import tensorflow as tf
import pygad
import numpy
from imageMulticlassClassification import ImageMulticlassClassification

def fitness_func(solution, solution_idx):
    try:
        print("solution_idx :",solution_idx)
        print("solution :",solution)
        neuronDense1 = [16, 32, 64, 128, 256, 512, 1024, 2048]
        neuronDense2 = [16, 32, 64, 128, 256, 512, 1024, 2048]
        Dropout1 = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]
        Dropout2 = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]
        Batchs = [16, 32, 64, 128, 256, 512, 1024, 2048]
        Activations = ["relu", "sigmoid", "softplus", "softsign", "tanh", "selu", "gelu", "linear"]
        Optimizers = ["Adam", "RMSprop", "SGD", "Adadelta", "Adagrad", "Adamax", "Ftrl", "Nadam"]
        LossFunction = ["SparseCategoricalCrossentropy", "CategoricalCrossentropy", "BinaryCrossentropy", "MeanAbsoluteError", "MeanSquaredError", "SquaredHinge", "CategoricalHinge", "CosineSimilarity"]
        usedNeuronDense1 = neuronDense1[solution[0]]
        print("==================================")
        print(f"usedNeuronDense1 : {usedNeuronDense1}")
        usedNeuronDense2 = neuronDense2[solution[1]]
        print(f"usedNeuronDense2 : {usedNeuronDense2}")
        usedDropout1 = Dropout1[solution[2]]
        print(f"usedDropout1 : {usedDropout1}")
        usedDropout2 = Dropout2[solution[3]]
        print(f"usedDropout2 : {usedDropout2}")
        usedBatchs = Batchs[solution[4]]
        print(f"usedBatchs : {usedBatchs}")
        usedActivations = Activations[solution[5]]
        print(f"usedActivations : {usedActivations}")
        usedOptimizers = Optimizers[solution[6]]
        print(f"usedOptimizers : {usedOptimizers}")
        usedLossFunction = LossFunction[solution[7]]
        print(f"usedLossFunction : {usedLossFunction}")
        print("==================================")

        imgWidth=50
        imgHeight=50
        batchSize=usedBatchs
        IMC = ImageMulticlassClassification(imgWidth,imgHeight,batchSize)
        IMC.data_MakeDataset(datasetUrl="https://huggingface.co/datasets/S1223/HandGestureDataset/resolve/main/HandGestureDataset.tgz",datasetDirectoryName="HandGestureDataset", ratioValidation=0.20)
        IMC.data_PreprocessingDataset()
        customModel = tf.keras.Sequential()
        customModel.add(tf.keras.layers.Conv2D(16, (3, 3), input_shape=(imgWidth, imgHeight, 3), activation=usedActivations))
        customModel.add(tf.keras.layers.Conv2D(16, (3, 3), activation=usedActivations))
        customModel.add(tf.keras.layers.Dropout(usedDropout1))
        customModel.add(tf.keras.layers.MaxPooling2D((2, 2)))
        customModel.add(tf.keras.layers.Flatten())
        customModel.add(tf.keras.layers.BatchNormalization())
        customModel.add(tf.keras.layers.Dense(usedNeuronDense1, activation=usedActivations))
        customModel.add(tf.keras.layers.Dense(usedNeuronDense2, activation=usedActivations))
        customModel.add(tf.keras.layers.Dropout(usedDropout2))
        customModel.add(tf.keras.layers.Dense(10, activation="softmax"))
        IMC.model_make(customModel) 
        modelName = ""
        for x in solution:
            modelName += f"{str(x)}_"
        IMC.training_model(epochs=50, modelName=modelName, optimizer=usedOptimizers, lossFunction=usedLossFunction)
        IMC.evaluation(labelName=["0","1","2","3","4","5","6","7","8","9"])
        output = float(IMC.history.history["val_accuracy"][-1])
        # output = numpy.max(solution)
        # print(output)
        # print(type(output))
        print("fitness :",output)
        fitness = output
        return fitness
    except Exception as e:
        print(str(e))
        return 0.00001

function_inputs = [1,2,3,4,5,6,7,8]
desired_output = 5

fitness_function = fitness_func

num_generations = 1
num_parents_mating = 4

sol_per_pop = 10
num_genes = len(function_inputs)

init_range_low = 0
init_range_high = 8

parent_selection_type = "rws"
keep_parents = 1

crossover_type = "single_point"

mutation_type = "swap"
mutation_percent_genes = 'default'

ga_instance = pygad.GA(num_generations=num_generations,
                       num_parents_mating=num_parents_mating,
                       fitness_func=fitness_function,
                       sol_per_pop=sol_per_pop,
                       num_genes=num_genes,
                       init_range_low=init_range_low,
                       init_range_high=init_range_high,
                       parent_selection_type=parent_selection_type,
                       keep_parents=keep_parents,
                       crossover_type=crossover_type,
                       mutation_type=mutation_type,
                       mutation_percent_genes=mutation_percent_genes,
                       gene_type=[int, int, int, int, int, int, int, int],
                       allow_duplicate_genes=False, 
                       save_best_solutions=False, 
                       save_solutions=False)
print("Initial Population")
print(ga_instance.initial_population)
print(ga_instance.run())
solution, solution_fitness, solution_idx = ga_instance.best_solution()
print("Parameters of the best solution : {solution}".format(solution=solution))
print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))

# from whatsapp import FWA
# fwa = FWA(APIkey="b2d95af932eedb4de92b3496f338aa5f97b36ae0", NoSender="6285157853522", host="http://wa.fianjulio.web.id:81")
# print(fwa.sendTextMessage(phoneNumber="082136815488", message="Training selesai"))