import sys

sys.path.append('../')

from config import models, index_to_label, acronyms_to_entities, MAX_LENGTH

import tensorflow as tf

from scripts.utils import predict

from keras import backend as K

def precision(y_true, y_pred):

    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))

    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))

    _precision = true_positives / (predicted_positives + K.epsilon())

    return _precision

def recall(y_true, y_pred):

    """Compute recall metric"""

    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))

    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))

    return true_positives / (possible_positives + K.epsilon())

def f1_score(y_true, y_pred):

    """Compute f1-score metric"""

    _precision = precision(y_true, y_pred)

    _recall = recall(y_true, y_pred)

    f1_score = 2 * ((_precision * _recall) / (_precision + _recall + K.epsilon()))

    return f1_score

def NER(model_name, text):

    # Print the arguments

    print("Model provided: ", models[model_name]['title'])

    model_path = models[model_name]['path']

    # Register the custom metric function

    tf.keras.utils.get_custom_objects()[precision.__name__] = precision

    tf.keras.utils.get_custom_objects()[recall.__name__] = recall

    tf.keras.utils.get_custom_objects()[f1_score.__name__] = f1_score

    model = tf.keras.models.load_model(model_path)

    predict(text, model, index_to_label, acronyms_to_entities, MAX_LENGTH)