model.py

import tensorflow as tf

class CryptoBinaryClassifier(tf.keras.Model):
    def __init__(self, *args, **kwargs):
        super(CryptoBinaryClassifier, self).__init__()
        # Define your model architecture here
        self.model = tf.keras.Sequential([
            tf.keras.layers.Input(shape=(27,)),
            tf.keras.layers.Dense(64, activation='relu'),
            tf.keras.layers.Dense(32, activation='relu'),
            tf.keras.layers.Dense(1, activation='sigmoid')
        ])
    
    def call(self, inputs, training=False):
        return self.model(inputs)

    def __init__(self, *args, **kwargs):
        super(CryptoBinaryClassifier, self).__init__()
        # Load your pre-trained weights
        self.load_weights('AVAXUSDT_x22.xlsx_binary_classification_model.h5')

    def predict(self, input_data):
        # Preprocess input_data if necessary
        return self(input_data)