005, 0.002, 0.002, 0.005, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 5, 0.1, 0.1, 1.0, 0.1, 0.5, 0.05, 0.05, 0.05])
        healthy_samples.append(features)
    
    # Parkinson's samples (class 1)
    parkinson_samples = []
    for _ in range(50):
        features = np.random.normal([100, 120, 80, 0.02, 0.01, 0.01, 0.025, 0.08, 0.04, 0.04, 0.05, 0.06, 0.04, 15, 0.5, 0.9, -6.0, 0.3, 2.5, 0.15, 0.15, 0.15], 
                                  [10, 15, 10, 0.005, 0.002, 0.002, 0.005, 0.02, 0.01, 0.01, 0.01, 0.01, 0.01, 5, 0.1, 0.1, 1.0, 0.1, 0.5, 0.05, 0.05, 0.05])
        parkinson_samples.append(features)
    
    # Combine data
    X = np.vstack([healthy_samples, parkinson_samples])
    y = np.hstack([np.zeros(50), np.ones(50)]).reshape(-1, 1)
    
    return X, y

def display_matrix_as_text(matrix, title):
    """Display matrix as text representation"""
    st.markdown(f"**{title}**")
    st.text("Matrix representation:")
    
    # Convert to string representation
    matrix_str = ""
    for row in matrix:
        row_str = ""
        for val in row:
            if val < 0.5:
                row_str += "█"  # Dark pixel
            else:
                row_str += "░"  # Light pixel
        matrix_str += row_str + "\n"
    
    st.text(matrix_str)

def main():
    """Main Streamlit application"""
    
    # Header
    st.markdown('<h1 class="main-header">🧠 Ultra Minimal Deep Learning Parkinson\'s Detection</h1>', unsafe_allow_html=True)
    st.markdown('<p style="text-align: center; font-size: 1.2rem; color: #7f8c8d;">Pure Neural Networks using ONLY Streamlit + NumPy</p>', unsafe_allow_html=True)
    
    # Sidebar
    st.sidebar.title("🔧 Deep Learning Models")
    
    analysis_type = st.sidebar.selectbox(
        "Choose Analysis Type",
        ["Voice Neural Network", "Handwriting Analysis", "Multimodal Fusion", "Complete System Training"]
    )
    
    if analysis_type == "Voice Neural Network":
        st.markdown("## 🎤 Voice Analysis with Neural Network")
        
        # Load sample data
        with st.spinner("Generating voice data..."):
            X, y = generate_sample_data()
        
        st.success(f"✅ Generated {len(X)} voice samples")
        
        # Network architecture
        st.markdown("### 🧠 Neural Network Architecture")
        
        col1, col2, col3 = st.columns(3)
        with col1:
            hidden1 = st.slider("Hidden Layer 1", 10, 100, 50)
        with col2:
            hidden2 = st.slider("Hidden Layer 2", 5, 50, 20)
        with col3:
            epochs = st.slider("Training Epochs", 100, 1000, 300)
        
        # Display network architecture
        st.markdown("### 🏗️ Network Visualization")
        
        architecture_html = f"""
        <div class="neural-network">
            <h4 style="text-align: center; color: #1f77b4; margin-bottom: 1rem;">🧠 Neural Network Architecture</h4>
            <div class="layer">📥 Input Layer: 22 voice features</div>
            <div style="text-align: center; color: #666; margin: 0.5rem 0;">⬇️</div>
            <div class="layer">🔗 Hidden Layer 1: {hidden1} neurons (sigmoid activation)</div>
            <div style="text-align: center; color: #666; margin: 0.5rem 0;">⬇️</div>
            <div class="layer">🔗 Hidden Layer 2: {hidden2} neurons (sigmoid activation)</div>
            <div style="text-align: center; color: #666; margin: 0.5rem 0;">⬇️</div>
            <div class="layer">📤 Output Layer: 1 neuron (sigmoid activation)</div>
            <div style="text-align: center; color: #666; margin: 0.5rem 0;">⬇️</div>
            <div style="text-align: center; color: #e74c3c; font-weight: bold; padding: 0.5rem; background-color: #ffeaea; border-radius: 0.25rem; margin-top: 0.5rem;">
                🎯 Parkinson's Probability (0-1)
            </div>
        </div>
        """
        st.markdown(architecture_html, unsafe_allow_html=True)
        
        if st.button("🚀 Train Neural Network", type="primary"):
            with st.spinner("Training neural network..."):
                # Create network
                nn = NeuralNetwork(
                    input_size=22,
                    hidden_sizes=[hidden1, hidden2],
                    output_size=1
                )
                
                # Train network
                losses = nn.train(X, y, epochs=epochs, learning_rate=0.01)
                
                st.success("✅ Neural Network trained successfully!")
                
                # Display training loss as text
                st.markdown("### 📊 Training Loss Analysis")
                
                col1, col2, col3 = st.columns(3)
                final_loss = losses[-1]
                
                with col1:
                    st.metric("🎯 Final Loss", f"{final_loss:.4f}")
                with col2:
                    st.metric("🚀 Initial Loss", f"{losses[0]:.4f}")
                with col3:
                    reduction = ((losses[0] - final_loss) / losses[0] * 100)
                    st.metric("📈 Loss Reduction", f"{reduction:.1f}%")
                
                # Create a simple text-based loss visualization
                st.markdown("**📉 Training Loss Progression:**")
                loss_text = "```\n"
                loss_text += f"Epoch    Loss\n"
                loss_text += f"-----    ----\n"
                
                # Show key epochs
                key_epochs = [0, len(losses)//4, len(losses)//2, 3*len(losses)//4, len(losses)-1]
                for epoch in key_epochs:
                    if epoch < len(losses):
                        loss_text += f"{epoch:5d}    {losses[epoch]:.4f}\n"
                
                loss_text += "```"
                st.markdown(loss_text)
                
                # Show last 10 losses in a more readable format
                st.markdown("**📋 Last 10 Training Epochs:**")
                for i, loss in enumerate(losses[-10:]):
                    epoch_num = len(losses)-10+i
                    st.markdown(f"• **Epoch {epoch_num}:** Loss = {loss:.4f}")
                
                # Test on sample
                test_sample = X[0:1]
                prediction = nn.predict(test_sample)[0, 0]
                
                st.markdown("### 📊 Prediction Results")
                
                risk_level = prediction
                
                if risk_level > 0.7:
                    risk_color = "🔴"
                    risk_text = "High Risk"
                    confidence_class = "confidence-high"
                elif risk_level > 0.4:
                    risk_color = "🟡"
                    risk_text = "Moderate Risk"
                    confidence_class = "confidence-medium"
                else:
                    risk_color = "🟢"
                    risk_text = "Low Risk"
                    confidence_class = "confidence-low"
                
                st.markdown(f"""
                <div class="prediction-box">
                    <h3>{risk_color} Neural Network Prediction: {risk_text}</h3>
                    <p><strong>Parkinson's Probability:</strong> {risk_level:.1%}</p>
                    <p><strong>Healthy Probability:</strong> {1-risk_level:.1%}</p>
                    <p><strong>Model Confidence:</strong> <span class="{confidence_class}">{max(risk_level, 1-risk_level):.1%}</span></p>
                </div>
                """, unsafe_allow_html=True)
    
    elif analysis_type == "Handwriting Analysis":
        st.markdown("## ✍️ Handwriting Analysis with Neural Networks")
        
        # Create handwriting analyzer
        analyzer = HandwritingAnalyzer()
        
        st.markdown("### 🖼️ Sample Handwriting Matrices")
        
        col1, col2 = st.columns(2)
        
        with col1:
            st.markdown("**Healthy Sample**")
            healthy_matrix = analyzer.create_sample_handwriting_matrix(is_parkinson=False)
            display_matrix_as_text(healthy_matrix, "Healthy Handwriting")
            
            # Extract features
            healthy_features = analyzer.extract_features(healthy_matrix)
            
            st.markdown("**Extracted Features:**")
            st.write(f"- Mean intensity: {healthy_features[0]:.3f}")
            st.write(f"- Standard deviation: {healthy_features[1]:.3f}")
            st.write(f"- Edge count: {healthy_features[8]:.0f}")
        
        with col2:
            st.markdown("**Parkinson's Sample**")
            parkinson_matrix = analyzer.create_sample_handwriting_matrix(is_parkinson=True)
            display_matrix_as_text(parkinson_matrix, "Parkinson's Handwriting")
            
            # Extract features
            parkinson_features = analyzer.extract_features(parkinson_matrix)
            
            st.markdown("**Extracted Features:**")
            st.write(f"- Mean intensity: {parkinson_features[0]:.3f}")
            st.write(f"- Standard deviation: {parkinson_features[1]:.3f}")
            st.write(f"- Edge count: {parkinson_features[8]:.0f}")
        
        # Feature comparison
        st.markdown("### 📊 Feature Comparison")
        
        comparison_data = {
            'Feature': ['Mean Intensity', 'Std Deviation', 'Variance', 'Edge Count'],
            'Healthy': [healthy_features[0], healthy_features[1], healthy_features[2], healthy_features[8]],
            'Parkinson\'s': [parkinson_features[0], parkinson_features[1], parkinson_features[2], parkinson_features[8]]
        }
        
        # Display as table
        col1, col2, col3 = st.columns(3)
        with col1:
            st.markdown("**Feature**")
            for feature in comparison_data['Feature']:
                st.write(feature)
        
        with col2:
            st.markdown("**Healthy**")
            for value in comparison_data['Healthy']:
                st.write(f"{value:.3f}")
        
        with col3:
            st.markdown("**Parkinson's**")
            for value in comparison_data['Parkinson\'s']:
                st.write(f"{value:.3f}")
        
        # Simple classification
        st.markdown("### 🤖 Handwriting Classification")
        
        if st.button("🔍 Classify Handwriting", type="primary"):
            # Create training data
            healthy_samples = []
            parkinson_samples = []
            
            for _ in range(20):
                healthy_samples.append(analyzer.extract_features(analyzer.create_sample_handwriting_matrix(False)))
                parkinson_samples.append(analyzer.extract_features(analyzer.create_sample_handwriting_matrix(True)))
            
            X_hand = np.vstack([healthy_samples, parkinson_samples])
            y_hand = np.hstack([np.zeros(20), np.ones(20)]).reshape(-1, 1)
            
            # Train simple neural network for handwriting
            hand_nn = NeuralNetwork(
                input_size=len(healthy_features),
                hidden_sizes=[10, 5],
                output_size=1
            )
            
            with st.spinner("Training handwriting classifier..."):
                hand_nn.train(X_hand, y_hand, epochs=200, learning_rate=0.1)
            
            # Test on current samples
            healthy_pred = hand_nn.predict(healthy_features.reshape(1, -1))[0, 0]
            parkinson_pred = hand_nn.predict(parkinson_features.reshape(1, -1))[0, 0]
            
            col1, col2 = st.columns(2)
            
            with col1:
                st.metric("Healthy Sample Prediction", f"{healthy_pred:.1%}", "Parkinson's risk")
            with col2:
                st.metric("Parkinson's Sample Prediction", f"{parkinson_pred:.1%}", "Parkinson's risk")
    
    elif analysis_type == "Multimodal Fusion":
        st.markdown("## 🔄 Multimodal Fusion Analysis")
        
        st.markdown("### 🎯 Combining Voice + Handwriting Predictions")
        
        # Simulate multimodal predictions
        col1, col2, col3 = st.columns(3)
        
        with col1:
            st.markdown("**🎤 Voice Model**")
            voice_prob = st.slider("Voice Parkinson's Probability", 0.0, 1.0, 0.3)
            st.metric("Voice Risk", f"{voice_prob:.1%}")
        
        with col2:
            st.markdown("**✍️ Handwriting Model**")
            hand_prob = st.slider("Handwriting Parkinson's Probability", 0.0, 1.0, 0.4)
            st.metric("Handwriting Risk", f"{hand_prob:.1%}")
        
        with col3:
            st.markdown("**🔄 Fusion Model**")
            # Simple fusion: weighted average
            fusion_prob = 0.6 * voice_prob + 0.4 * hand_prob
            st.metric("Fused Risk", f"{fusion_prob:.1%}")
        
        # Fusion visualization using text
        st.markdown("### 📊 Fusion Visualization")
        
        # Create a simple text-based visualization
        fusion_text = f"""
        ```
        Voice Model:      {'█' * int(voice_prob * 20):20} {voice_prob:.1%}
        Handwriting Model: {'█' * int(hand_prob * 20):20} {hand_prob:.1%}
        Fusion Model:     {'█' * int(fusion_prob * 20):20} {fusion_prob:.1%}
        
        Threshold:        {'█' * 10:20} 50%
        ```
        """
        st.text(fusion_text)
        
        # Final assessment
        st.markdown("### 🎯 Final Assessment")
        
        if fusion_prob > 0.7:
            risk_color = "🔴"
            risk_text = "High Risk"
            recommendation = "Immediate clinical evaluation recommended"
        elif fusion_prob > 0.4:
            risk_color = "🟡"
            risk_text = "Moderate Risk"
            recommendation = "Clinical follow-up recommended"
        else:
            risk_color = "🟢"
            risk_text = "Low Risk"
            recommendation = "Continue regular monitoring"
        
        st.markdown(f"""
        <div class="prediction-box">
            <h3>{risk_color} Final Assessment: {risk_text}</h3>
            <p><strong>Fused Probability:</strong> {fusion_prob:.1%}</p>
            <p><strong>Recommendation:</strong> {recommendation}</p>
            <p><strong>Confidence:</strong> Based on multimodal analysis</p>
        </div>
        """, unsafe_allow_html=True)
    
    elif analysis_type == "Complete System Training":
        st.markdown("## 🎓 Complete Multimodal System Training")
        
        st.markdown("### 🏗️ Training Both Models Simultaneously")
        
        # Network architectures
        col1, col2 = st.columns(2)
        
        with col1:
            st.markdown("**Voice Network**")
            voice_layers = st.multiselect(
                "Select hidden layers for voice model:",
                [10, 20, 30, 50, 100],
                default=[50, 20]
            )
            
        with col2:
            st.markdown("**Handwriting Network**")
            hand_layers = st.multiselect(
                "Select hidden layers for handwriting model:",
                [5, 10, 15, 20],
                default=[10, 5]
            )
        
        if st.button("🚀 Train Complete Multimodal System", type="primary"):
            with st.spinner("Training complete multimodal system..."):
                # Load voice data
                X_voice, y_voice = generate_sample_data()
                
                # Train voice network
                st.write("Training voice neural network...")
                voice_nn = NeuralNetwork(
                    input_size=22,
                    hidden_sizes=voice_layers,
                    output_size=1
                )
                voice_losses = voice_nn.train(X_voice, y_voice, epochs=200, learning_rate=0.01)
                
                # Train handwriting network
                st.write("Training handwriting neural network...")
                analyzer = HandwritingAnalyzer()
                
                # Create handwriting training data
                hand_features = []
                hand_labels = []
                
                for _ in range(50):
                    # Healthy samples
                    healthy_matrix = analyzer.create_sample_handwriting_matrix(False)
                    healthy_feat = analyzer.extract_features(healthy_matrix)
                    hand_features.append(healthy_feat)
                    hand_labels.append(0)
                    
                    # Parkinson's samples
                    parkinson_matrix = analyzer.create_sample_handwriting_matrix(True)
                    parkinson_feat = analyzer.extract_features(parkinson_matrix)
                    hand_features.append(parkinson_feat)
                    hand_labels.append(1)
                
                X_hand = np.array(hand_features)
                y_hand = np.array(hand_labels).reshape(-1, 1)
                
                hand_nn = NeuralNetwork(
                    input_size=len(hand_features[0]),
                    hidden_sizes=hand_layers,
                    output_size=1
                )
                hand_losses = hand_nn.train(X_hand, y_hand, epochs=200, learning_rate=0.01)
                
                st.success("✅ Complete multimodal system trained!")
                
                # Display training results
                col1, col2 = st.columns(2)
                
                with col1:
                    st.markdown("**Voice Model Training:**")
                    st.metric("Final Loss", f"{voice_losses[-1]:.4f}")
                    st.metric("Initial Loss", f"{voice_losses[0]:.4f}")
                
                with col2:
                    st.markdown("**Handwriting Model Training:**")
                    st.metric("Final Loss", f"{hand_losses[-1]:.4f}")
                    st.metric("Initial Loss", f"{hand_losses[0]:.4f}")
                
                # Test the system
                st.markdown("### 🧪 System Testing")
                
                # Test on sample
                test_voice = X_voice[0:1]
                test_hand_matrix = analyzer.create_sample_handwriting_matrix(False)
                test_hand_feat = analyzer.extract_features(test_hand_matrix).reshape(1, -1)
                
                voice_pred = voice_nn.predict(test_voice)[0, 0]
                hand_pred = hand_nn.predict(test_hand_feat)[0, 0]
                
                # Fusion
                fusion_pred = 0.6 * voice_pred + 0.4 * hand_pred
                
                col1, col2, col3 = st.columns(3)
                
                with col1:
                    st.metric("Voice Model", f"{voice_pred:.1%}")
                with col2:
                    st.metric("Handwriting Model", f"{hand_pred:.1%}")
                with col3:
                    st.metric("Fusion Model", f"{fusion_pred:.1%}")
                
                st.markdown("### 🏆 Model Performance Summary")
                st.markdown(f"""
                - **Voice Network**: {len(voice_layers)} hidden layers, {22} → {voice_layers} → {1} neurons
                - **Handwriting Network**: {len(hand_layers)} hidden layers, {len(hand_features[0])} → {hand_layers} → {1} neurons
                - **Fusion Strategy**: Weighted average (60% voice, 40% handwriting)
                - **Total Parameters**: ~{sum(voice_layers) + len(voice_layers)*22 + sum(hand_layers) + len(hand_layers)*len(hand_features[0])} weights
                """)
    
    # Footer
    st.markdown("---")
    st.markdown("### 📝 Ultra Minimal Deep Learning Features")
    st.info("""
    **This demo showcases:**
    - 🧠 **Pure Neural Networks**: Custom implementation using ONLY NumPy
    - 🎤 **Voice Analysis**: 22-feature neural network with configurable architecture
    - ✍️ **Handwriting Analysis**: Matrix-based feature extraction and classification
    - 🔄 **Multimodal Fusion**: Combining predictions from multiple models
    - 🎓 **Interactive Training**: Real-time model training and architecture selection
    - 📊 **Text-based Visualization**: No external plotting libraries needed
    
    **Zero Dependencies**: Only Streamlit + NumPy - works with any Python environment!
    """)

if __name__ == "__main__":
    main()