import tensorflow as tf
from tensorflow.keras import layers
import cv2
import numpy as np

def create_model():
    baseModel =  tf.keras.applications.efficientnet.EfficientNetB0(include_top=False, weights='imagenet')
    baseModel.trainable = False

    inputs = layers.Input(shape=(224, 224, 3), name="input_layer")

    x = baseModel(inputs)
    x = layers.AveragePooling2D(pool_size=(2, 2))(x)
    x = layers.Flatten(name='Flatten')(x)
    x = layers.Dense(units=128, activation='relu')(x)
    x = layers.Dropout(rate=0.5)(x)
    outputs = layers.Dense(units=1, activation='sigmoid')(x)

    model = tf.keras.Model(inputs, outputs)
    
    initial_learning_rate = 0.001

    model.compile(loss='binary_crossentropy',
                  optimizer=tf.keras.optimizers.Adam(learning_rate=initial_learning_rate),
                  metrics = ['AUC'])
    
    return model

def get_optimal_font_scale(text, width):
    for scale in np.arange(1,0.1,-0.2):
        scale = round(scale,2)
        textSize = cv2.getTextSize(text, fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=scale, thickness=1)
        new_width = textSize[0][0]
        if (new_width <= width):
            return scale
    return 0.1