import cv2
import numpy as np
import keras.models as models

IMAGE_SIZE = (224, 224)
class_names = ['Tubercolosi', 'No_Tubercolosi', 'Pneumonia', 'No_Pneumonia']
BASE_PATH = './data/'
MODEL = BASE_PATH + 'model/'

def image_predict(numpy_image, model_path):
    image = cv2.resize(numpy_image, IMAGE_SIZE)
    images = []
    images.append(image)
    images = np.array(images, dtype='float32')
    model = models.load_model(model_path, compile=False)
    predictions = model.predict(images)    # Vector of probabilities
    #pred_labels = np.argmax(predictions, axis= 1)
    return predictions#, pred_labels

def image_classification(numpy_image):
    model = MODEL + 'medical-image-classification.h5' 
    labels = image_predict(numpy_image, model)
    response = {}
    for i, label in enumerate(labels[0]):
        response[class_names[i]] = "%.4f" % float(label)
    return response