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app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from tensorflow.keras.models import load_model
+
+# Load model
+model = load_model("xception_deri.h5")
+
+def predict_image(image):
+    img = cv2.resize(image, (128, 128))
+    img = img / 255.0
+    img = np.expand_dims(img, axis=0)
+    
+    prediction = model.predict(img)[0][0]
+    label = "Ini Deri" if prediction > 0.5 else "Ini bukan Deri"
+    return label, float(prediction)
+
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="numpy"),
+    outputs=[gr.Label(), gr.Number()],
+    title="Deri Classifier",
+    description="Upload gambar dan model akan memprediksi apakah itu Deri atau bukan."
+)
+
+iface.launch()

model_deri.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4a6238ea2b4d390daadb6bda4bd359bd481da6a733e9740f2b0594a002b2a0cc
+size 39712160

requirements.txt

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+tensorflow==2.10
+opencv-python
+numpy
+gradio

xception_deri.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ac6a26cb2fcc03102c46dd2f7736c70d8d944d57480d0487d2e775a3d77596a2
+size 87014712