"""

# Get command line arguments using in_arg

in_args = get_input_args('predict')

# Load the model and class mapping

model, idx_class_mapping = load_model(in_args.model_path)

# Check the type of input data

input_type = check_input_type(in_args.input_data)

# Map the labels

labels = label_mapping(in_args.labels)

if input_type == 'folder':

# Define field names for the CSV file

field_names = ['image_file', 'expected', 'prediction', 'probability', 'match']

all_predictions = []

# Find all image files in the folder

image_files = find_image_files(in_args.input_data)

# Loop through each image file to make predictions

for image_file in image_files:

probs, classes = predict(image_file, model, labels, in_args.device, idx_class_mapping, 1)

expected = labels[image_file.split('/')[-2]]

all_predictions.append({

'image_file': image_file,

'expected': expected,

'prediction': classes[0],

'probability': round(float(probs[0]) * 100, 2),

'match': expected == classes[0]

})

# Create a DataFrame from the predictions

df = pd.DataFrame(all_predictions)

# Create a Tkinter root window

root = tk.Tk()

root.withdraw() # Hide the main window

root.attributes('-topmost', True) # Ensure the dialog box appears in front of other apps

# Open the "Save As" dialog box

file_path = filedialog.asksaveasfilename(defaultextension=".csv",

initialfile='predictions.csv',

filetypes=[("CSV files", "*.csv"), ("All Files", "*.*")])

# Save the predictions to a CSV file

if file_path:

with open('predictions.csv', 'w') as csvfile:

writer = csv.DictWriter(csvfile, fieldnames = field_names)

writer.writeheader()

writer.writerows(all_predictions)

print(f"Predictions saved to {file_path}")

else:

print("Save operation was cancelled.")

elif input_type == 'single':

# Predict classes and probabilities for a single image

probs, classes = predict(in_args.input_data, model, labels, idx_class_mapping, in_args.device, in_args.topk)

print_predictions(probs, classes)

else:

raise Exception("Input type must be a single images or a folder containing images")