#!/usr/bin/env python3 """ EDEN AGI - INTERACTIVE VISUALIZATION DASHBOARD Real-time capability activation visualization """ import torch import torch.nn as nn import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation from matplotlib.patches import Rectangle import time device = torch.device('cuda') # Load agent class UnifiedEdenAgent(nn.Module): def __init__(self): super().__init__() self.perception = nn.Sequential( nn.Linear(100, 512), nn.ReLU(), nn.Dropout(0.3), nn.Linear(512, 256), nn.ReLU() ) self.cognitive_core = nn.Sequential( nn.Linear(256, 512), nn.ReLU(), nn.Dropout(0.3), nn.Linear(512, 256) ) self.meta_learning = nn.Linear(256, 64) self.reasoning = nn.Linear(256, 64) self.common_sense = nn.Linear(256, 64) self.theory_of_mind = nn.Linear(256, 64) self.goals = nn.Linear(256, 64) self.integration = nn.Sequential( nn.Linear(64 * 5, 256), nn.ReLU(), nn.Dropout(0.3), nn.Linear(256, 128), nn.ReLU() ) self.output = nn.Linear(128, 10) def forward(self, x): perceived = self.perception(x) cognitive = self.cognitive_core(perceived) meta = self.meta_learning(cognitive) reason = self.reasoning(cognitive) cs = self.common_sense(cognitive) tom = self.theory_of_mind(cognitive) goal = self.goals(cognitive) integrated = self.integration(torch.cat([meta, reason, cs, tom, goal], dim=1)) output = self.output(integrated) # Return activations for visualization return output, { 'meta_learning': meta.abs().mean().item(), 'reasoning': reason.abs().mean().item(), 'common_sense': cs.abs().mean().item(), 'theory_of_mind': tom.abs().mean().item(), 'goal_emergence': goal.abs().mean().item() } print("="*70) print("EDEN AGI - VISUALIZATION DASHBOARD") print("="*70) agent = UnifiedEdenAgent().to(device) checkpoint = torch.load('capabilities/unified_eden_working.pth', weights_only=False) agent.load_state_dict(checkpoint['model_state']) agent.eval() print("✅ Agent loaded\n") # Create visualization fig, axes = plt.subplots(2, 2, figsize=(15, 10)) fig.suptitle('Eden AGI - Real-Time Capability Activation', fontsize=16, fontweight='bold') # Capability names and colors capabilities = ['Meta-Learning', 'Reasoning', 'Common Sense', 'Theory of Mind', 'Goal Emergence'] colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#FFA07A', '#98D8C8'] # Initialize plots ax_activation = axes[0, 0] ax_history = axes[0, 1] ax_distribution = axes[1, 0] ax_performance = axes[1, 1] # Data storage activation_history = {cap: [] for cap in capabilities} task_history = [] max_history = 50 def create_test_input(task_type): """Create input for specific task""" x = torch.zeros(1, 100).to(device) if task_type == 0: # Meta-learning x[0, 0:10] = 1 x[0, 10:20] = torch.randn(10).to(device) elif task_type == 1: # Reasoning x[0, 20:30] = 1 x[0, 30:40] = torch.randn(10).to(device) elif task_type == 2: # Common sense x[0, 40:50] = 1 x[0, 50:60] = torch.randn(10).to(device) elif task_type == 3: # Theory of mind x[0, 60:70] = 1 x[0, 70:80] = torch.randn(10).to(device) else: # Goal emergence x[0, 80:90] = 1 x[0, 90:100] = torch.randn(10).to(device) return x def update_visualization(frame): """Update all plots""" # Random task task_type = np.random.randint(0, 5) x = create_test_input(task_type) # Get activations with torch.no_grad(): pred, activations = agent(x) # Update history for i, cap in enumerate(['meta_learning', 'reasoning', 'common_sense', 'theory_of_mind', 'goal_emergence']): activation_history[capabilities[i]].append(activations[cap]) if len(activation_history[capabilities[i]]) > max_history: activation_history[capabilities[i]].pop(0) task_history.append(task_type) if len(task_history) > max_history: task_history.pop(0) # Clear all axes for ax in axes.flat: ax.clear() # 1. Current Activation (Bar Chart) ax_activation.set_title('Current Capability Activation', fontweight='bold') current_vals = [activation_history[cap][-1] if activation_history[cap] else 0 for cap in capabilities] bars = ax_activation.bar(range(5), current_vals, color=colors, alpha=0.7) ax_activation.set_xticks(range(5)) ax_activation.set_xticklabels(capabilities, rotation=45, ha='right') ax_activation.set_ylabel('Activation Level') ax_activation.set_ylim(0, max(current_vals) * 1.2 if current_vals else 1) ax_activation.grid(axis='y', alpha=0.3) # Add value labels on bars for bar, val in zip(bars, current_vals): height = bar.get_height() ax_activation.text(bar.get_x() + bar.get_width()/2., height, f'{val:.3f}', ha='center', va='bottom', fontsize=9) # 2. Activation History (Line Plot) ax_history.set_title('Activation History (Last 50 Steps)', fontweight='bold') for i, cap in enumerate(capabilities): if activation_history[cap]: ax_history.plot(activation_history[cap], label=cap, color=colors[i], linewidth=2, alpha=0.8) ax_history.set_xlabel('Time Steps') ax_history.set_ylabel('Activation Level') ax_history.legend(loc='upper right', fontsize=8) ax_history.grid(alpha=0.3) # 3. Task Distribution (Pie Chart) ax_distribution.set_title('Task Distribution', fontweight='bold') if task_history: task_counts = [task_history.count(i) for i in range(5)] wedges, texts, autotexts = ax_distribution.pie( task_counts, labels=capabilities, colors=colors, autopct='%1.1f%%', startangle=90 ) for autotext in autotexts: autotext.set_color('white') autotext.set_fontweight('bold') # 4. Performance Metrics ax_performance.set_title('Real-Time Metrics', fontweight='bold') ax_performance.axis('off') # Calculate metrics avg_activations = [np.mean(activation_history[cap]) if activation_history[cap] else 0 for cap in capabilities] overall_avg = np.mean(avg_activations) if avg_activations else 0 metrics_text = f""" Overall Activation: {overall_avg:.3f} Average by Capability: • Meta-Learning: {avg_activations[0]:.3f} • Reasoning: {avg_activations[1]:.3f} • Common Sense: {avg_activations[2]:.3f} • Theory of Mind: {avg_activations[3]:.3f} • Goal Emergence: {avg_activations[4]:.3f} Tasks Processed: {len(task_history)} Status: ✅ OPERATIONAL """ ax_performance.text(0.1, 0.5, metrics_text, fontsize=11, verticalalignment='center', family='monospace', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.3)) print("Starting real-time visualization...") print("Close the window to stop.\n") # Animate ani = animation.FuncAnimation(fig, update_visualization, interval=500, cache_frame_data=False) plt.tight_layout() plt.show() print("\n✅ Visualization complete!")