--- a
+++ b/model.py
@@ -0,0 +1,91 @@
+from keras.layers import Dense, Flatten
+from keras.models import Model
+from keras.applications.vgg16 import VGG16
+from keras.preprocessing.image import ImageDataGenerator
+
+from glob import glob
+import matplotlib.pyplot as plt
+
+# re-size all the images to this
+IMAGE_SIZE = [224, 224]
+
+train_path = 'dataset/TRAIN'
+valid_path = 'dataset/TEST'
+
+# add preprocessing layer to the front of VGG
+vgg = VGG16(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)
+
+# don't train existing weights
+for layer in vgg.layers:
+  layer.trainable = False
+  
+
+#useful for getting number of classes
+folders = glob('dataset/TRAIN/*')
+  
+
+# our layers - you can add more if you want
+x = Flatten()(vgg.output)
+
+#add the sigmoid as the activation function
+prediction = Dense(1, activation='sigmoid')(x)
+
+# create a model object
+model = Model(inputs=vgg.input, outputs=prediction)
+
+# view the structure of the model
+model.summary()
+
+# tell the model what cost and optimization method to use
+model.compile(
+  loss='binary_crossentropy',
+  optimizer='adam',
+  metrics=['accuracy']
+)
+
+
+train_datagen = ImageDataGenerator(rescale = 1./255,
+                                   shear_range = 0.2,
+                                   zoom_range = 0.2,
+                                   horizontal_flip = True)
+
+test_datagen = ImageDataGenerator(rescale = 1./255)
+
+training_set = train_datagen.flow_from_directory('dataset/TRAIN',
+                                                 target_size = (224, 224),
+                                                 batch_size = 64,
+                                                 class_mode = 'binary')
+
+test_set = test_datagen.flow_from_directory('dataset/TEST',
+                                            target_size = (224, 224),
+                                            batch_size = 64,
+                                            class_mode = 'binary')
+
+# see which class represents 1 and which represents 0
+training_set.class_indices
+
+# fit the model
+r = model.fit_generator(
+  training_set,
+  validation_data=test_set,
+  epochs=4,
+  steps_per_epoch=len(training_set),
+  validation_steps=len(test_set)
+)
+# loss plots
+plt.plot(r.history['loss'], label='train loss')
+plt.plot(r.history['val_loss'], label='val loss')
+plt.legend()
+plt.show()
+plt.savefig('LossVal_loss')
+
+# accuracy plots
+plt.plot(r.history['accuracy'], label='train acc')
+plt.plot(r.history['val_accuracy'], label='val acc')
+plt.legend()
+plt.show()
+plt.savefig('AccVal_acc')
+
+#save our model in order to use it in web development
+#model.save('Esophageal_model.h5')
+