#%%
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
import keras

# %%
os.chdir(r"C:\Users\pittb\Documents\biedronkapp_2.0\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)
# %%
model = keras.models.load_model('saved_model/my_model')

# %%
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))


# %%
img_path = r"test\azjatyckie\Asian_lady_beetle-(Harmonia-axyridis).jpg"

img_nump = np.array(cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB))

plt.imshow(img_nump)
plt.show()

img_nump = cv2.resize(img_nump,(224,224))
img_nump = img_nump/255.0
img_nump = img_nump[np.newaxis,...]

pr = model.predict(img_nump)
print('azjatycka: '+str(pr[0][0])+' , siedmiokropka: '+str(pr[0][1]))
print(['azjatycka' if np.argmax(pr,axis=1)[0]==0 else 'siedmiokropka'][0])
# %%