CLARIS / infrerence_examples /keras_inference_example.py

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import tensorflow as tf
import pickle
import numpy as np
import pandas as pd

def load_model_and_processor():
    """Load the trained model and processor."""
    # Load the complete model
    model = tf.keras.models.load_model('model.h5')
    
    # Load the processor
    with open('processor.pkl', 'rb') as f:
        processor = pickle.load(f)
    
    return model, processor

def predict_sign(model, processor, landmark_data):
    """
    Predict sign from landmark data.
    
    Args:
        model: Loaded Keras model
        processor: SignLanguageProcessor instance
        landmark_data: DataFrame with columns ['frame', 'row_id', 'x', 'y', 'z']
    
    Returns:
        predicted_class: Predicted sign class
        confidence: Prediction confidence
    """
    # Process the landmark data
    X, _ = processor.process_dataset(landmark_data)
    
    if len(X) == 0:
        return None, 0.0
    
    # Make prediction
    predictions = model.predict(X)
    predicted_class = np.argmax(predictions, axis=1)[0]
    confidence = np.max(predictions, axis=1)[0]
    
    # Convert back to sign name if mapping exists
    if hasattr(processor, 'index_to_sign'):
        sign_name = processor.index_to_sign[predicted_class]
        return sign_name, confidence
    
    return predicted_class, confidence

# Example usage
if __name__ == "__main__":
    # Load model and processor
    model, processor = load_model_and_processor()
    
    # Example landmark data (replace with your actual data)
    # landmark_data = pd.read_csv('your_landmark_data.csv')
    
    # Make prediction
    # predicted_sign, confidence = predict_sign(model, processor, landmark_data)
    # print(f"Predicted sign: {predicted_sign}, Confidence: {confidence:.3f}")
    
    print("Model and processor loaded successfully!")
    print(f"Model input shape: {model.input_shape}")
    print(f"Model output shape: {model.output_shape}")
    print(f"Number of classes: {processor.sign_count}")

	import tensorflow as tf
	import pickle
	import numpy as np
	import pandas as pd

	def load_model_and_processor():
	"""Load the trained model and processor."""
	# Load the complete model
	model = tf.keras.models.load_model('model.h5')

	# Load the processor
	with open('processor.pkl', 'rb') as f:
	processor = pickle.load(f)

	return model, processor

	def predict_sign(model, processor, landmark_data):
	"""
	Predict sign from landmark data.

	Args:
	model: Loaded Keras model
	processor: SignLanguageProcessor instance
	landmark_data: DataFrame with columns ['frame', 'row_id', 'x', 'y', 'z']

	Returns:
	predicted_class: Predicted sign class
	confidence: Prediction confidence
	"""
	# Process the landmark data
	X, _ = processor.process_dataset(landmark_data)

	if len(X) == 0:
	return None, 0.0

	# Make prediction
	predictions = model.predict(X)
	predicted_class = np.argmax(predictions, axis=1)[0]
	confidence = np.max(predictions, axis=1)[0]

	# Convert back to sign name if mapping exists
	if hasattr(processor, 'index_to_sign'):
	sign_name = processor.index_to_sign[predicted_class]
	return sign_name, confidence

	return predicted_class, confidence

	# Example usage
	if __name__ == "__main__":
	# Load model and processor
	model, processor = load_model_and_processor()

	# Example landmark data (replace with your actual data)
	# landmark_data = pd.read_csv('your_landmark_data.csv')

	# Make prediction
	# predicted_sign, confidence = predict_sign(model, processor, landmark_data)
	# print(f"Predicted sign: {predicted_sign}, Confidence: {confidence:.3f}")

	print("Model and processor loaded successfully!")
	print(f"Model input shape: {model.input_shape}")
	print(f"Model output shape: {model.output_shape}")
	print(f"Number of classes: {processor.sign_count}")