Update app.py

app.py

@@ -3,22 +3,83 @@ import tensorflow as tf
 
 import gradio as gr
 
-inception_net = tf.keras.applications.MobileNetV2()  # load the model
 
-# Download human-readable labels for ImageNet.
-response = requests.get("https://git.io/JJkYN")
-labels = response.text.split("\n")
+def classify_image(inp):
+    # Dataset link: https://www.kaggle.com/pranavraikokte/covid19-image-dataset
 
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
 
-def classify_image(inp):
-    inp = inp.reshape((-1, 224, 224, 3))
-    inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
-    prediction = inception_net.predict(inp).flatten()
-    return {labels[i]: float(prediction[i]) for i in range(1000)}
+import matplotlib.pyplot as plt
+#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)
 
-image = gr.Image(shape=(224, 224))
-label = gr.Label(num_top_classes=3)
+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(