Update app.py

app.py

@@ -1,51 +1,59 @@
-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)
+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
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# Load the model
+model = load_model('MyResNet101Model_final.keras')
+
+# Define label mappings based on your dataset
+label2id = {'Hispa': 0, 'Bercak Coklat': 1, 'Blast Daun': 2, 'Sehat': 3}
+class_names = list(label2id.keys())
+
+# Function to predict an image
+def predict(img):
+    start = timer()
+
+    img = img.resize((224, 224))  # Ensure size matches the model's expected input
+    img_array = tf.keras.preprocessing.image.img_to_array(img)
+    img_array = tf.expand_dims(img_array, 0)  # Create batch dimension
+
+    # Normalize image
+    img_array /= 255.0
+
+    # Prediction
+    predictions = model.predict(img_array)
+    pred_prob = tf.nn.softmax(predictions[0])
+    pred_dict = {class_names[i]: float(pred_prob[i]) for i in range(len(class_names))}
+
+    pred_time = round(timer() - start, 5)
+
+    return pred_dict, pred_time
+
+# Example images for demonstration
+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)