ESP32 Chatbot with Display

class="text-gray-class="text-purple-400">500 italic"># TensorFlow Lite Inference Example
import numpy as np
import tflite_runtime.interpreter as tflite

class="text-gray-class="text-purple-400">500 italic"># Load the TFLite model
interpreter = tflite.Interpreter(model_path=class="text-green-class="text-purple-400">400">"model.tflite")
interpreter.allocate_tensors()

class="text-gray-class="text-purple-400">500 italic"># Get input and output tensors
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

class="text-gray-class="text-purple-400">500 italic"># Prepare input data
input_data = np.array([[class="text-purple-400">1.0, class="text-purple-400">2.0, class="text-purple-400">3.0]], dtype=np.float32)
interpreter.set_tensor(input_details[class="text-purple-400">0][class="text-green-class="text-purple-400">400">'index'], input_data)

class="text-gray-class="text-purple-400">500 italic"># Run inference
interpreter.invoke()

class="text-gray-class="text-purple-400">500 italic"># Get output
output_data = interpreter.get_tensor(output_details[class="text-purple-400">0][class="text-green-class="text-purple-400">400">'index'])
print(fclass="text-green-class="text-purple-400">400">"Prediction: {output_data}")