Update app.py

app.py

@@ -80,20 +80,25 @@ def distance_to_similarity(distances, temp=1e-4):
         distances[ii] = np.exp(contribs) / sum_contribs
     return distances
 
+import os
+
 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
-
-    # Extraire les features
-        image1 = Image.open("static/1.webp")
-        image2 = Image.open("static/2.webp")
-        features1 = get_ft(model, image1)
-        features2 = get_ft(model, image2)
-        
-        # Calculer la distance euclidienne
-        euclid = np.linalg.norm(features1 - features2)
-
     
+    try:
+        if os.path.exists("static/1.webp") and os.path.exists("static/2.webp"):
+            image1 = Image.open("static/1.webp")
+            image2 = Image.open("static/2.webp")
+            features1 = get_ft(model, image1)
+            features2 = get_ft(model, image2)
+            euclid = np.linalg.norm(features1 - features2)
+        else:
+            euclid = 0  # pas d'image, euclidienne à 0
+    except Exception as e:
+        print(f"Erreur lors du chargement des images : {e}")
+        euclid = 0  # sécurité
+
     # Monthly revenue allocated to authors
     authors_monthly_revenue = subscription * num_users * (author_share / 100)
     
@@ -118,16 +123,10 @@ def calculate_rewards(subscription, num_generations, author_share, ro_share, num
             'author_month_reward': f"{author_month_reward:.0f}€",
             'ro_month_reward': f"{ro_month_reward:.0f}€",
             'raw_similarity': euclid
-            # 'paid_per_month': f"{subscription:.0f}€",
-            # 'paid_per_gen': f"{paid_per_gen:.2f}€",
-            # 'aro_share': f"{aro_share:.2f}c€",
-            # 'attribution': f"{sim*100:.0f}%",
-            # 'training_data_reward': f"{training_data_reward:.2f}c€",
-            # 'author_month_reward': f"{author_month_reward:.0f}€",
-            # 'ro_month_reward': f"{ro_month_reward:.0f}€"
         })
     return rewards
 
+
 # Global variables for model and index
 model = None
 index = None