Explain, with code snippets, how you have loaded your model to avoid incurring long waiting times for each classification.

To avoid incurring long waiting times for each classification, you can load your model once and reuse it for multiple predictions. Here's the code snippet to load the model and use it for predictions:

import tensorflow as tf
from tensorflow.python.keras.backend import set_session

# Create a new session and set it as the default session
session = tf.compat.v1.Session(graph=tf.compat.v1.Graph())
set_session(session)

# Load the model
with session.graph.as_default():
    set_session(session)
    model = tf.keras.models.load_model('path/to/your/model.h5')

# Function to make predictions using the loaded model
def predict(input_data):
    with session.graph.as_default():
        set_session(session)
        predictions = model.predict(input_data)
    return predictions

# Example usage
input1 = ... # Your input data for prediction
input2 = ... # Another input data for prediction

# Make predictions using the loaded model
predictions1 = predict(input1)
predictions2 = predict(input2)

By loading the model once and reusing it, you can avoid the overhead of loading the model for each prediction, which can significantly reduce the waiting time.