Ensure application file is placed correctly

app.py

@@ -0,0 +1,40 @@
+import gradio as gr
+import numpy as np
+from keras.preprocessing import image
+from Model_Load import load_model_from_files
+
+# Load model dan label
+model = load_model_from_files("model.json", "my_model.h5")
+
+labels = [
+    "Benteng Vredeburg", "Candi Borobudur", "Candi Prambanan", "Gedung Agung Istana Kepresidenan",
+    "Masjid Gedhe Kauman", "Monumen Serangan 1 Maret", "Museum Gunungapi Merapi",
+    "Situs Ratu Boko", "Taman Sari", "Tugu Yogyakarta"
+]
+
+# Fungsi preprocessing dan prediksi
+def classify_image(img):
+    img = img.resize((224, 224))
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array / 255.0
+
+    pred = model.predict(img_array)[0]
+    confidence = np.max(pred)
+    predicted_label = labels[np.argmax(pred)]
+
+    return f"{predicted_label} (Confidence: {confidence * 100:.2f}%)"
+
+# Buat antarmuka Gradio
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Klasifikasi Cagar Budaya DIY",
+    description="Upload gambar dan model akan mengklasifikasikannya ke dalam salah satu situs budaya di Yogyakarta."
+)
+
+# Launch untuk Hugging Face Spaces
+if __name__ == "__main__":
+    demo.launch()
+