import pickle

import cv2

import numpy as np

import tensorflow as tf

from tensorflow.keras.models import load_model

# Process image and predict label

from tensorflow.python.keras.preprocessing.image import img_to_array

from class_names import prediction_classs

IMG_SIZE = 256

LEARNING_RATE = 0.001

COLOUR_MAP = 3

classes = prediction_classs

# Evaluate the model

def evaluate_image(model_path, class_path, valididation_path, image_size):

    # Load the model

    model = tf.keras.models.load_model(model_path)

    # Load classes

    with open(class_path, 'rb') as file:

        classes = pickle.load(file)

    # Get a list of categories

    image = cv2.imread(valididation_path)

    # Get input reshaped and rescaled

    image = cv2.resize(image, (image_size, image_size))

    image_f = image.astype("float") / 255.0

    image_a = img_to_array(image_f)

    image_p = np.expand_dims(image_a, axis=0)

    # Get predictions

    predictions = model.predict(image_p).ravel()

    # Print predictions

    print(predictions)

    # Get the class with the highest probability

    prediction = np.argmax(predictions)

    # Check if the prediction is correct

    return classes[prediction]

def processImg(img_path):

    return evaluate_image(model_path=r'/Users/sikarwar07/PycharmProjects/MLPredictModelFlask/models'

                                     r'/NASNet_32B_331x331.h5',

                          class_path=r'/Users/sikarwar07/PycharmProjects/MLPredictModelFlask/models'

                                     r'/NASNet_32B_331x331.pkl',

                          valididation_path=img_path,

                          image_size=331)

# TODO Future implantation

# Create image prediction in tabular format

#     # Read image

#     model = load_model("models/resnet152_class_23_epoch_50.h5")

#     loss = tf.keras.losses.CategoricalCrossentropy()

#     optimizer = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE)

#     model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])

#     image = cv2.imread(r"D:\test_data/" + category + '/' + name)

#     # Preprocess image

#     image = cv2.imread(IMG_PATH, 1)

#     image = cv2.resize(image, (IMG_SIZE, IMG_SIZE))

#     image = image.astype("float") / 255.0

#     image = img_to_array(image)

#     image = np.expand_dims(image, axis=0)

#     predictions = model.predict(image).ravel()

#     # Print predictions

#     print(predictions)

#     # Get the class with the highest probability

#     prediction = np.argmax(predictions)

#     # Check if the prediction is correct

#     correct = True if classes[prediction].lower() == category else False

#     # Draw the image and show the best prediction

#     image = cv2.resize(image, (256, 256))

#     cv2.putText(image, '{0}: {1} %'.format(classes[prediction], str(round(predictions[prediction] * 100, 2))),

#                 (12, 22), cv2.FONT_HERSHEY_DUPLEX, 0.7, (0, 0, 0), 2)

#     cv2.putText(image, '{0}: {1} %'.format(classes[prediction], str(round(predictions[prediction] * 100, 2))),

#                 (10, 20), cv2.FONT_HERSHEY_DUPLEX, 0.7, (65, 105, 225), 2)

#     cv2.putText(image, '{0}'.format('CORRECT!' if correct else 'WRONG!'), (12, 50), cv2.FONT_HERSHEY_DUPLEX, 0.7,

#                 (0, 0, 0), 2)

#     cv2.putText(image, '{0}'.format('CORRECT!' if correct else 'WRONG!'), (10, 48), cv2.FONT_HERSHEY_DUPLEX, 0.7,

#                 (0, 255, 0) if correct else (0, 0, 255), 2)

#

#     # Append the image

#     blocks.append(image)

#

#

# # Display images and predictions

# row1 = np.concatenate(blocks[0:3], axis=1)

# row2 = np.concatenate(blocks[3:6], axis=1)

# # cv2.imshow('Predictions', np.concatenate((row1, row2), axis=0))

# cv2.imwrite('D:/New folder/New folderpredictions_renet50.jpg', np.concatenate((row1, row2), axis=0))

# cv2.waitKey(0)