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def getKeyPts(imgpath):
       import cv2
       import mediapipe     #uses RGB
       import time

       modelPose = mediapipe.solutions.pose
       mpDraw = mediapipe.solutions.drawing_utils
       pose = modelPose.Pose()
       mp_holistic = mediapipe.solutions.holistic
       #visit media pipe for landmark labels

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def get_predictions(n):
    
    image1= validgen[0][0][n]
    
    plt.imshow(image1)
    input_arr = ks.preprocessing.image.img_to_array(validgen[0][0][n])
    input_arr = np.array([input_arr])  # Convert single image to a batch.
    predictions = model_com01[0].predict_classes(input_arr)
    
    return predictions

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def compiler2(model,train_generator,valid_generator,epchs,bsize=32,lr=0.0001):

    from tensorflow import keras as ks
    callbck = ks.callbacks.EarlyStopping(monitor='val_loss',patience=10,
                                         verbose=2,
                                         restore_best_weights=True,) 
    
    opt = ks.optimizers.Adam(learning_rate=lr)
    
    model.compile(loss="categorical_crossentropy",

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def imageclf2(input_shape):
    from tensorflow import keras as ks
    #from tensorflow.keras import regularizers
    model = ks.models.Sequential()
    #building architecture
    #Adding layers
    model.add(ks.layers.Conv2D(8,(3,3),
                               strides=1,
                               activation="relu",
                               padding='same',

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#Function that can build a dataframe on passing folderpath.
def getdata(folder_path):
    sig = pd.DataFrame(columns=['image_abs_path','image_labels'])
    for key,value in labelnames.items():
        #print("processing for label: {}".format(label))
        label_i = folder_path+"/"+str(key)
        #read directory
        dirs_label_i =  os.listdir(label_i)
        idx = 0
        for image in dirs_label_i:

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def datapreprocessing(main_dir,bsize):
    from tensorflow.keras.preprocessing.image import ImageDataGenerator
    
    train_gen = ImageDataGenerator(rescale=1.0/255,
                                   zoom_range=0.2,
                                   shear_range=0.1,
                                   horizontal_flip=True,
                                   vertical_flip=True,
                                   rotation_range=20,
                                   width_shift_range=0.2,

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def get_predictions(n):
    image1= validgen[0][0][n]
    #print(image1.shape)
    plt.imshow(image1)
    input_arr = tf.keras.preprocessing.image.img_to_array(validgen[0][0][n])
    input_arr = np.array([input_arr])  # Convert single image to a batch.
    predictions = model01[0].predict_classes(input_arr)
    #our dictionary starts from 1 whereas model has classes from 0.
    return insect_names[str(predictions[0]+1)]

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def compiler(model,train_generator,valid_generator,epchs,bsize,lr=0.0001):

    from tensorflow import keras as ks
    callbck = ks.callbacks.EarlyStopping(monitor='val_loss',patience=20,
                                         verbose=2,restore_best_weights=True) 
    #red_lr= ReduceLROnPlateau(monitor='val_acc',patience=3,verbose=1,factor=0.1)
    opt = ks.optimizers.Adam(learning_rate=lr)
    
    model.compile(loss="categorical_crossentropy",
                      optimizer=opt,

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def insectclf(input_shape):
    from tensorflow import keras as ks
    #from tensorflow.keras import regularizers
    model = ks.models.Sequential()
    #building architecture
    #Adding layers
    model.add(ks.layers.Conv2D(16,(3,3),
                               strides=1,
                               activation="relu",
                               padding='valid',

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def datapreprocessing(main_dir,bsize):
    from tensorflow.keras.preprocessing.image import ImageDataGenerator
    
    train_gen = ImageDataGenerator(rescale=1.0/255,
                                  validation_split=0.30,
                                  rotation_range=40,
                                  horizontal_flip=True,
                                  fill_mode='nearest')

    train_generator = train_gen.flow_from_directory(