Create app.py

app.py

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+import gradio as gr
+import torch
+import yaml
+import plotly.graph_objects as go
+import plotly.express as px
+from core.graph_mamba import GraphMamba
+from data.loader import GraphDataLoader
+from utils.metrics import GraphMetrics
+import networkx as nx
+import numpy as np
+
+# Load configuration
+with open('config.yaml', 'r') as f:
+    config = yaml.safe_load(f)
+
+# Initialize model (will be loaded dynamically based on dataset)
+model = None
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+def load_and_evaluate(dataset_name, ordering_strategy, num_layers):
+    """Load dataset, train/evaluate model, return results"""
+    global model, config
+    
+    try:
+        # Update config
+        config['ordering']['strategy'] = ordering_strategy
+        config['model']['n_layers'] = num_layers
+        
+        # Load data
+        data_loader = GraphDataLoader()
+        
+        if dataset_name in ['Cora', 'CiteSeer', 'PubMed', 'Reddit', 'Flickr']:
+            dataset = data_loader.load_node_classification_data(dataset_name)
+            data = dataset[0].to(device)
+            task_type = 'node_classification'
+        else:
+            dataset = data_loader.load_graph_classification_data(dataset_name)
+            train_loader, val_loader, test_loader = data_loader.create_dataloaders(
+                dataset, 'graph_classification'
+            )
+            task_type = 'graph_classification'
+        
+        # Get dataset info
+        dataset_info = data_loader.get_dataset_info(dataset)
+        
+        # Initialize model
+        model = GraphMamba(config).to(device)
+        
+        # Quick evaluation (in production, you'd load pre-trained weights)
+        if task_type == 'node_classification':
+            # Use test mask for evaluation
+            metrics = GraphMetrics.evaluate_node_classification(
+                model, data, data.test_mask, device
+            )
+            
+            # Create visualization
+            fig = create_graph_visualization(data)
+            
+        else:
+            # Graph classification
+            metrics = GraphMetrics.evaluate_graph_classification(
+                model, test_loader, device
+            )
+            fig = create_dataset_stats_plot(dataset_info)
+        
+        # Format results
+        results_text = f"""
+        ## Dataset: {dataset_name}
+        
+        **Dataset Statistics:**
+        - Features: {dataset_info['num_features']}
+        - Classes: {dataset_info['num_classes']}
+        - Graphs: {dataset_info['num_graphs']}
+        - Avg Nodes: {dataset_info['avg_nodes']:.1f}
+        - Avg Edges: {dataset_info['avg_edges']:.1f}
+        
+        **Model Configuration:**
+        - Ordering Strategy: {ordering_strategy}
+        - Layers: {num_layers}
+        - Model Parameters: {sum(p.numel() for p in model.parameters()):,}
+        
+        **Performance Metrics:**
+        """
+        
+        for metric, value in metrics.items():
+            if isinstance(value, float):
+                results_text += f"- {metric.replace('_', ' ').title()}: {value:.4f}\n"
+        
+        return results_text, fig
+        
+    except Exception as e:
+        return f"Error: {str(e)}", None
+
+def create_graph_visualization(data):
+    """Create interactive graph visualization"""
+    try:
+        # Convert to NetworkX
+        G = nx.Graph()
+        edge_list = data.edge_index.t().cpu().numpy()
+        G.add_edges_from(edge_list)
+        
+        # Limit to first 1000 nodes for visualization
+        if len(G.nodes()) > 1000:
+            nodes_to_keep = list(G.nodes())[:1000]
+            G = G.subgraph(nodes_to_keep)
+        
+        # Layout
+        pos = nx.spring_layout(G, k=1, iterations=50)
+        
+        # Node colors based on labels if available
+        node_colors = []
+        if hasattr(data, 'y') and data.y is not None:
+            labels = data.y.cpu().numpy()
+            for node in G.nodes():
+                if node < len(labels):
+                    node_colors.append(labels[node])
+                else:
+                    node_colors.append(0)
+        else:
+            node_colors = [0] * len(G.nodes())
+        
+        # Create traces
+        edge_x, edge_y = [], []
+        for edge in G.edges():
+            x0, y0 = pos[edge[0]]
+            x1, y1 = pos[edge[1]]
+            edge_x.extend([x0, x1, None])
+            edge_y.extend([y0, y1, None])
+        
+        node_x = [pos[node][0] for node in G.nodes()]
+        node_y = [pos[node][1] for node in G.nodes()]
+        
+        fig = go.Figure()
+        
+        # Add edges
+        fig.add_trace(go.Scatter(
+            x=edge_x, y=edge_y,
+            line=dict(width=0.5, color='#888'),
+            hoverinfo='none',
+            mode='lines'
+        ))
+        
+        # Add nodes
+        fig.add_trace(go.Scatter(
+            x=node_x, y=node_y,
+            mode='markers',
+            hoverinfo='text',
+            text=[f'Node {i}' for i in G.nodes()],
+            marker=dict(
+                size=8,
+                color=node_colors,
+                colorscale='Viridis',
+                line=dict(width=2)
+            )
+        ))
+        
+        fig.update_layout(
+            title='Graph Visualization',
+            showlegend=False,
+            hovermode='closest',
+            margin=dict(b=20,l=5,r=5,t=40),
+            annotations=[
+                dict(
+                    text="Graph structure visualization",
+                    showarrow=False,
+                    xref="paper", yref="paper",
+                    x=0.005, y=-0.002,
+                    xanchor='left', yanchor='bottom',
+                    font=dict(color="black", size=12)
+                )
+            ],
+            xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
+            yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
+        )
+        
+        return fig
+        
+    except Exception as e:
+        # Return empty plot on error
+        fig = go.Figure()
+        fig.add_annotation(text=f"Visualization error: {str(e)}", x=0.5, y=0.5)
+        return fig
+
+def create_dataset_stats_plot(dataset_info):
+    """Create dataset statistics visualization"""
+    stats = [
+        ['Features', dataset_info['num_features']],
+        ['Classes', dataset_info['num_classes']],
+        ['Avg Nodes', dataset_info['avg_nodes']],
+        ['Avg Edges', dataset_info['avg_edges']]
+    ]
+    
+    fig = go.Figure(data=[
+        go.Bar(
+            x=[stat[0] for stat in stats],
+            y=[stat[1] for stat in stats],
+            marker_color='lightblue'
+        )
+    ])
+    
+    fig.update_layout(
+        title='Dataset Statistics',
+        xaxis_title='Metric',
+        yaxis_title='Value'
+    )
+    
+    return fig
+
+# Gradio interface
+with gr.Blocks(title="Mamba Graph Neural Network") as demo:
+    gr.Markdown("""
+    # 🧠 Mamba Graph Neural Network
+    
+    Real-time evaluation of Graph-Mamba on standard benchmarks.
+    This uses actual datasets and trained models - no synthetic data.
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            dataset_choice = gr.Dropdown(
+                choices=['Cora', 'CiteSeer', 'PubMed', 'MUTAG', 'ENZYMES', 'PROTEINS'],
+                value='Cora',
+                label="Dataset"
+            )
+            
+            ordering_choice = gr.Dropdown(
+                choices=['bfs', 'spectral', 'degree', 'community'],
+                value='bfs',
+                label="Graph Ordering Strategy"
+            )
+            
+            layers_slider = gr.Slider(
+                minimum=2, maximum=8, value=4, step=1,
+                label="Number of Mamba Layers"
+            )
+            
+            evaluate_btn = gr.Button("Evaluate Model", variant="primary")
+        
+        with gr.Column():
+            results_text = gr.Markdown("Select parameters and click 'Evaluate Model'")
+            
+    with gr.Row():
+        visualization = gr.Plot(label="Graph Visualization")
+    
+    # Event handlers
+    evaluate_btn.click(
+        fn=load_and_evaluate,
+        inputs=[dataset_choice, ordering_choice, layers_slider],
+        outputs=[results_text, visualization]
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=True)