import time
from machine import Pin, ADC, PWM
import dht

# --- Hardware Initialization (As per your diagram.json) ---
sensor_dht = dht.DHT22(Pin(4)) 
ldr = ADC(Pin(34))
pot_vin = ADC(Pin(35))
pwm_out = PWM(Pin(18), freq=20000)

# --- Tuned Neural Network Weights ---
# W1: 4 neurons, each with 3 inputs [Temp, Lux, Vin]
# Weights are adjusted to reduce D as Vin increases (typical for Boost MPPT)
W1 = [
    [-0.15, 0.05, -0.60],  # Neuron 1: Strong negative Vin sensitivity
    [ 0.10, 0.25, -0.30],  # Neuron 2: Lux sensitivity
    [-0.05, 0.15, -0.75],  # Neuron 3: Strong negative Vin sensitivity
    [ 0.05, 0.10, -0.20]   # Neuron 4: General sensitivity
]
B1 = [0.4, 0.1, 0.5, 0.2]   # Biases for hidden layer

# W2: Output neuron weights for the 4 hidden neurons
W2 = [0.35, 0.25, 0.40, 0.15]
B2 = 0.15                   # Lowered base bias to avoid saturation

def relu(x):
    return max(0, x)

def predict_mppt(temp, lux, vin):
    # Normalize inputs: T(0-50), Lux(0-100), Vin(0-20V)
    inputs = [temp/50.0, lux/100.0, vin/20.0]
    
    # Hidden Layer
    hidden = []
    for i in range(len(W1)):
        z = sum(W1[i][j] * inputs[j] for j in range(len(inputs))) + B1[i]
        hidden.append(relu(z))

    # Output Layer
    # Calculates a raw value roughly between 0.1 and 0.9
    raw_d = sum(W2[i] * hidden[i] for i in range(len(W2))) + B2
    
    # Map to 10% - 90% Duty Cycle
    return max(10, min(90, raw_d * 100))

print("\n--- TUNED MPPT SYSTEM STARTING ---")

while True:
    try:
        sensor_dht.measure()
        t = sensor_dht.temperature()
        
        # Scaling ADC (0-4095)
        intensity = ldr.read() / 40.95       
        voltage_in = pot_vin.read() / 204.75  
        
        # Inference
        d_val = predict_mppt(t, intensity, voltage_in)
        
        # PWM Register (0-1023)
        pwm_out.duty(int((d_val / 100) * 1023))
        
        # Terminal Display
        print(f"Vin: {voltage_in:4.1f}V | Lux: {intensity:4.1f}% | Temp: {t:2.0f}C | -> Predicted D: {d_val:4.1f}%")
        
    except Exception as e:
        print(f"Error: {e}")
        
    time.sleep(1.5)