Upload Model

app.py

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+import streamlit as st
+import eda
+import prediction
+
+
+navigation = st.sidebar.selectbox('Pilih Halaman', ('EDA', 'Predict'))
+
+
+if navigation == 'EDA':
+    eda.run()
+else:
+    prediction.run()

eda.py

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+import streamlit as st
+import os
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import plotly.express as px
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.preprocessing import image
+
+st.set_page_config(page_title='Fast Food Classification Dataset Analysis', layout='wide', initial_sidebar_state='expanded')
+
+def run():
+
+    # Buat Title
+    st.title('EDA on Fast Food Classification')
+
+    # Buat Deskripsi
+    st.subheader('Written by Franciscus Andrew Sunanda, FTDS-RMT-018')
+
+    st.markdown('---')
+
+    
+    st.write('Dataset         : Fast Food Classification')
+
+    st.write('Objective       : To create a model that can predict the type of a fast food based on image')
+    
+
+    st.markdown('---')
+    main_path= 'D:\\tugas_andrew_DS\\phase_2\\m2\\food'
+    
+    # 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('---')
+
+    
+
+    
+if __name__ == '__main__':
+    run()

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:914ea06afe62dda6b724ec2eb65a6ce9881c6686d7983f4aefbb3694245228b9
+size 4719152

prediction.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from PIL import Image
+import os
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.models import load_model
+
+
+# Load the Models
+
+model = load_model('model.h5')
+img_size = (64, 64)
+# Define a function to preprocess the input image
+def preprocess_input_image(img_path):
+    img = image.load_img(img_path, target_size=img_size)
+    img1 = image.load_img(img_path)
+    x = image.img_to_array(img)
+    x = np.expand_dims(x, axis=0)
+    x /= 255.
+    return x, img1
+
+main_path= 'D:\\tugas_andrew_DS\\phase_2\\m2\\food'
+    
+# 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'
+)
+
+   
+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)
+
+def run():
+
+    st.title('Fast Food Image Prediction')
+
+
+    with st.form(key='form_food'):
+        uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
+        
+        submitted = st.form_submit_button('Predict')
+
+
+    
+    if submitted:
+        # Transform Inference-Set
+        if uploaded_file is not None:
+            # Preprocess the input image
+            img = Image.open(uploaded_file)
+            x = np.array(img.resize(img_size))/255.
+            x = np.expand_dims(x, axis=0)
+        
+        # Make predictions on the input image
+        preds = model.predict(x, verbose=0)
+        pred_class = np.argmax(preds)
+        pred_class_name = class_names[pred_class]
+        
+        # Display the input image and prediction result
+        st.image(img, caption=f"Predicted Class: {pred_class_name}", use_column_width=True)
+
+
+
+if __name__ == '__main__':
+    run()