import os
import numpy as np
import tensorflow as tf
from tensorflow import keras
import cv2
import random

# You can download model from https://uam-my.sharepoint.com/:f:/g/personal/pavbia_st_amu_edu_pl/EmBHjnETuk5LiCZS6xk7AnIBNsnffR3Sygf8EX2bhR1w4A
# Change the path to model + to datasets (string 12 + strings 35,41,47,53)


class VacuumRecognizer:
    model = keras.models.load_model("AI_brain\model.h5")  # Neuron model path

    def recognize(self, image_path) -> str:
        class_names = ["Banana", "Cat", "Earings", "Plant"]

        img = cv2.imread(image_path, flags=cv2.IMREAD_GRAYSCALE)
        cv2.waitKey(0)
        img = np.expand_dims(img, 0)

        predictions = self.model.predict(img)[0].tolist()

        # More information about neuron decision
        # print(img.shape)
        # cv2.imshow("test_show", img)
        # print(class_names)
        # print(predictions)
        # print(max(predictions))
        # print(predictions.index(max(predictions)))

        return class_names[predictions.index(max(predictions))]

    def get_random_dir(self, type) -> str:
        if type == "Plant":
            plant_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Plant"  # Plant dataset path
            plant_dirs = os.listdir(plant_image_paths)
            full_path = (
                plant_image_paths
                + "\\"
                + plant_dirs[random.randint(0, len(plant_dirs) - 1)]
            )
            print(full_path)
            return full_path
        elif type == "Earings":
            earnings_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Earings"  # Earings dataset path
            earning_dirs = os.listdir(earnings_image_paths)
            full_path = (
                earnings_image_paths
                + "\\"
                + earning_dirs[random.randint(0, len(earning_dirs) - 1)]
            )
            print(full_path)
            return full_path
        elif type == "Banana":
            banana_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Banana"  # Banana dataset path
            banana_dirs = os.listdir(banana_image_paths)
            full_path = (
                banana_image_paths
                + "\\"
                + banana_dirs[random.randint(0, len(banana_dirs) - 1)]
            )
            print(full_path)
            return full_path
        elif type == "Cat":
            cat_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Cat"  # Cat dataset path
            cat_dir = os.listdir(cat_image_paths)


# For testing the neuron model
"""image_paths = []
image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Banana')
image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Cat')
image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Earings')
image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Plant')
uio = VacuumRecognizer()

for image_path in image_paths:
    dirs = os.listdir(image_path)
    for i in range(3):
        print(uio.recognize(image_path + '\\' + dirs[random.randint(0, len(dirs)-1)]))"""