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.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/bercak_coklat/Ex[[:space:]]brownSpot.jpg filter=lfs diff=lfs merge=lfs -text
+examples/Hispa/IMG_20190420_194642.jpg filter=lfs diff=lfs merge=lfs -text
+MyResNet101Model_final.keras filter=lfs diff=lfs merge=lfs -text

MyResNet101Model_final.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dd162f759e74edc39e7c93124150529c08f1c159fef79b1355d31897db01ecb5
+size 232818380

app.py

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+import gradio as gr
+import pathlib
+import random
+import tensorflow as tf
+from PIL import Image
+from timeit import default_timer as timer
+from keras.models import load_model
+
+# Load the model
+model = load_model('MyResNet101Model_final.keras')
+
+# Define label mappings
+label2id = {'Hispa': 0, 'Bercak Coklat': 1, 'Blast Daun': 2, 'Sehat': 3}
+class_names = list(label2id.keys())
+
+def predict(img):
+    start = timer()
+
+    img = img.resize((224, 224))
+    img_aug = tf.keras.layers.Rescaling(1., input_shape=(224, 224, 3))
+    output = tf.expand_dims(img_aug(img), 0)
+    pred_prob = tf.nn.softmax(model.predict(output))
+    pred_dict = {class_names[i]:float(tf.squeeze(pred_prob)[i]) for i in range(len(class_names))}
+
+    pred_time = round(timer() - start, 5)
+
+    return pred_dict, pred_time
+
+# Get example images
+example_paths = list(pathlib.Path('examples').glob("*/*.jpg"))
+example_list = [[str(filepath)] for filepath in random.sample(example_paths, k=4)]
+
+# Set up the Gradio interface
+title = 'Klasifikasi Penyakit Daun Padi'
+description = 'Upload gambar daun padi untuk mengklasifikasikan penyakitnya.'
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type='pil'),
+    outputs=[
+        gr.Label(num_top_classes=4, label='Prediksi'),
+        gr.Number(label="Waktu prediksi (detik)")
+    ],
+    description=description,
+    title=title,
+    allow_flagging='never',
+    examples=example_list
+)
+
+if __name__ == "__main__":
+    demo.launch(debug=True)

requirements.txt

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+gradio
+tensorflow==2.15.0
+pillow
+keras==2.15.0