import machine
import time
from machine import Pin
import random  # Simulating the machine learning prediction

# Setup for TCS3200 Color Sensor
S2 = Pin(15, Pin.OUT)
S3 = Pin(2, Pin.OUT)
out = Pin(4, Pin.IN)  # Output from sensor
S2.off()
S3.off()

# Initialize variables
color_data = {'red': 0, 'green': 0, 'blue': 0}
threshold = 50  # Example threshold for detecting cancerous cells
sensor_error = False

# Simulated AI-based cancer detection (replace with an actual ML model later)
def detect_cancerous_cells(color_data):
    # Simulating a machine learning-based prediction for cancer detection
    avg_color = (color_data['red'] + color_data['green'] + color_data['blue']) / 3
    return random.choice([True, False]) if avg_color < threshold else False

def read_color():
    # Dummy function to simulate reading from the TCS3200 sensor
    # Replace with actual sensor reading logic
    return {'red': random.randint(20, 80), 'green': random.randint(20, 80), 'blue': random.randint(20, 80)}

def calculate_average_color(color_data):
    # Calculate the average color value from red, green, and blue components
    return (color_data['red'] + color_data['green'] + color_data['blue']) / 3

# Main loop
while True:
    try:
        color_data = read_color()
        print("Color Data:", color_data)

        avg_color = calculate_average_color(color_data)
        print("Average Color Value:", avg_color)
        
        # Perform AI-based cancerous cell detection
        cancer_detected = detect_cancerous_cells(color_data)
        if cancer_detected:
            print("Warning: Potential cancerous cells detected!")
        else:
            print("Skin pigmentation is within normal range.")

    except OSError as e:
        print("Error reading color sensor data. Check wiring or sensor condition.")
        sensor_error = True
        break
    
    time.sleep(1)

import machine
import time
from machine import Pin
import random  # Simulating the machine learning prediction

# Setup for TCS3200 Color Sensor
S2 = Pin(15, Pin.OUT)
S3 = Pin(2, Pin.OUT)
out = Pin(4, Pin.IN)  # Output from sensor
S2.off()
S3.off()

# Initialize variables
color_data = {'red': 0, 'green': 0, 'blue': 0}
threshold = 50  # Example threshold for detecting cancerous cells
sensor_error = False

# Simulated AI-based cancer detection (replace with an actual ML model later)
def detect_cancerous_cells(color_data):
    # Simulating a machine learning-based prediction for cancer detection
    avg_color = (color_data['red'] + color_data['green'] + color_data['blue']) / 3
    return random.choice([True, False]) if avg_color < threshold else False

def read_color():
    # Dummy function to simulate reading from the TCS3200 sensor
    # Replace with actual sensor reading logic
    return {'red': random.randint(20, 80), 'green': random.randint(20, 80), 'blue': random.randint(20, 80)}

def calculate_average_color(color_data):
    # Calculate the average color value from red, green, and blue components
    return (color_data['red'] + color_data['green'] + color_data['blue']) / 3

# Main loop
while True:
    try:
        color_data = read_color()
        print("Color Data:", color_data)

        avg_color = calculate_average_color(color_data)
        print("Average Color Value:", avg_color)
        
        # Perform AI-based cancerous cell detection
        cancer_detected = detect_cancerous_cells(color_data)
        if cancer_detected:
            print("Warning: Potential cancerous cells detected!")
        else:
            print("Skin pigmentation is within normal range.")

    except OSError as e:
        print("Error reading color sensor data. Check wiring or sensor condition.")
        sensor_error = True
        break
    
    time.sleep(1)