#%%

from tensorflow.keras.layers import Input, Lambda, Dense, Flatten,Dropout
from tensorflow.keras.models import Model
from tensorflow.keras.applications.vgg19 import VGG19
from tensorflow.keras.applications.vgg19 import preprocess_input
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
import numpy as np
import pandas as pd
import os
import cv2
import matplotlib.pyplot as plt
# %%
os.chdir(r"C:\Users\pittb\Documents\biedronkapp\BiedronkApp\biedronki_dataset")
train_path="train"
test_path="test"
# %%
x_train=[]

for folder in os.listdir(train_path):

    sub_path=train_path+"/"+folder

    for img in os.listdir(sub_path):
        image_path=sub_path+"/"+img

        img_arr=cv2.imread(image_path)

        img_arr=cv2.resize(img_arr,(224,224))

        x_train.append(img_arr)

x_test=[]

for folder in os.listdir(test_path):

    sub_path=test_path+"/"+folder

    for img in os.listdir(sub_path):

        image_path=sub_path+"/"+img

        img_arr=cv2.imread(image_path)

        img_arr=cv2.resize(img_arr,(224,224))

        x_test.append(img_arr)
# %%
train_x=np.array(x_train)
test_x=np.array(x_test)

train_x=train_x/255.0
test_x=test_x/255.0
# %%
train_datagen = ImageDataGenerator(rescale = 1./255)
test_datagen = ImageDataGenerator(rescale = 1./255)
# %%
training_set = train_datagen.flow_from_directory(train_path,
                                                 target_size = (224, 224),
                                                 batch_size = 32,
                                                 class_mode = 'sparse')
test_set = test_datagen.flow_from_directory(test_path,
                                            target_size = (224, 224),
                                            batch_size = 32,
                                            class_mode = 'sparse')
# %%
train_y=training_set.classes
test_y=test_set.classes

training_set.class_indices

train_y.shape,test_y.shape
# %%
IMAGE_SIZE = [224, 224]

vgg = VGG19(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)

for layer in vgg.layers:
    layer.trainable = False

# %%
# our layers - you can add more if you want
x = Flatten()(vgg.output)

prediction = Dense(2, activation='softmax')(x)
# %%
# create a model object
model = Model(inputs=vgg.input, outputs=prediction)

# view the structure of the model
model.summary()
# %%
model.compile(
  loss='sparse_categorical_crossentropy',
  optimizer="adam",
  metrics=['accuracy']
)
# %%
from tensorflow.keras.callbacks import EarlyStopping
early_stop=EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=5)
# %%
history = model.fit(
  train_x,
  train_y,
  epochs=10,
  callbacks=[early_stop],
  batch_size=32,shuffle=True)
# %%
plt.plot(history.history['loss'], label='train loss')
plt.legend()

plt.savefig('vgg-loss-rps-1.png')
plt.show()
# %%
# accuracies
plt.plot(history.history['accuracy'], label='train acc')
plt.legend()

plt.savefig('vgg-acc-rps-1.png')
plt.show()
# %%
model.evaluate(test_x,test_y,batch_size=6)
# %%
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
import numpy as np

y_pred=model.predict(test_x)
y_pred=np.argmax(y_pred,axis=1)

accuracy_score(y_pred,test_y)

print(classification_report(y_pred,test_y))
# %%