bodybuilding_pose_classifier.py

import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
import argparse
import os

# Define class labels (ensure these match your training)
CNN_CLASS_LABELS = ['Side Chest', 'Front Double Biceps', 'Back Double Biceps', 'Front Lat Spread', 'Back Lat Spread']
MODEL_PATH = 'bodybuilding_pose_classifier_savedmodel.keras' # Corrected model path

def predict_pose_from_image(model, img_path):
    """
    Loads an image, preprocesses it, and predicts the bodybuilding pose.

    Args:
        model: The loaded Keras model.
        img_path (str): Path to the image file.

    Returns:
        tuple: (predicted_class_label, confidence_score) or (None, None) if error.
    """
    try:
        if not os.path.exists(img_path):
            print(f"Error: Image path not found: {img_path}")
            return None, None

        # Load and preprocess the image
        img = image.load_img(img_path, target_size=(150, 150))
        img_array = image.img_to_array(img)
        img_array = np.expand_dims(img_array, axis=0) / 255.0  # Normalize

        # Make prediction
        predictions = model.predict(img_array)
        predicted_class_index = np.argmax(predictions, axis=1)[0]
        confidence = float(np.max(predictions))
        
        predicted_class_label = CNN_CLASS_LABELS[predicted_class_index]
        
        return predicted_class_label, confidence
    except Exception as e:
        print(f"Error during prediction: {e}")
        return None, None

def main():
    parser = argparse.ArgumentParser(description="Classify a bodybuilding pose from an image.")
    parser.add_argument("image_path", help="Path to the input image file.")
    args = parser.parse_args()

    # Load the Keras model
    print(f"Loading model from: {MODEL_PATH}")
    try:
        model = load_model(MODEL_PATH)
        # Optional: Print model summary to verify
        # model.summary() 
    except Exception as e:
        print(f"Error loading model: {e}")
        return

    print(f"Classifying image: {args.image_path}")
    predicted_pose, confidence_score = predict_pose_from_image(model, args.image_path)

    if predicted_pose and confidence_score is not None:
        print(f"Predicted Pose: {predicted_pose}")
        print(f"Confidence: {confidence_score:.2f}")

if __name__ == "__main__":
    main()