gibberish-detector

import os import tempfile # NOTE: In a real scenario, you would train a model on a large text file. # For this quickstart, we'll create a dummy model file for demonstration. # A proper model training example would be: # gibberish-detector train examples/big.txt > big.model # Create a dummy model file for demonstration purposes # This content is NOT a valid gibberish-detector model and will likely fail. # It's purely to show the API usage. A real model is a JSON file. model_content = "{}" with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.model', encoding='utf-8') as tmp_model_file: tmp_model_file.write(model_content) model_path = tmp_model_file.name try: from gibberish_detector import detector # Attempt to load from the dummy model file # This will likely fail with a JSONDecodeError or similar since it's an empty dict string. # In a real application, ensure your model_path points to a valid, trained model file. print(f"Attempting to load model from: {model_path}") my_detector = detector.create_from_model(model_path) # Example usage with a loaded detector print(f"'superman' is gibberish: {my_detector.is_gibberish('superman')}") print(f"'ertrjiloifdfyyoiu' is gibberish: {my_detector.is_gibberish('ertrjiloifdfyyoiu')}") except Exception as e: print(f"Could not run quickstart due to an error. This is expected if the model_path is not a valid trained model. Error: {e}") print("To run properly, first train a model using the command line tool:") print(" gibberish-detector train <path_to_good_text_file> > your_model.model") print("Then, replace 'model_path' above with 'your_model.model'.") finally: # Clean up the dummy model file if os.path.exists(model_path): os.remove(model_path)