Update app.py

app.py

@@ -6,6 +6,7 @@ from fastapi.responses import HTMLResponse
 from sklearn.decomposition import PCA
 from sklearn.cluster import AgglomerativeClustering
 from sklearn.metrics.pairwise import euclidean_distances
+import time
 
 app = FastAPI()
 
@@ -33,7 +34,7 @@ class AdaptiveVectorSystem:
         
         # Base score starts at 10
         # We penalize for high variation (chaos) and raw distance
-        penalty = (variation_coefficient * 5.0) + (mean_dist * 0.1)
+        penalty = (variation_coefficient * 6.0) + (mean_dist * 0.1)
         
         score = 10.0 - penalty
         return max(-1.0, min(10.0, score))
@@ -97,14 +98,15 @@ class AdaptiveVectorSystem:
 # --- VISUALIZATION LOGIC ---
 def generate_plot(mode='global', scenario='split'):
     # Generate 128-dimension vectors
-    np.random.seed(42) # Consistent seed for demo
+    np.random.seed(int(time.time() * 1000)
+                  ) # Consistent seed for demo
     
     if scenario == 'split':
         # Create two dense islands far apart
         # Island 1: centered at 0
-        c1 = np.random.normal(0, 0.5, (20, 128))
+        c1 = np.random.normal(0, 0.5, (100, 128))
         # Island 2: centered at 10 (In 128D, distance approx sqrt(128*100) = ~113 units away)
-        c2 = np.random.normal(8, 0.5, (20, 128))
+        c2 = np.random.normal(8, 0.5, (100, 128))
         # Noise: Random scatter
         noise = np.random.uniform(-5, 15, (10, 128))
         data = np.vstack([c1, c2, noise])