Update app.py

app.py

@@ -2,32 +2,65 @@ import gradio as gr
 import cv2
 import numpy as np
 import joblib
-from utils import extract_features  # Your feature extraction logic
+import os
+from utils import extract_features  # Make sure this exists in utils.py
 
-# Load model and encoders
-model = joblib.load("model/random_forest.pkl")
-label_encoders = joblib.load("model/label_encoders.pkl")
+def safe_load_model():
+    """Safely loads model files with error handling"""
+    try:
+        # Verify files exist
+        if not all(os.path.exists(f'model/{f}') for f in ['random_forest.pkl', 'label_encoders.pkl']):
+            raise FileNotFoundError("Model files missing")
+            
+        # Load with mmap_mode for Hugging Face
+        model = joblib.load('model/random_forest.pkl', mmap_mode='r')
+        encoders = joblib.load('model/label_encoders.pkl', mmap_mode='r')
+        print("Model loaded successfully!")
+        return model, encoders
+        
+    except Exception as e:
+        print(f"Model loading failed: {str(e)}")
+        # Fallback simple model
+        from sklearn.ensemble import RandomForestClassifier
+        from sklearn.preprocessing import LabelEncoder
+        print("Using fallback model")
+        return RandomForestClassifier(n_estimators=10), {
+            'face_shape': LabelEncoder().fit(['Oval', 'Round', 'Square']),
+            'skin_tone': LabelEncoder().fit(['Fair', 'Medium', 'Dark']),
+            'face_size': LabelEncoder().fit(['Small', 'Medium', 'Large'])
+        }
 
 def recommend_mask(image):
-    # Extract face shape, skin tone, face size from image
-    face_shape, skin_tone, face_size = extract_features(image)
-    
-    # Label encode features
-    face_encoded = label_encoders["face_shape"].transform([face_shape])[0]
-    skin_encoded = label_encoders["skin_tone"].transform([skin_tone])[0]
-    size_encoded = label_encoders["face_size"].transform([face_size])[0]
-    
-    # Predict mask style
-    prediction = model.predict([[face_encoded, skin_encoded, size_encoded]])[0]
-    return prediction
+    """Process image and make prediction"""
+    try:
+        # Extract features
+        face_shape, skin_tone, face_size = extract_features(image)
+        
+        # Encode features
+        face_encoded = encoders["face_shape"].transform([face_shape])[0]
+        skin_encoded = encoders["skin_tone"].transform([skin_tone])[0]
+        size_encoded = encoders["face_size"].transform([face_size])[0]
+        
+        # Predict
+        prediction = model.predict([[face_encoded, skin_encoded, size_encoded]])[0]
+        return encoders["mask_style"].classes_[prediction]
+        
+    except Exception as e:
+        print(f"Prediction error: {str(e)}")
+        return f"Error: Could not process image - {str(e)}"
 
-# Gradio Interface
+# Initialize model and encoders
+model, encoders = safe_load_model()
+
+# Create Gradio interface
 demo = gr.Interface(
     fn=recommend_mask,
     inputs=gr.Image(label="Upload Your Face", type="filepath"),
     outputs=gr.Textbox(label="Recommended Mask Style"),
     title="🎭 AI Party Mask Recommender",
     description="Upload a photo to get a personalized mask recommendation!",
+    examples=[["example_face.jpg"]] if os.path.exists("example_face.jpg") else None
 )
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)