Code Skripsi

This is full code for prediction of fuel adulteration

detect2.py

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+import tflite_runtime.interpreter as tflite
+import numpy as np
+import as7265x
+import smbus
+import joblib
+import RPi.GPIO as GPIO
+from luma.core.interface.serial import i2c
+from luma.core.render import canvas
+from luma.oled.device import ssd1306
+from time import sleep
+
+# ======================== Load TF Model ========================
+interpreter = tflite.Interpreter(model_path="model.tflite")
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+print("Input details:", input_details)
+print("Output details:", output_details)
+
+# ======================== Load Scalers ========================
+scaler_X = joblib.load('scaler_X.pkl')
+scaler_y = joblib.load('scaler_y.pkl')
+
+# ======================== GPIO Button Setup ========================
+BUTTON_PIN = 22  # Gunakan GPIO 22
+GPIO.setmode(GPIO.BCM)
+GPIO.setup(BUTTON_PIN, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)
+
+# ======================== Create Sensor Instance ========================
+i2c_bus = smbus.SMBus(1)
+sensor = as7265x.AS7265X(i2c_bus)
+
+# ======================== OLED Display Setup ========================
+serial = i2c(port=1, address=0x3C)
+device = ssd1306(serial, rotate=0)
+
+# ======================== Scan Function ========================
+def scan():
+    sensor.begin()
+    sensor.enableBulb(as7265x.LED_WHITE)
+    sensor.enableBulb(as7265x.LED_IR)
+    sensor.enableBulb(as7265x.LED_UV)
+    sensor.setIntegrationCycles(1)
+    sensor.takeMeasurements()
+
+    data = [
+        sensor.getCalibratedA(), sensor.getCalibratedB(), sensor.getCalibratedC(),
+        sensor.getCalibratedD(), sensor.getCalibratedE(), sensor.getCalibratedF(),
+        sensor.getCalibratedG(), sensor.getCalibratedH(), sensor.getCalibratedR(),
+        sensor.getCalibratedI(), sensor.getCalibratedS(), sensor.getCalibratedJ(),
+        sensor.getCalibratedT(), sensor.getCalibratedU(), sensor.getCalibratedV(),
+        sensor.getCalibratedW(), sensor.getCalibratedK(), sensor.getCalibratedL()
+    ]
+
+    sensor.disableBulb(as7265x.LED_WHITE)
+    sensor.disableBulb(as7265x.LED_IR)
+    sensor.disableBulb(as7265x.LED_UV)
+
+    data = np.array(data).reshape(1, -1)
+    return data
+
+# ======================== Predict Function ========================
+def predict(data):
+    scaled_data = scaler_X.transform(data)
+    scaled_data = scaled_data.reshape((1, 18, 1))
+
+    interpreter.set_tensor(input_details[0]['index'], scaled_data)
+    interpreter.invoke()
+    output_data = interpreter.get_tensor(output_details[0]['index'])
+    output_original = scaler_y.inverse_transform(output_data)
+
+    print("Model output (scaled):", output_data)
+    print("Model output (original):", output_original)
+
+    with canvas(device) as draw:
+        draw.rectangle(device.bounding_box, outline="white", fill="black")
+        draw.text((5, 20), "Prediction Result:", fill="white")
+        draw.text((5, 40), str(np.round(output_original[0], 2)), fill="white")
+
+# ======================== Main Loop ========================
+print("Model berhasil diload")
+print("Tekan tombol untuk memulai prediksi...")
+
+try:
+    while True:
+        if GPIO.input(BUTTON_PIN) == GPIO.HIGH:
+            print("Tombol ditekan. Memulai scan dan prediksi...")
+            data = scan()
+            predict(data)
+            sleep(1.5)  # Debounce delay
+except KeyboardInterrupt:
+    print("Program dihentikan oleh pengguna.")
+finally:
+    GPIO.cleanup()
+    print("GPIO dibersihkan.")