ai-edge-litert

import numpy as np from tflite_runtime.interpreter import Interpreter import os # Ensure you have a .tflite model file, e.g., downloaded from Google AI Edge. # For this example, we'll assume 'model.tflite' exists in the current directory. # Replace 'model.tflite' with your actual model path. model_path = os.environ.get('LITERT_MODEL_PATH', 'model.tflite') try: # Load the TFLite model and allocate tensors. interpreter = Interpreter(model_path=model_path) interpreter.allocate_tensors() # Get input and output tensor details. input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() # Assuming a single input tensor for simplicity input_shape = input_details[0]['shape'] input_dtype = input_details[0]['dtype'] # Create a dummy input tensor (replace with actual data for your model) input_data = np.array(np.random.random_sample(input_shape), dtype=input_dtype) # Set the tensor to point to the input data to be inferred. interpreter.set_tensor(input_details[0]['index'], input_data) # Run inference. interpreter.invoke() # Get the output tensor. # Assuming a single output tensor for simplicity output_data = interpreter.get_tensor(output_details[0]['index']) print(f"Model loaded from: {model_path}") print(f"Input shape: {input_shape}, Dtype: {input_dtype}") print(f"Output data shape: {output_data.shape}, Dtype: {output_data.dtype}") print(f"First 5 output values: {output_data.flatten()[:5]}") except FileNotFoundError: print(f"Error: Model file not found at '{model_path}'. Please provide a valid .tflite model path.") except Exception as e: print(f"An error occurred during model inference: {e}")