app.py

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator

import matplotlib.pyplot as plt


import gradio as gr


def classify_image(inp):
    # Dataset link: https://www.kaggle.com/pranavraikokte/covid19-image-dataset


#import app


    batch = 4

    generator = ImageDataGenerator(
            rotation_range=40,
            width_shift_range=0.2,
            height_shift_range=0.2,
            rescale=1./255,
            shear_range=0.2,
            zoom_range=0.2,
            horizontal_flip=True,
            fill_mode='nearest')

    train_iterator = generator.flow_from_directory("C:/Users/Lyall Stewart/Documents/Coding/NeuralNetwork/COVID-19 Classification/data/train",
                                               batch_size=batch,
                                               color_mode='grayscale',
                                               class_mode='sparse')
    test_iterator = generator.flow_from_directory("C:/Users/Lyall Stewart/Documents/Coding/NeuralNetwork/COVID-19 Classification/data/test",
                                              batch_size=batch,
                                              color_mode='grayscale',
                                              class_mode='sparse')


    def design_model():
        model = Sequential()
        model.add(tf.keras.Input(shape=(256, 256, 1)))

        model.add(tf.keras.layers.Conv2D(2, 5, strides=3, activation="relu"))
        model.add(tf.keras.layers.MaxPooling2D(pool_size=(5, 5), strides=(5,5)))
        model.add(tf.keras.layers.Conv2D(4, 3, strides=1, activation="relu"))
        model.add(tf.keras.layers.MaxPooling2D(pool_size=(3,2), strides=(2,2)))

        model.add(tf.keras.layers.Flatten())
    #model.add(tf.keras.layers.Dense(8, activation="relu"))
    #model.add(tf.keras.layers.Dropout(.20))
        model.add(tf.keras.layers.Dense(4, activation='softmax'))

        model.summary()

        callback = tf.keras.callbacks.EarlyStopping(monitor='accuracy', patience=5, restore_best_weights=True, verbose=1)

        print("Model designed")
        return model, callback


    model, es_callback = design_model()

    model.compile(loss='sparse_categorical_crossentropy',
              optimizer=keras.optimizers.Adam(learning_rate=0.01),
              metrics=['accuracy'])

    history = model.fit_generator(train_iterator,
                              epochs=50,
                              steps_per_epoch=50,
                              validation_data=test_iterator,
                              validation_steps=50,
                              callbacks=[es_callback],
                              verbose=1)

    plt.plot(history.history['accuracy'])
    plt.title('model accuracy')
    plt.ylabel('accuracy')
    plt.xlabel('epoch')
    plt.legend(['train'], loc='upper left')
    plt.show()

title = "Gradio Image Classifiction + Interpretation Example"
gr.Interface(
    fn=classify_image, inputs=image, outputs=label, interpretation="default", title=title
).launch()