Update app.py

app.py

@@ -83,6 +83,16 @@ 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
+
+    # 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)
+
     
     # Monthly revenue allocated to authors
     authors_monthly_revenue = subscription * num_users * (author_share / 100)
@@ -107,7 +117,7 @@ def calculate_rewards(subscription, num_generations, author_share, ro_share, num
             '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
+            'raw_similarity': euclid
             # 'paid_per_month': f"{subscription:.0f}€",
             # 'paid_per_gen': f"{paid_per_gen:.2f}€",
             # 'aro_share': f"{aro_share:.2f}c€",
@@ -286,11 +296,3 @@ if __name__ == '__main__':
     init_model()
     app.run(host='0.0.0.0', port=7860)
 
-# 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
-        #distances = np.linalg.norm(features1 - features2)