Update app.py

app.py

@@ -86,35 +86,36 @@ def distance_to_similarity(distances, temp=1e-4):
 def calculate_rewards(subscription, num_generations, author_share, ro_share, num_users_k, similarities, num_authors=1800):
     """Calculate rewards based on user inputs and similarities"""
     num_users = num_users_k * 1000
-    
+
     # Monthly revenue allocated to authors
     authors_monthly_revenue = subscription * num_users * (author_share / 100)
-    
+
     rewards = []
     for sim in similarities[0]:
-        # Attribution bonus based on similarity score (sim) and number of neighbors (len)
+        # Attribution bonus based on similarity score (sim) and number of neighbors
         attribution_bonus = sim * len(similarities[0])
-        
+
         # Calculate monthly rewards
         author_month_reward = (authors_monthly_revenue / num_authors) * attribution_bonus
         ro_month_reward = author_month_reward / (author_share / 100) * (ro_share / 100)
-        
+
         try:
-        sim_value = float(sim.item()) if hasattr(sim, "item") else float(sim)
+            sim_value = float(sim.item()) if hasattr(sim, "item") else float(sim)
         except Exception as e:
-        print("Erreur de conversion de sim:", e)
-        sim_value = 0.0  # valeur par défaut en cas d'erreur
+            print("Erreur de conversion de sim:", e)
+            sim_value = 0.0  # valeur par défaut en cas d'erreur
 
         rewards.append({
-    'paid_per_month': f"{subscription:.0f}€",
-    'attribution': f"{sim * 100:.0f}%",
-    'author_month_reward': f"{author_month_reward:.0f}€",
-    'ro_month_reward': f"{ro_month_reward:.0f}€",
-    'raw_similarity': sim_value  # Ajout sécurisé
-})
+            'paid_per_month': f"{subscription:.0f}€",
+            'attribution': f"{sim * 100:.0f}%",
+            'author_month_reward': f"{author_month_reward:.0f}€",
+            'ro_month_reward': f"{ro_month_reward:.0f}€",
+            'raw_similarity': sim_value
+        })
 
     return rewards
 
+
 # Global variables for model and index
 model = None
 index = None