Update app.py

la version précédente provoque encore un "runtime error"
Remplacement complet de calculate_rewards

app.py

@@ -82,51 +82,33 @@ def distance_to_similarity(distances, temp=1e-4):
 
 import os
 
+import os
+from PIL import Image
+import numpy as np
+
 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
+    """Calculate raw similarity (distance) between two static images"""
     
     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, image1)    #temporaire : remettre image2
+            features2 = get_ft(model, image1)  # temporaire : remettre image2
             euclid = float(np.linalg.norm(features1 - features2))
         else:
-            euclid = 0  # pas d'image, euclidienne à 0
+            euclid = 0.0
     except Exception as e:
         print(f"Erreur lors du chargement des images : {e}")
-        euclid = 0  # sécurité
+        euclid = 0.0
 
-    # 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 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)
-        ###except Exception as e:
-        ###    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': euclid
-        })
+    rewards = [{
+        'raw_similarity': euclid
+    }]
     return rewards
 
 
+
 # Global variables for model and index
 model = None
 index = None