Update app.py

app.py

@@ -6,6 +6,26 @@ from sklearn.ensemble import RandomForestClassifier
 from sklearn.preprocessing import LabelEncoder
 from utils import extract_features
 
+def initialize_fallback_model():
+    """Creates and trains a simple fallback model"""
+    print("Initializing fallback model...")
+    
+    # Simple training data
+    X = np.array([[0,0,0], [1,1,1], [2,2,2]])  # Dummy encoded features
+    y = np.array([0, 1, 0])  # Dummy target
+    
+    model = RandomForestClassifier(n_estimators=10)
+    model.fit(X, y)
+    
+    encoders = {
+        'face_shape': LabelEncoder().fit(['Oval', 'Round', 'Square']),
+        'skin_tone': LabelEncoder().fit(['Fair', 'Medium', 'Dark']),
+        'face_size': LabelEncoder().fit(['Small', 'Medium', 'Large']),
+        'mask_style': LabelEncoder().fit(['StyleA', 'StyleB', 'StyleC'])  # Added mask_style
+    }
+    
+    return model, encoders
+
 def safe_load_model():
     """Safely loads model files with comprehensive fallback"""
     try:
@@ -48,27 +68,6 @@ def recommend_mask(image):
 # Initialize model and encoders
 model, encoders = safe_load_model()
 
-def initialize_fallback_model():
-    """Creates and trains a simple fallback model"""
-    print("Initializing fallback model...")
-    
-    # Simple training data
-    X = np.array([[0,0,0], [1,1,1], [2,2,2]])  # Dummy encoded features
-    y = np.array([0, 1, 0])  # Dummy target
-    
-    model = RandomForestClassifier(n_estimators=10)
-    model.fit(X, y)
-    
-    encoders = {
-        'face_shape': LabelEncoder().fit(['Oval', 'Round', 'Square']),
-        'skin_tone': LabelEncoder().fit(['Fair', 'Medium', 'Dark']),
-        'face_size': LabelEncoder().fit(['Small', 'Medium', 'Large']),
-        'mask_style': LabelEncoder().fit(['StyleA', 'StyleB', 'StyleC'])  # Added mask_style
-    }
-    
-    return model, encoders
-
-
 # Create Gradio interface
 demo = gr.Interface(
     fn=recommend_mask,