Create app.py

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+import joblib
+import os
+from utils import extract_features
+
+# Auto-train if model doesn't exist
+if not os.path.exists('model/random_forest.pkl'):
+    print("Training model...")
+    os.system('python train_model.py')
+
+# Load model
+model = joblib.load('model/random_forest.pkl')
+encoders = joblib.load('model/label_encoders.pkl')
+
+def recommend_mask(image):
+    try:
+        face_shape, skin_tone, face_size = extract_features(image)
+        prediction = model.predict([[
+            encoders['face_shape'].transform([face_shape])[0],
+            encoders['skin_tone'].transform([skin_tone])[0],
+            encoders['face_size'].transform([face_size])[0]
+        ]])[0]
+        return (
+            encoders['mask_style'].classes_[prediction],
+            encoders['mask_images'][prediction]
+        )
+    except Exception as e:
+        return "Error", "masks/default.png"
+
+demo = gr.Interface(
+    fn=recommend_mask,
+    inputs=gr.Image(type="filepath"),
+    outputs=[
+        gr.Textbox(label="Recommended Style"),
+        gr.Image(label="Mask Preview")
+    ],
+    title="🎭 Mask Recommender"
+)
+
+if __name__ == "__main__":
+    demo.launch()