Update app.py

app.py

@@ -71,19 +71,25 @@ def register_user(image, user_id):
 def recognize_user(image):
     try:
         new_embedding = generate_embedding(image)
-        closest_user_id = None
-        closest_distance = float('inf')
-
-        for user_id, embedding in zip(user_ids, user_embeddings):
-            distance = cosine(new_embedding, embedding)
-            if distance < closest_distance:
-                closest_distance = distance
-                closest_user_id = user_id
-
-        if closest_distance <= RECOGNITION_THRESHOLD:
-            return f"Recognized User: {closest_user_id}"
-        else:
+        
+        if len(user_embeddings) < n_clusters:
+            # Handle the case where there are not enough users for K-means
+            # For example, you could use nearest neighbor search among existing embeddings
+            # Here, I'm just returning a message for simplicity
+            return "Not enough registered users for recognition."
+
+        # Update the KMeans model
+        kmeans.fit(user_embeddings)
+        cluster_label = kmeans.predict([new_embedding])[0]
+        distances = kmeans.transform([new_embedding])[0]
+        min_distance = np.min(distances)
+
+        if min_distance > RECOGNITION_THRESHOLD:
             return "User not recognized."
+
+        # Find the user ID(s) in the closest cluster
+        recognized_user_ids = [user_ids[i] for i, label in enumerate(kmeans.labels_) if label == cluster_label]
+        return f"Recognized User(s): {', '.join(recognized_user_ids)}"
     except Exception as e:
         return f"Error during recognition: {str(e)}"