update file

app.py

@@ -0,0 +1,54 @@
+import os
+import cv2
+import numpy as np
+from PIL import Image
+import streamlit as st
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array
+
+# Load model
+model = load_model('model/model-mask.h5', include_optimizer=True)
+model.compile(loss = 'categorical_crossentropy', optimizer = tf.keras.optimizers.AdamW(learning_rate=0.001, weight_decay=0.004), metrics = ['accuracy'])
+
+# Class names sesuai dengan dataset Anda
+class_names = ['Klana', 'Pamindo', 'Panji', 'Rumyang', 'Tumenggung']
+
+# Setup Streamlit
+st.title('Image Classification - Topeng Cirebon')
+st.write("Upload gambar topeng untuk diklasifikasikan:")
+
+uploaded_file = st.file_uploader("Pilih file gambar...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Convert the uploaded image to a PIL image
+    image = Image.open(uploaded_file)
+
+    # Resize the image to the model input size
+    image = image.resize((100, 100))
+
+    # Convert the image to a numpy array
+    image_array = img_to_array(image)
+
+    # Add a batch dimension to the image
+    image_array = np.expand_dims(image_array, axis=0)
+
+    # Make predictions using the model
+    predictions = model.predict(image_array)
+
+    # Get the predicted class indices
+    predicted_class_indices = np.argmax(predictions, axis=1)
+
+    # Get the predicted class names
+    predicted_class_names = []
+    for i in predicted_class_indices:
+        if 0 <= i < len(class_names):
+            predicted_class_names.append(class_names[i])
+        else:
+            predicted_class_names.append("Unknown")
+
+    # Display the predicted class names
+    st.write("Predicted class:", predicted_class_names[0])
+
+    # Display the uploaded image
+    st.image(image, caption="Uploaded image")

data/hasil_model.csv

@@ -1,2 +1,2 @@
 Model,Data Train,Data Test,Epochs,Batch Size,Loss,Accuracy
-<keras.src.engine.sequential.Sequential object at 0x000001F0A93A76D0>,"(800, 28, 1)","(200, 28, 1)",30,32,0.01797403395175934,0.9950000047683716
+<keras.src.engine.sequential.Sequential object at 0x0000025D7305EE90>,"(800, 28, 1)","(200, 28, 1)",30,32,0.015860186889767647,0.9950000047683716

data/hasil_model_NN.csv

@@ -1,2 +1,2 @@
 Model,Data Train,Data Test,Epochs,Batch Size,Loss,Accuracy
-<keras.src.engine.sequential.Sequential object at 0x0000022028123FD0>,"(800, 28, 1)","(200, 28, 1)",20,64,0.06803662329912186,0.9850000143051147
+<keras.src.engine.sequential.Sequential object at 0x000002EF9A331550>,"(800, 28, 1)","(200, 28, 1)",20,64,0.06901808828115463,0.9850000143051147

model/base_model.json/fingerprint.pb

@@ -1 +1 @@
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model/nn_model.json/fingerprint.pb

@@ -0,0 +1 @@
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model/nn_model.json/keras_metadata.pb

@@ -0,0 +1,7 @@
+
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+�
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model/saved_model/fingerprint.pb

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