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from keras.layers import Dense, Input, Dropout, GlobalAveragePooling2D, Flatten, Conv2D, BatchNormalization, Activation, MaxPooling2D
from keras.models import Model, Sequential
from keras.optimizers import Adam

# number of possible label values
nb_classes = 7

# Initialising the CNN
model = Sequential()

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#imported libs
import numpy as np
import pandas as pd
from xgboost import XGBClassifier
import matplotlib.pyplot as plt
from scipy import stats
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

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import cv2
import numpy as np
import time
import os
from face_detection import *
from buzzer import alertor, stopAlertor

global thres
thres = 0

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from face_detection import *
label = input('What is your name?')
label = label.strip().lower()
print('We will be taking 5 pictures! Smile when you are ready!')
cap = cv2.VideoCapture(0) # video capture source camera (Here webcam of laptop) 
count = 0
print('Press on y to select the current photo')
while(True):
	ret,frame = cap.read() # return a single frame in variable `frame`
	rannumb = random.randint(1,100000000)

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def data():
    """
    Data providing function:

    This function is separated from create_model() so that hyperopt
    won't reload data for each evaluation run.
    """
    #Encodings
    encode = {0:'Attire',
              1:'Decorationandsignage',

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def data():
    """
    Data providing function:
    This function is separated from create_model() so that hyperopt
    won't reload data for each evaluation run.
    """
    #Encodings
    encode = {0:'Attire',
              1:'Decorationandsignage',
              2:'Food',

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def create_model(X_train,y_train,X_test,y_test):
    # Initialising the CNN
    model = Sequential()

    # 1 - Convolution
    model.add(Conv2D(64,(3,3), padding='same', input_shape=(48, 48,1)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout({{uniform(0,1)}}))

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    best_run, best_model = optim.minimize(model=create_model,
                                            data=data,
                                            algo=tpe.suggest,
                                            max_evals=5,
                                            trials=Trials(),
                                            notebook_name='gala_recog')
    
    # Evaluate the model on the test data using `evaluate`
    print('\n# Evaluate on test data')
    results = best_model.evaluate(X_test, y_test, batch_size=128)

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def array_to_color(array, cmap="Oranges"):
    s_m = plt.cm.ScalarMappable(cmap=cmap)
    return s_m.to_rgba(array)[:,:-1]


def rgb_data_transform(data):
    data_t = []
    for i in range(data.shape[0]):
        data_t.append(array_to_color(data[i]).reshape(16, 16, 16, 3))
    return np.asarray(data_t, dtype=np.float32)

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with h5py.File("./full_dataset_vectors.h5", "r") as hf:    

    # Split the data into training/test features/targets
    X_train = hf["X_train"][:]
    targets_train = hf["y_train"][:]
    X_test = hf["X_test"][:] 
    targets_test = hf["y_test"][:]

    # Determine sample shape
    sample_shape = (16, 16, 16, 3)