Update app.py

la version précédente calcule et affiche correctement la distance entre static1 et static2.
Cette version neutralise avec trois ### certaines lignes superflues, à vérifier

app.py

@@ -84,14 +84,14 @@ 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
+    ###num_users = num_users_k * 1000
     
     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)
+            features2 = get_ft(model, image1)    #temporaire : remettre image2
             euclid = float(np.linalg.norm(features1 - features2))
         else:
             euclid = 0  # pas d'image, euclidienne à 0
@@ -100,28 +100,28 @@ def calculate_rewards(subscription, num_generations, author_share, ro_share, num
         euclid = 0  # sécurité
 
     # Monthly revenue allocated to authors
-    authors_monthly_revenue = subscription * num_users * (author_share / 100)
+    ###authors_monthly_revenue = subscription * num_users * (author_share / 100)
     
     rewards = []
-    for sim in similarities[0]:
+    ###for sim in similarities[0]:
         # Attribution bonus based on similarity score and number of neighbors
-        attribution_bonus = sim * len(similarities[0])
+        ###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)
+        ###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
+        ###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}€",
+        ###    '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
         })
     return rewards
@@ -223,60 +223,62 @@ def select_preset(preset_id):
 def process_image():
     if 'image' not in request.files:
         return jsonify({'error': 'No image provided'}), 400
-    
+
+    #Récupère l'image envoyée, la lit avec PIL (Image.open), la convertit en RGB
     try:
-        image_file = request.files['image']
+        image_file = request.files['image']                                
         image = Image.open(io.BytesIO(image_file.read())).convert('RGB')
         
         # Use default parameters if none provided
-        params = DEFAULT_PARAMS.copy()
-        if request.form:
-            params.update({
-                'subscription': float(request.form.get('subscription', params['subscription'])),
-                'num_generations': int(request.form.get('num_generations', params['num_generations'])),
-                'author_share': float(request.form.get('author_share', params['author_share'])),
-                'ro_share': float(request.form.get('ro_share', params['ro_share'])),
-                'num_users_k': int(request.form.get('num_users_k', params['num_users_k'])),
-                'num_neighbors': int(request.form.get('num_neighbors', params['num_neighbors'])),
-                'num_authors': int(request.form.get('num_authors', DEFAULT_PARAMS['num_authors'])),
-            })
+        ### params = DEFAULT_PARAMS.copy()
+        ### if request.form:
+        ###    params.update({
+        ###        'subscription': float(request.form.get('subscription', params['subscription'])),
+        ###        'num_generations': int(request.form.get('num_generations', params['num_generations'])),
+        ###        'author_share': float(request.form.get('author_share', params['author_share'])),
+        ###        'ro_share': float(request.form.get('ro_share', params['ro_share'])),
+        ###        'num_users_k': int(request.form.get('num_users_k', params['num_users_k'])),
+        ###        'num_neighbors': int(request.form.get('num_neighbors', params['num_neighbors'])),
+        ###        'num_authors': int(request.form.get('num_authors', DEFAULT_PARAMS['num_authors'])),
+        ###    })
         
         # Process image
         features = get_ft(model, image)
         distances, indices = get_topk(index, features, topk=params['num_neighbors'])
         
         # Collect valid results first
-        valid_results = []
-        valid_similarities = []
-        for i in range(params['num_neighbors']):
-            image_url = urls[indices[0][i]].strip()
-            try:
-                response = requests.head(image_url)
-                if response.status_code == 200:
-                    valid_results.append({
-                        'index': i,
-                        'url': image_url
-                    })
-                    valid_similarities.append(distances[0][i])
-            except requests.RequestException:
-                continue
+           valid_results = []                         #Pour chaque image trouvée : Vérifie que l'URL est valide (avec requests.head)
+        ### valid_similarities = []                    #Si l'URL est OK, ajoute l'image à valid_results et garde sa distance
+        ### for i in range(params['num_neighbors']):
+        ###     image_url = urls[indices[0][i]].strip()
+        ###     try:
+        ###         response = requests.head(image_url)
+        ###         if response.status_code == 200:
+        ###             valid_results.append({
+        ###                 'index': i,
+        ###                 'url': image_url
+        ###             })
+        ###             valid_similarities.append(distances[0][i])  
+        ###     except requests.RequestException:
+        ###         continue
         
         # Renormalize similarities for valid results
-        if valid_similarities:
-            similarities = distance_to_similarity(np.array([valid_similarities]), temp=1e-5)
+        ### if valid_similarities:
+        ###     similarities = distance_to_similarity(np.array([valid_similarities]), temp=1e-5)
             
             # Calculate rewards with renormalized similarities
-            rewards = calculate_rewards(
-                params['subscription'],
-                params['num_generations'],
-                params['author_share'],
-                params['ro_share'],
-                params['num_users_k'],
-                similarities,
-                params['num_authors']
-            )
+            ### rewards = calculate_rewards(
+            ###     params['subscription'],
+            ###     params['num_generations'],
+            ###     params['author_share'],
+            ###     params['ro_share'],
+            ###     params['num_users_k'],
+            ###     similarities,
+            ###     params['num_authors']
+            ### )
             
             # Build final results
+            # Pour chaque image valide, associe l'URL, la récompense calculée, et des infos supplémentaires récupérées avec get_other_info()
             results = []
             for i, result in enumerate(valid_results):
                 other_info = get_other_info(result['url'])