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app.py

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+
+import gradio as gr
+import numpy as np
+import json
+
+# RRF Phi 5.2 Constants (Derived from simulation)
+LAMBDA_INF = 1.000156
+ALPHA = -7.295183
+BETA = 0.963103
+RECIPROCITY = 1.000000
+
+def predict_rrf_stability(n_nodes):
+    """
+    Calculates the RRF Stability parameters for a given manifold density.
+    """
+    n = float(n_nodes)
+    # Apply RRF Scaling Law
+    predicted_lambda = LAMBDA_INF + ALPHA / (n**BETA)
+    
+    # Spectral Reciprocity g = 1/lambda
+    required_g = RECIPROCITY / predicted_lambda
+    
+    coherence_estimate = 0.8163 + (0.18 * (1 - 1/np.log10(n)))
+    
+    return {
+        "Predicted Spectral Gap (lambda2)": round(predicted_lambda, 6),
+        "Required Coupling Strength (g_crit)": round(required_g, 6),
+        "Estimated Coherence Score": f"{round(coherence_estimate * 100, 2)}%",
+        "Status": "Stable" if predicted_lambda > 0.3 else "Critically Undersampled"
+    }
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=predict_rrf_stability,
+    inputs=gr.Number(value=1000, label="Manifold Density (Node Count N)"),
+    outputs="json",
+    title="RRF Phi 5.2: Resonance Stability Service",
+    description="Compute universal invariants and stability ridges for the Resonance of Reality Framework."
+)
+
+if __name__ == '__main__':
+    iface.launch()

requirements.txt

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+gradio
+numpy