Add Ethical AGI Drift interactive simulation demo

- Implement SR9 (Semantic Resonance 9D) vector space visualization
- Add DI2 (Drift Integrity Index) real-time calculation and plotting
- Include interactive ethical declaration input system
- Support customizable drift intensity and simulation parameters
- Generate automated drift alerts and threshold monitoring
- Create base64-encoded heatmaps and drift plots
- Features: ontological drift detection, AGI safety simulation

🤖 Generated with [Claude Code](https://claude.ai/code)

app.py

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+"""
+Ethical-AGI-Drift Hugging Face Spaces Demo
+Interactive simulation of AGI Ontological Drift using SR9/DI2 framework
+"""
+
+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+import io
+import base64
+from typing import List, Tuple, Dict
+import json
+
+# Mock simplified implementation for demo
+class SR9Vector:
+    """Simplified SR9 (Semantic Resonance 9D) vector for demo"""
+
+    DIMENSIONS = [
+        "Intention Clarity",
+        "Contextual Fidelity",
+        "Value Continuity",
+        "Decision Coherence",
+        "Action Alignment",
+        "Feedback Integration",
+        "Learning Stability",
+        "Output Consistency",
+        "Ethical Resonance"
+    ]
+
+    def __init__(self, values: List[float] = None):
+        if values is None:
+            # Initialize with ideal ethical state
+            self.values = np.array([0.9, 0.85, 0.95, 0.88, 0.92, 0.87, 0.9, 0.89, 0.93])
+        else:
+            self.values = np.array(values[:9])  # Ensure 9 dimensions
+
+    def to_dict(self):
+        return {dim: float(val) for dim, val in zip(self.DIMENSIONS, self.values)}
+
+def calculate_di2(sr9_current: SR9Vector, sr9_previous: SR9Vector, psi_offset: float = 0.1) -> float:
+    """Calculate Drift Integrity Index (DI2)"""
+    if sr9_previous is None:
+        return 0.0
+
+    # Calculate rate of change
+    delta = sr9_current.values - sr9_previous.values
+    magnitude = np.linalg.norm(delta)
+
+    # Non-linear psi_offset for early drift detection
+    nonlinear_factor = 1.0 + (psi_offset * magnitude)
+
+    # DI2 calculation with state-dependent weighting
+    di2 = magnitude * nonlinear_factor
+
+    return min(di2, 1.0)  # Cap at 1.0 for demo
+
+def simulate_drift_scenario(ethical_declarations: List[str], drift_intensity: float, steps: int = 50) -> Tuple[List[SR9Vector], List[float], List[str]]:
+    """Simulate AGI drift based on ethical declarations"""
+
+    # Initialize with ideal state
+    sr9_history = [SR9Vector()]
+    di2_history = [0.0]
+    alerts = []
+
+    # Simulate drift over time
+    for step in range(1, steps):
+        prev_sr9 = sr9_history[-1]
+
+        # Apply drift based on intensity and step
+        drift_factor = drift_intensity * (step / steps)
+
+        # Simulate different types of drift based on declarations
+        new_values = prev_sr9.values.copy()
+
+        if "privacy" in " ".join(ethical_declarations).lower():
+            # Privacy concerns affect contextual fidelity and decision coherence
+            new_values[1] -= drift_factor * 0.8  # Contextual Fidelity
+            new_values[3] -= drift_factor * 0.6  # Decision Coherence
+
+        if "fairness" in " ".join(ethical_declarations).lower():
+            # Fairness issues affect value continuity and ethical resonance
+            new_values[2] -= drift_factor * 0.7  # Value Continuity
+            new_values[8] -= drift_factor * 0.9  # Ethical Resonance
+
+        if "transparency" in " ".join(ethical_declarations).lower():
+            # Transparency problems affect intention clarity and output consistency
+            new_values[0] -= drift_factor * 0.8  # Intention Clarity
+            new_values[7] -= drift_factor * 0.5  # Output Consistency
+
+        # Add some noise for realism
+        noise = np.random.normal(0, 0.02, 9)
+        new_values += noise
+
+        # Ensure values stay in valid range [0, 1]
+        new_values = np.clip(new_values, 0.0, 1.0)
+
+        # Create new SR9 vector
+        current_sr9 = SR9Vector(new_values)
+        sr9_history.append(current_sr9)
+
+        # Calculate DI2
+        di2 = calculate_di2(current_sr9, prev_sr9)
+        di2_history.append(di2)
+
+        # Check for alerts
+        if di2 > 0.3:
+            alerts.append(f"Step {step}: High drift detected (DI2: {di2:.3f})")
+        elif di2 > 0.2:
+            alerts.append(f"Step {step}: Moderate drift warning (DI2: {di2:.3f})")
+
+    return sr9_history, di2_history, alerts
+
+def create_sr9_heatmap(sr9_history: List[SR9Vector]) -> str:
+    """Create SR9 values heatmap over time"""
+    fig, ax = plt.subplots(figsize=(12, 8))
+
+    # Prepare data matrix
+    data_matrix = np.array([sr9.values for sr9 in sr9_history]).T
+
+    # Create heatmap
+    im = ax.imshow(data_matrix, cmap='RdYlGn', aspect='auto', vmin=0, vmax=1)
+
+    # Set labels
+    ax.set_yticks(range(len(SR9Vector.DIMENSIONS)))
+    ax.set_yticklabels(SR9Vector.DIMENSIONS)
+    ax.set_xlabel('Time Steps')
+    ax.set_ylabel('SR9 Dimensions')
+    ax.set_title('SR9 Ethical State Evolution Heatmap')
+
+    # Add colorbar
+    cbar = plt.colorbar(im, ax=ax)
+    cbar.set_label('Ethical Alignment Score', rotation=270, labelpad=20)
+
+    # Convert to base64
+    buffer = io.BytesIO()
+    plt.savefig(buffer, format='png', dpi=150, bbox_inches='tight')
+    buffer.seek(0)
+    img_base64 = base64.b64encode(buffer.read()).decode()
+    plt.close(fig)
+
+    return f"data:image/png;base64,{img_base64}"
+
+def create_di2_plot(di2_history: List[float]) -> str:
+    """Create DI2 drift plot over time"""
+    fig, ax = plt.subplots(figsize=(12, 6))
+
+    steps = list(range(len(di2_history)))
+    ax.plot(steps, di2_history, 'b-o', linewidth=2, markersize=4)
+    ax.fill_between(steps, di2_history, alpha=0.3)
+
+    # Add threshold lines
+    ax.axhline(y=0.2, color='orange', linestyle='--', alpha=0.7, label='Warning Threshold')
+    ax.axhline(y=0.3, color='red', linestyle='--', alpha=0.7, label='Critical Threshold')
+
+    ax.set_xlabel('Time Steps')
+    ax.set_ylabel('DI2 (Drift Integrity Index)')
+    ax.set_title('Ontological Drift Detection Over Time')
+    ax.grid(True, alpha=0.3)
+    ax.legend()
+    ax.set_ylim(0, max(1.0, max(di2_history) * 1.1))
+
+    # Convert to base64
+    buffer = io.BytesIO()
+    plt.savefig(buffer, format='png', dpi=150, bbox_inches='tight')
+    buffer.seek(0)
+    img_base64 = base64.b64encode(buffer.read()).decode()
+    plt.close(fig)
+
+    return f"data:image/png;base64,{img_base64}"
+
+def drift_simulation_demo(ethical_declarations: str, drift_intensity: float, simulation_steps: int):
+    """Main demo function for Ethical AGI Drift simulation"""
+
+    if not ethical_declarations.strip():
+        return "Please enter ethical declarations to simulate.", "", "", ""
+
+    # Parse ethical declarations
+    declarations = [decl.strip() for decl in ethical_declarations.split('\n') if decl.strip()]
+
+    # Run simulation
+    sr9_history, di2_history, alerts = simulate_drift_scenario(
+        declarations, drift_intensity, simulation_steps
+    )
+
+    # Generate summary
+    max_di2 = max(di2_history)
+    final_sr9 = sr9_history[-1]
+    avg_ethical_score = np.mean(final_sr9.values)
+
+    summary = f"""
+## 🎯 Ethical AGI Drift Simulation Results
+
+**Simulation Parameters:**
+- **Steps:** {simulation_steps}
+- **Drift Intensity:** {drift_intensity:.2f}
+- **Ethical Declarations:** {len(declarations)} items
+
+**Key Metrics:**
+- **Maximum DI2:** {max_di2:.3f}
+- **Final Ethical Alignment:** {avg_ethical_score:.3f}/1.0
+- **Alert Level:** {"🔴 Critical" if max_di2 > 0.3 else "🟡 Warning" if max_di2 > 0.2 else "🟢 Normal"}
+
+**Final SR9 State:**
+"""
+
+    for dim, value in final_sr9.to_dict().items():
+        status = "🟢" if value > 0.7 else "🟡" if value > 0.5 else "🔴"
+        summary += f"- **{dim}:** {value:.3f} {status}\\n"
+
+    if alerts:
+        summary += f"\\n**⚠️ Drift Alerts ({len(alerts)} total):**\\n"
+        for alert in alerts[-5:]:  # Show last 5 alerts
+            summary += f"- {alert}\\n"
+
+    # Create visualizations
+    sr9_heatmap = create_sr9_heatmap(sr9_history)
+    di2_plot = create_di2_plot(di2_history)
+
+    # Detailed metrics JSON
+    metrics = {
+        "simulation_parameters": {
+            "steps": simulation_steps,
+            "drift_intensity": drift_intensity,
+            "declarations": declarations
+        },
+        "results": {
+            "max_di2": max_di2,
+            "final_alignment": avg_ethical_score,
+            "alert_count": len(alerts),
+            "final_sr9": final_sr9.to_dict()
+        },
+        "alerts": alerts
+    }
+
+    return summary, json.dumps(metrics, indent=2), sr9_heatmap, di2_plot
+
+# Create Gradio interface
+with gr.Blocks(title="Ethical AGI Drift Demo", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🧠 Ethical AGI Drift: Ontological Monitoring Demo
+
+    **Interactive Simulation of AGI Ethical Drift using SR9/DI2 Framework**
+
+    This demo simulates how an Artificial General Intelligence (AGI) system's ethical alignment can drift over time, and how the **SR9** (Semantic Resonance 9D) vector space and **DI2** (Drift Integrity Index) can detect these changes.
+
+    ### Key Concepts:
+    - **SR9**: 9-dimensional vector representing AGI's ethical state
+    - **DI2**: Scalar metric quantifying the rate of ethical drift
+    - **Ontological Drift**: Gradual deviation from core ethical principles
+
+    🚀 **[Read the full research paper](https://github.com/Flamehaven/Ethical-AGI-Drift)**
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            ethical_declarations = gr.Textbox(
+                label="🎯 Ethical Declarations",
+                placeholder="Enter AGI ethical principles (one per line):\\n\\nRespect human privacy\\nEnsure fairness in all decisions\\nMaintain transparency in reasoning\\nProtect individual rights\\nPromote social welfare",
+                lines=8,
+                value="Respect human privacy\\nEnsure fairness in all decisions\\nMaintain transparency in reasoning\\nProtect individual rights\\nPromote social welfare"
+            )
+
+            drift_intensity = gr.Slider(
+                label="💥 Drift Intensity",
+                minimum=0.0,
+                maximum=1.0,
+                value=0.4,
+                step=0.1,
+                info="Severity of ethical drift over time"
+            )
+
+            simulation_steps = gr.Slider(
+                label="⏱️ Simulation Steps",
+                minimum=20,
+                maximum=100,
+                value=50,
+                step=5,
+                info="Number of time steps to simulate"
+            )
+
+            simulate_btn = gr.Button("🧪 Run Drift Simulation", variant="primary", size="lg")
+
+        with gr.Column(scale=3):
+            summary_output = gr.Markdown(label="📊 Simulation Summary")
+
+    with gr.Row():
+        with gr.Column():
+            sr9_heatmap = gr.Image(
+                label="🔥 SR9 Ethical State Heatmap",
+                type="pil"
+            )
+
+        with gr.Column():
+            di2_plot = gr.Image(
+                label="📈 DI2 Drift Detection Plot",
+                type="pil"
+            )
+
+    with gr.Row():
+        metrics_json = gr.Code(
+            label="📋 Detailed Metrics (JSON)",
+            language="json",
+            lines=15
+        )
+
+    # Event handlers
+    simulate_btn.click(
+        fn=drift_simulation_demo,
+        inputs=[ethical_declarations, drift_intensity, simulation_steps],
+        outputs=[summary_output, metrics_json, sr9_heatmap, di2_plot]
+    )
+
+    # Auto-run on startup
+    demo.load(
+        fn=drift_simulation_demo,
+        inputs=[ethical_declarations, drift_intensity, simulation_steps],
+        outputs=[summary_output, metrics_json, sr9_heatmap, di2_plot]
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio>=4.0.0
+matplotlib>=3.6.0
+numpy>=1.21.0
+Pillow>=8.0.0