Upload 2 files

eda.py

@@ -7,9 +7,6 @@ import matplotlib.pyplot as plt
 import plotly.express as px
 from tensorflow.keras.preprocessing.image import ImageDataGenerator
 from tensorflow.keras.preprocessing import image
-from datasets import load_dataset
-from torchvision import transforms
-from torch.utils.data import DataLoader
 
 st.set_page_config(page_title='Fast Food Classification Dataset Analysis', layout='wide', initial_sidebar_state='expanded')
 
@@ -30,11 +27,105 @@ def run():
     
 
     st.markdown('---')
+    main_path= 'D:\\tugas_andrew_DS\\phase_2\\m2\\food'
     
-    dataset_path = "andrewsunanda/fast_food_image_classification"
-    st.write(os.listdir(dataset_path))
-    
-    #
+    # Define batch size and image size
+    batch_size = 256
+    img_size = (64, 64)
+    # Define paths to the data folders
+    train_path = os.path.join(main_path, 'Train')
+    valid_path = os.path.join(main_path, 'Valid')
+    test_path = os.path.join(main_path, 'Test')
+    # Create data generators for training, validation, and testing
+    train_datagen = ImageDataGenerator(
+        rescale=1./255, 
+        horizontal_flip=True
+    )
+
+    valid_datagen = ImageDataGenerator(
+        rescale=1./255
+    )
+    test_datagen = ImageDataGenerator(
+        rescale=1./255
+    )
+
+    train_generator = train_datagen.flow_from_directory(
+    train_path, 
+    target_size=img_size, 
+    batch_size=batch_size, 
+    class_mode='categorical'
+    )
+
+    valid_generator = valid_datagen.flow_from_directory(
+        valid_path, 
+        target_size=img_size, 
+        batch_size=batch_size, 
+        class_mode='categorical'
+    )
+
+    test_generator = test_datagen.flow_from_directory(
+        test_path,                                                 
+        target_size=img_size, 
+        batch_size=batch_size, 
+        class_mode='categorical'
+    )
+
+    st.write('## Showing Random Samples')
+    class_names = list(train_generator.class_indices.keys())
+    train_classes = pd.Series(train_generator.classes)
+    test_classes = pd.Series(test_generator.classes)
+    valid_classes = pd.Series(valid_generator.classes)
+    # Plot some samples from each class
+    fig, ax = plt.subplots(nrows=2, ncols=5, figsize=(10, 6), subplot_kw={'xticks': [], 'yticks': []})
+    for i, axi in enumerate(ax.flat):
+        img = plt.imread(f'{train_path}/{class_names[i]}/{os.listdir(train_path+"/"+class_names[i])[0]}')
+        axi.imshow(img)
+        axi.set_title(class_names[i])
+    plt.tight_layout()
+    st.pyplot(fig)
+
+
+    st.markdown('---')
+
+    st.write('## Balance Classification')
+
+   # Create a pandas dataframe to show the distribution of classes in train, test, and validation data
+    df = pd.concat([train_classes.value_counts(), test_classes.value_counts(), valid_classes.value_counts()], axis=1)
+    df.columns = ['Training Data', 'Test Data', 'Validation Data']
+    df.index = class_names
+
+    fig, ax = plt.subplots(figsize=(12, 6))
+    df.plot(kind='bar', stacked=False, ax=ax, width=0.8)
+    plt.xlabel('Class')
+    plt.ylabel('Data Distribution')
+    plt.title('Data Distribution for each class')
+    plt.xticks(rotation=45, ha='right')
+    st.pyplot(fig)
+
+
+    st.markdown('---')
+
+    st.write('## Mean Pixel Value')
+
+    # Plot the mean of pixel mean of each channel for each class (unstacked bar chart)
+    means = []
+    for i in range(len(class_names)):
+        class_name = class_names[i]
+        img_path = os.path.join(train_path, class_name, os.listdir(os.path.join(train_path, class_name))[0])
+        img = image.load_img(img_path, target_size=img_size)
+        img_array = image.img_to_array(img)
+        means.append(np.mean(img_array, axis=(0, 1)))
+    means_df = pd.DataFrame(means, columns=['Red', 'Green', 'Blue'])
+    means_df.index = class_names
+    fig, ax = plt.subplots(figsize=(12, 6))
+    means_df.plot(kind='bar', stacked=False, ax=ax, width=0.8)
+    plt.xlabel('Class')
+    plt.ylabel('Mean pixel value')
+    plt.title('Mean pixel value of each channel for each class')
+    plt.xticks(rotation=45, ha='right')
+    st.pyplot(fig)
+
+    st.markdown('---')
 
     
 

prediction.py

@@ -5,9 +5,6 @@ from PIL import Image
 import os
 from tensorflow.keras.preprocessing.image import ImageDataGenerator
 from tensorflow.keras.models import load_model
-from datasets import load_dataset
-from torchvision import transforms
-from torch.utils.data import DataLoader
 
 
 # Load the Models
@@ -23,50 +20,15 @@ def preprocess_input_image(img_path):
     x /= 255.
     return x, img1
 
-   
+main_path= 'D:\\tugas_andrew_DS\\phase_2\\m2\\food'
     
-import os
-import torch
-import torchvision.transforms as transforms
-from torch.utils.data import DataLoader
-from datasets import load_dataset
-
-# Define the path to the dataset
-dataset_path = 'andrewsunanda/fast_food_image_classification'
-
-# Load the dataset from Hugging Face
-dataset = load_dataset(dataset_path)
-
-# Define the batch size and image size
+# Define batch size and image size
 batch_size = 256
 img_size = (64, 64)
-
-# Define the paths to the train, validation, and test folders
-train_path = os.path.join(dataset_path, 'Train')
-valid_path = os.path.join(dataset_path, 'Valid')
-test_path = os.path.join(dataset_path, 'Test')
-
-# Define the transforms for the dataset
-transform = transforms.Compose([
-    transforms.Resize(img_size),
-    transforms.ToTensor(),
-])
-
-# Load the training dataset
-train_dataset = dataset['train']
-train_dataset = train_dataset.map(lambda x: {'image': transform(x['image']), 'label': x['label']})
-train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
-
-# Load the validation dataset
-valid_dataset = dataset['validation']
-valid_dataset = valid_dataset.map(lambda x: {'image': transform(x['image']), 'label': x['label']})
-valid_loader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)
-
-# Load the testing dataset
-test_dataset = dataset['test']
-test_dataset = test_dataset.map(lambda x: {'image': transform(x['image']), 'label': x['label']})
-test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
-
+# Define paths to the data folders
+train_path = os.path.join(main_path, 'Train')
+valid_path = os.path.join(main_path, 'Valid')
+test_path = os.path.join(main_path, 'Test')
 
 # Create data generators for training, validation, and testing
 train_datagen = ImageDataGenerator(