#include <TensorFlowLite_ESP32.h>
#include "tensorflow/lite/micro/all_ops_resolver.h"
#include "tensorflow/lite/micro/micro_error_reporter.h"
#include "tensorflow/lite/micro/micro_interpreter.h"

#include "model_data.cpp"  // Include the generated model data source file

// Define TFLite model variables
extern const unsigned char model_data[];
const int model_data_len = 1372;  // Update this value with the actual size of your model data

tflite::MicroErrorReporter micro_error_reporter;
tflite::AllOpsResolver resolver;
const tflite::Model* tflite_model = nullptr;
tflite::MicroInterpreter interpreter(tensor_arena, tensor_arena_size, &micro_error_reporter);
constexpr int tensor_arena_size = 8 * 1024;  // Adjust the size as needed
uint8_t tensor_arena[tensor_arena_size];

// Input and output tensor pointers
TfLiteTensor* input;
TfLiteTensor* output;

// Function to initialize TFLite interpreter
void setup() {
  // Initialize TFLite model
  tflite_model = tflite::GetModel(model_data);
  if (tflite_model->version() != TFLITE_SCHEMA_VERSION) {
    micro_error_reporter.Report("Model version does not match schema", 0, "");
    return;
  }

  // Allocate memory from the tensor_arena for the model's tensors
  interpreter.AllocateTensors();

  // Get pointers to the input and output tensors
  input = interpreter.input(0);
  output = interpreter.output(0);
}

// Function to run inference on TFLite model
void loop() {
  // Fetch weather data (temperature, humidity, etc.) from sensors or API
  float input_data[] = {10.0, 25.0, 15.0, 5.0}; // Example weather data

  // Prepare input tensor
  for (int i = 0; i < input->dims->size; i++) {
    input->data.f[i] = input_data[i];
  }

  // Run inference
  interpreter.Invoke();

  // Get prediction result
  float prediction = output->data.f[0]; // Assuming output is a single float representing predicted class or value

  // Process prediction result (display, send over serial, etc.)
  // Implement your logic here based on the prediction

  delay(1000); // Add delay for stability
}