{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3362,"databundleVersionId":31148,"sourceType":"competition"},{"sourceId":10055362,"sourceType":"datasetVersion","datasetId":6195942}],"dockerImageVersionId":30787,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Image Classification\r\nDibuat dan dipaparkan oleh:\r\n\r\n<img src=\"https://media.licdn.com/dms/image/v2/C560BAQEeV7PUcmeqYg/company-logo_200_200/company-logo_200_200/0/1630665375202/imv_laboratory_logo?e=2147483647&v=beta&t=ndUvAcYZhpWKbQ0MUc-f4tY2Wi58MZlRuiTFNa_pcHs\" width=\"200px\"/>\r\n\r\n© 2024 Image Processing and Vision Laboratory\r\n\r\n[Instagram](https://www.instagram.com/imv.laboratory) |  [LinkedIn](https://www.linkedin.com/company/imv-laboratory/)  |  [OA Line](http://line.me/ti/p/@uyy9001n)\r\n<br/>\r\n<br/>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"Initialization\"></a>\n# <p style=\"background-color: #33312b; font-family:calibri; color:white; font-size:140%; font-family:Verdana; text-align:center; border-radius:15px 50px;\">Step 1 | Setup and Initialization</p>","metadata":{}},{"cell_type":"code","source":"# Basic\nimport os\nfrom os import listdir\nimport numpy as np\nimport pandas as pd\nimport zipfile\n\n# visuals\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom matplotlib.image import imread\nfrom PIL import Image\n\n# Scikit-learn\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report,confusion_matrix,ConfusionMatrixDisplay\n\n# Tensorflow\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.layers import Input, Dense,MaxPooling2D,Dropout,Flatten,BatchNormalization,Conv2D\nfrom tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau,EarlyStopping\nfrom tensorflow.keras.preprocessing.image import load_img, img_to_array\nfrom tensorflow.keras.metrics import Precision, Recall, BinaryAccuracy\n\nimport warnings\nwarnings.filterwarnings('ignore')","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Tentukan path file zip yang berisi dataset dan lokasi ekstraksi file\ntrain_path = \"file_path\"  # Ganti dengan path file zip dataset yang sesuai\nfiles = \"file_path\"  # Ganti dengan path folder tempat file akan diekstraksi\n\n# Membuka file zip dan mengekstrak seluruh isinya\nwith zipfile.ZipFile(train_path, 'r') as zipp:\n    zipp.extractall(files)  # Mengekstrak file ke folder yang telah ditentukan","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<a id=\"Analysis\"></a>\n# <p style=\"background-color: #33312b; font-family:calibri; color:white; font-size:140%; font-family:Verdana; text-align:center; border-radius:15px 50px;\">Step 2 | Dataset Analysis</p>","metadata":{}},{"cell_type":"code","source":"# Tentukan direktori yang berisi gambar\nimage_dir = \"file_path\"  # Ganti dengan path folder yang berisi gambar\n\n# Mengambil daftar nama file (gambar) yang ada di dalam direktori\nfilenames = os.listdir(image_dir) # Ganti dengan variabel folder gambar\n\n# Mengambil label untuk setiap gambar dengan memisahkan nama file berdasarkan titik (.) dan mengambil bagian pertama (misalnya: 'cat.jpg' -> 'cat')\nlabels = [x.split(\".\")[0] for x in filenames]  # Misalnya, jika nama file adalah 'cat.jpg', labelnya akan 'cat'\n\n# Membuat DataFrame dari Pandas dengan dua kolom: 'filename' dan 'label'\ndata = pd.DataFrame({\"filename\": filenames, \"label\": labels})\n\n# Menampilkan lima baris pertama dari DataFrame untuk memastikan data sudah terbaca dengan benar\ndata.head()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(20, 10))\nplt.subplots_adjust(hspace=0.4)\n\n# Menampilkan gambar anjing dan kucing\nfor i, label in enumerate(['dog', 'cat']):\n    for j in range(5): \n        plt.subplot(2, 5, i * 5 + j + 1) \n        filename = f'train/{label}.{j}.jpg' \n        image = imread(filename)\n        plt.imshow(image)\n        plt.title(label.capitalize(), fontsize=12)\n        plt.axis('off')\n\nplt.show()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<a id=\"Preparation\"></a>\n# <p style=\"background-color: #33312b; font-family:calibri; color:white; font-size:140%; font-family:Verdana; text-align:center; border-radius:15px 50px;\">Step 3 | Dataset Preparation</p>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"Data_Splitting\"></a>\n# <b><span style='color:gray'>Step 3.1 |</span><span style='color:#33312b'> Data Splitting into Training, Validation and Training Sets</span></b>","metadata":{}},{"cell_type":"code","source":"#Split dataset menjadi training (70%), validation (20%), test (10%)\nlabels = data['label']\n\nX_train, X_temp = train_test_split(data, test_size= , stratify=labels, random_state = 123)\n\nlabel_test_val = X_temp['label']\n\nX_test, X_val = train_test_split(X_temp, test_size= , stratify=label_test_val, random_state = 123)\n\nprint('The shape of train data',X_train.shape)\nprint('The shape of validation data',X_val.shape)\nprint('The shape of test data',X_test.shape)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"labels = ['Cat','Dog']\n\nlabel1,count1 = np.unique(X_train.label,return_counts=True)\nlabel2,count2 = np.unique(X_val.label,return_counts=True)\nlabel3,count3 = np.unique(X_test.label,return_counts=True)\n\nuni1 = pd.DataFrame(data=count1,index=labels,columns=['Count1'])\nuni2 = pd.DataFrame(data=count2,index=labels,columns=['Count2'])\nuni3 = pd.DataFrame(data=count3,index=labels,columns=['Count3'])\n\n\nplt.figure(figsize=(20,6),dpi=200)\nsns.set_style('darkgrid')\n\nplt.subplot(131)\nsns.barplot(data=uni1,x=uni1.index,y='Count1',palette='icefire',width=0.2).set_title('Class distribution in Training set',fontsize=15)\nplt.xlabel('Labels',fontsize=12)\nplt.ylabel('Count',fontsize=12)\n\nplt.subplot(132)\nsns.barplot(data=uni2,x=uni2.index,y='Count2',palette='icefire',width=0.2).set_title('Class distribution in validation set',fontsize=15)\nplt.xlabel('Labels',fontsize=12)\nplt.ylabel('Count',fontsize=12)\n\n\nplt.subplot(133)\nsns.barplot(data=uni3,x=uni3.index,y='Count3',palette='icefire',width=0.2).set_title('Class distribution in Testing set',fontsize=15)\nplt.xlabel('Labels',fontsize=12)\nplt.ylabel('Count',fontsize=12)\n\nplt.show()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<a id=\"Augmentation\"></a>\n# <b><span style='color:gray'>Step 3.2 |</span><span style='color:#33312b'> Data Augmentation & Preprocessing</span></b>","metadata":{}},{"cell_type":"code","source":"# parameters\nimage_size = # Ukuran gambar\nimage_channel = # Jumlah saluran warna\nbat_size = # Ukuran batch untuk pelatihan model","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Membuat ImageDataGenerator untuk pelatihan (training) dengan augmentasi gambar\ntrain_datagen = ImageDataGenerator(rescale= , # Menormalisasi piksel gambar ke rentang 0-1\n                                    rotation_range = , # Rotasi gambar acak\n                                    horizontal_flip = , # Membalik gambar secara horizontal\n                                    zoom_range = , # Zoom acak pada gambar\n                                    shear_range = , # Transformasi shear acak pada gambar\n                                    fill_mode = , # Metode pengisian piksel kosong setelah transformasi\n                                    width_shift_range = , # Pergeseran gambar acak secara horizontal\n                                    height_shift_range = ) # Pergeseran gambar acak secara vertikal\n\n# Membuat ImageDataGenerator untuk pengujian (testing)\ntest_datagen = ImageDataGenerator(rescale=)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Applying image data gernerator to train and validation data\n\ntrain_generator = train_datagen.flow_from_dataframe(#variabel train,\n                                                    directory = ,\n                                                    x_col= ,\n                                                    y_col= ,\n                                                    batch_size = ,\n                                                    target_size = ()\n                                                   )\nval_generator = test_datagen.flow_from_dataframe(#variabel validation, \n                                                 directory = '/train',  # Direktori tempat gambar berada\n                                                 x_col= 'filename', # Kolom yang berisi nama file gambar\n                                                 y_col= 'label', # Kolom yang berisi label gambar\n                                                 batch_size = bat_size, # Ukuran batch untuk pelatihan\n                                                 target_size = (image_size, image_size), # Ukuran gambar yang akan diproses oleh model\n                                                )\n\ntest_generator = test_datagen.flow_from_dataframe(#variabel test, \n                                                  directory = 'train/', # Direktori tempat gambar berada\n                                                  x_col= 'filename', # Kolom yang berisi nama file gambar\n                                                  y_col= 'label', # Kolom yang berisi label gambar\n                                                  batch_size = bat_size, # Ukuran batch untuk pelatihan\n                                                  target_size = (image size, image size), # Ukuran gambar yang akan diproses oleh model\n                                                 )","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<a id=\"Augmentation\"></a>\n# <b><span style='color:#33312b'> Contoh Data Augmentation</span></b>","metadata":{}},{"cell_type":"code","source":"img_path = '/kaggle/input/testset/anjing-1.jpeg' \nimage = load_img(img_path, target_size=(150, 150))  \nimage_array = img_to_array(image)  \nimage_array = image_array.reshape((1, 150, 150, 3)) \n\n# Membuat ImageDataGenerator untuk augmentasi\ndatagen = ImageDataGenerator(\n    rotation_range=15,          \n    width_shift_range=0.2,     \n    height_shift_range=0.2,     \n    shear_range=0.2,            \n    zoom_range=0.2,             \n    horizontal_flip=True,      \n    fill_mode='reflect'      \n)\n\n# Menampilkan gambar asli dan gambar-gambar augmentasi\nplt.figure(figsize=(12, 12))\ni = 0\nfor batch in datagen.flow(image_array, batch_size=1, save_to_dir='.', save_prefix='aug', save_format='jpeg'):\n    plt.subplot(4, 4, i + 1)\n    plt.imshow(batch[0].astype('uint8'))\n    plt.axis('off')\n    i += 1\n    if i >= 15:\n        break\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<a id=\"Model_Architecture\"></a>\n# <p style=\"background-color: #33312b; font-family:calibri; color:white; font-size:140%; font-family:Verdana; text-align:center; border-radius:15px 50px;\">Step 4 | Model Architecture Development</p>","metadata":{}},{"cell_type":"markdown","source":"![CNN](https://raw.githubusercontent.com/106AbdulBasit/Kaggle-Projects/main/Cat%20Vs%20Dog/97717Cats-Dogs-Classification-deep-learning.gif)","metadata":{}},{"cell_type":"markdown","source":"<span style=\"font-family:cursive; font-size:15px;\">\nConvolutional neural network mempunyai 3 bagian.\n</span>\n\n\n<span style=\"font-family:cursive; font-size:15px;\">1 Convolutional Layer </span>\n\n<span style=\"font-family:cursive; font-size:15px;\">2 Pooling Layer </span>\n\n<span style=\"font-family:cursive; font-size:15px;\">3 Fully Connected Layer </span>","metadata":{}},{"cell_type":"markdown","source":"<h4   style=\"color:white;\n           display:fill;\n           border-radius:5px;\n           background-color:#33312b;\n           font-size:350%;\n           font-family:Verdana;\n           letter-spacing:0.5px\">  convolutional layer </h4>\n           \n<span style=\"font-family:cursive; font-size:15px;\">Lapisan konvolusional membantu mengekstrak informasi dari gambar dengan bantuan filter.  </span>\n\n<span style=\"font-family:cursive; font-size:15px;\">Lihat gambar di bawah ini.  </span>\n\n![Coonfilter](https://raw.githubusercontent.com/106AbdulBasit/Kaggle-Projects/main/Cat%20Vs%20Dog/Convolutional%20Filter.gif)","metadata":{}},{"cell_type":"markdown","source":"<h4   style=\"color:white;\n           display:fill;\n           border-radius:5px;\n           background-color:#33312b;\n           font-size:350%;\n           font-family:Verdana;\n           letter-spacing:0.5px\">  Feature Map </h4>\n           \n<span style=\"font-family:cursive; font-size:15px;\">Filter akan membuat gambar menjadi ukuran 3 *3. </span>\n\n<span style=\"font-family:cursive; font-size:15px;\">Kemudian, akan melakukan beberapa operasi matematika . </span>\n\n<span style=\"font-family:cursive; font-size:15px;\">Elemen-elemen akan dikalikan dan kemudian dijumlahkan untuk menghasilkan satu output, dan output tersebut akan disimpan di output feature map . </span>\n\n\n![](https://raw.githubusercontent.com/106AbdulBasit/Kaggle-Projects/main/Cat%20Vs%20Dog/Featuree%20map.png)","metadata":{}},{"cell_type":"markdown","source":"<h4   style=\"color:white;\n           display:fill;\n           border-radius:5px;\n           background-color:#33312b;\n           font-size:350%;\n           font-family:Verdana;\n           letter-spacing:0.5px\">  Pooling layer</h4>\n\n\n\n<span style=\"font-family:cursive; font-size:15px;\">Inti dari lapisan pooling adalah untuk mengurangi dimensi peta fitur.. </span>\n\n<span style=\"font-family:cursive; font-size:15px;\">Lapisan pooling 2x2 akan diterapkan pada gambar input dan mengambil nilai maksimum pada setiap pergeseran.</span>\n\n\n![](https://raw.githubusercontent.com/106AbdulBasit/Kaggle-Projects/main/Cat%20Vs%20Dog/pooling%20Layer.gif)","metadata":{}},{"cell_type":"markdown","source":"<h4   style=\"color:white;\n           display:fill;\n           border-radius:5px;\n           background-color:#33312b;\n           font-size:350%;\n           font-family:Verdana;\n           letter-spacing:0.5px\">  Fully Connected Layer </h4>\n\n<span style=\"font-family:cursive; font-size:15px;\">Lapisan terakhir adalah fully connected layer</span>\n\n<span style=\"font-family:cursive; font-size:15px;\">Input ke fully connected layer adalah fitur-fitur yang telah diekstraksi menggunakan filter konvolusional.</span>\n","metadata":{}},{"cell_type":"code","source":"model = Sequential()\n\n# Input Layer\n\n\n# Block 1: Conv + MaxPooling + BatchNormalization\n\n\n# Block 2: Conv + MaxPooling + BatchNormalization\n\n\n# Flatten layer\n\n\n# Fully Connected Layer with Dropout\n\n\n# Output layer untuk binary classification\n\n\n# Menampilkan summary model\nmodel.summary()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ReduceLROnPlateau: Mengurangi learning rate jika model tidak menunjukkan perbaikan\nlearning_rate_reduction = ReduceLROnPlateau(monitor = '', # Metrik yang dipantau untuk mengurangi learning rate jika tidak ada perbaikan\n                                            patience= , # Jumlah epoch tanpa perbaikan sebelum learning rate dikurangi\n                                            factor= ,# Faktor pengurangan learning rate\n                                            min_lr = , # Nilai learning rate terkecil yang diizinkan\n                                            verbose = ) # Menampilkan informasi saat learning rate dikurangi\n\n# EarlyStopping: Menghentikan pelatihan lebih awal jika model tidak menunjukkan perbaikan\nearly_stoping = EarlyStopping(monitor='',  # Metrik yang dipantau untuk menghentikan untuk menghentikan pelatihan jika tidak ada perbaikan\n                              patience= , # Jumlah epoch tanpa perbaikan sebelum pelatihan dihentikan\n                              restore_best_weights= , # Mengembalikan bobot model ke keadaan terbaik setelah pelatihan dihentikan\n                              verbose= ) # Menampilkan informasi jika pelatihan dihentikan lebih awal\n\n# ModelCheckpoint: Menyimpan model terbaik berdasarkan metrik tertentu selama pelatihan\ncheckpoint = ModelCheckpoint(filepath='', # Lokasi untuk menyimpan model terbaik\n                             save_best_only= , # Menyimpan hanya model dengan perbaikan terbaik pada metrik yang dipantau\n                             monitor='') # Metrik yang dipantau untuk menentukan model terbaik","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"model.compile(optimizer='', # Optimizer yang digunakan untuk memperbarui bobot model\n              loss='', # Fungsi loss yang digunakan untuk menghitung kesalahan model\n              metrics=['']) # Metrik yang digunakan untuk mengevaluasi kinerja model","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<a id=\"Train_model\"></a>\n# <p style=\"background-color: #33312b; font-family:calibri; color:white; font-size:140%; font-family:Verdana; text-align:center; border-radius:15px 50px;\">Step 5 | Train the Model</p>","metadata":{}},{"cell_type":"code","source":"cat_dog = model.fit( , # Generator untuk data pelatihan\n                    validation_data = , # Generator untuk data validasi\n                    callbacks=[], # Daftar callback\n                    epochs = , # Jumlah epoch pelatihan\n                   )","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<a id=\"Evaluate_model\"></a>\n# <p style=\"background-color: #33312b; font-family:calibri; color:white; font-size:140%; font-family:Verdana; text-align:center; border-radius:15px 50px;\">Step 6 | Evaluate the Model</p>","metadata":{}},{"cell_type":"code","source":"error = pd.DataFrame(cat_dog.history)\n\nplt.figure(figsize=(18, 5), dpi=200)\nsns.set_style('darkgrid')\n\nplt.subplot(1, 2, 1)\nplt.plot(error['loss'], label='Training Loss', color='blue')\nplt.plot(error['val_loss'], label='Validation Loss', color='orange')\nplt.title('Training and Validation Loss', fontsize=15)\nplt.xlabel('Epochs', fontsize=12)\nplt.ylabel('Loss', fontsize=12)\nplt.legend()\n\nplt.subplot(1, 2, 2)\nplt.plot(error['accuracy'], label='Training Accuracy', color='blue')\nplt.plot(error['val_accuracy'], label='Validation Accuracy', color='orange')\nplt.title('Training and Validation Accuracy', fontsize=15)\nplt.xlabel('Epochs', fontsize=12)\nplt.ylabel('Accuracy', fontsize=12)\nplt.legend()\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"![image.png](attachment:5fe6cffb-76ee-4068-a07b-32d8e1b689f8.png)\n\nKita dapat melakukan berbagai teknik untuk mencegah overfitting/underfitting seperti:\n\n- Menambah layer pada arsitektur CNN\n- Data Augmentation\n- [Layer weight regularizers](https://keras.io/api/layers/regularizers/)\n- [Layer weight constraints](https://keras.io/api/layers/constraints/)\n- [Dropout](https://keras.io/api/layers/regularization_layers/dropout/)\n- [Batch normalization](https://keras.io/api/layers/normalization_layers/batch_normalization/)\n- [EarlyStopping](https://keras.io/api/callbacks/early_stopping/)","metadata":{},"attachments":{"de9e8093-643e-4d9f-8ef2-c365d2df974a.png":{"image/png":"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"},"5fe6cffb-76ee-4068-a07b-32d8e1b689f8.png":{"image/png":"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"}}},{"cell_type":"code","source":"loss,acc = model.evaluate(train_generator,batch_size = bat_size, verbose = 0)\n\nprint('The accuracy of the model for training data is:',acc*100)\nprint('The Loss of the model for training data is:',loss)\n\nloss,acc = model.evaluate(val_generator,batch_size = bat_size, verbose = 0)\n\nprint('The accuracy of the model for validation data is:',acc*100)\nprint('The Loss of the model for validation data is:',loss)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"<a id=\"Prediction\"></a>\n# <p style=\"background-color: #33312b; font-family:calibri; color:white; font-size:140%; font-family:Verdana; text-align:center; border-radius:15px 50px;\">Step 7 | Use Model for Prediction</p>","metadata":{}},{"cell_type":"code","source":"# Muat model yang sudah dilatih\nmodel = keras.models.load_model('file_path') #direktori model disimpan\n\nresult = model.predict(test_generator, batch_size=bat_size, verbose=0)\n\ny_pred = np.argmax(result, axis=1)\n\ny_true = test_generator.classes\n\nloss, acc = model.evaluate(test_generator, batch_size=bat_size, verbose=0)\n\nprint('The accuracy of the model for testing data is: {:.2f}%'.format(acc * 100))\nprint('The Loss of the model for testing data is: {:.4f}'.format(loss))","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"confusion_mtx = confusion_matrix(y_true,y_pred) \nConfusionMatrixDisplay(confusion_mtx, display_labels=['Cat', 'Dog']).plot(cmap='Blues')\n\nplt.title(\"Confusion Matrix\",fontsize=13)\nplt.show()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"images, labels = next(test_generator)\n\npredictions = model.predict(images)\nbinary_predictions = (predictions > 0.5).astype(int).flatten()\n\n# Menampilkan gambar dan prediksinya\nplt.figure(figsize=(12, 12))\nfor i in range(len(images)):\n    plt.subplot(4, 4, i + 1)\n    plt.imshow(images[i])\n    plt.axis('off')\n    \n    pred_label = 'Dog' if binary_predictions[i] == 1 else 'Cat'\n    \n    true_label = 'Cat' if labels[i][0] == 1 else 'Dog'\n    \n    plt.title(f'Pred: {pred_label}\\nTrue: {true_label}', color=\"green\" if pred_label == true_label else \"red\")\n    \n    if i == 15:\n        break\n\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from tensorflow.keras.preprocessing import image\n\ndef prepare_image(img_path):\n    if isinstance(img_path, np.ndarray):  \n        img = img_path\n    else:\n        img = image.load_img(img_path, target_size=(64, 64))\n    img_array = image.img_to_array(img)\n    img_array = np.expand_dims(img_array, axis=0) \n    img_array = img_array / 255.0\n    return img_array\n\ndef predict_image(img_path):\n    img_array = prepare_image(img_path)\n    prediction = model.predict(img_array)\n    class_names = ['Cat', 'Dog'] \n    predicted_class = class_names[np.argmax(prediction)]\n    confidence = np.max(prediction) * 100\n    return predicted_class, confidence\n\ndef display_prediction(img_path):\n    predicted_class, confidence = predict_image(img_path)\n    if isinstance(img_path, str):\n        img = image.load_img(img_path)  \n    else:\n        img = img_path  \n    plt.imshow(img)\n    plt.axis('off')  \n    plt.show()\n\n    print(f\"Predicted Class: {predicted_class}\")\n    print(f\"Confidence: {confidence:.2f}%\")\n\n# Path gambar yang ingin diprediksi, menggunakan path yang sesuai di Kaggle (misalnya, gambar di folder input)\nimg_path = 'file_path'  # Ganti dengan path gambar yang relevan\ndisplay_prediction(img_path)","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}