"""
Copyright 2024 X_G85
Model Integration Utils
-------------------------
"""

# Author: Adam-Al-Rahman <adam.al.rahman.dev@gmail.com>

import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.preprocessing.text import tokenizer_from_json
from tensorflow.keras.preprocessing.sequence import pad_sequences


def tokenizer(arch: str, tokenizer_json: str, text: str, max_length=300):
    """
    ::param:: arch: type of model `Bstm` or `Bert`
    """
    tokenized_data = None
    if arch == "Lstm":
        # Load the tokenizer from the JSON file
        with open(tokenizer_json) as file:
            data = file.read()
            tokenizer = tokenizer_from_json(data)

        # Use the tokenizer to transform test data
        tokenized_text = tokenizer.texts_to_sequences(text)
        tokenized_data = pad_sequences(tokenized_text, maxlen=max_length)
        tokenized_data = tokenized_data.astype(np.float32)

    return tokenized_data


def modelx(
    arch: str,
    model_path: str,
    text: str,
    tokenizer_json: str = "",
    batch_size=32,
    max_length=300,
):
    model_result = None
    if tokenizer_json:
        text = tokenizer(arch, tokenizer_json, text, max_length)
    else:
        text = pd.Series(text)

    if arch == "Lstm":
        model = tf.keras.models.load_model(model_path)
        model_result = model.predict(text, batch_size=batch_size)

    model_result = tf.squeeze(tf.round(model_result))

    if model_result == 1.0:
        model_result = "REAL NEWS"
    elif model_result == 0.0:
        model_result = "FAKE NEWS"

    return model_result