Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load model
+model = tf.keras.models.load_model("model/car_model.h5")
+class_names = ['Audi A4', 'Toyota Corolla', 'BMW X5', 'Ford Focus', 'Honda Civic', 
+               'Hyundai Elantra', 'Mercedes C Class', 'Kia Sportage', 'Chevrolet Cruze', 'Mazda 3']  # Ganti sesuai dataset
+
+def classify_car(image):
+    image = image.resize((224, 224))
+    img_array = tf.keras.utils.img_to_array(image) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    predictions = model.predict(img_array)[0]
+    top_3 = np.argsort(predictions)[-3:][::-1]
+    return {class_names[i]: float(predictions[i]) for i in top_3}
+
+interface = gr.Interface(fn=classify_car, 
+                         inputs=gr.Image(type="pil"),
+                         outputs=gr.Label(num_top_classes=3),
+                         title="Car Brand & Model Classifier",
+                         description="Upload a car image to predict the brand and model.")
+
+if __name__ == "__main__":
+    interface.launch()