from machine import Pin 
import time 

# --------------------------------------------------------- 
# 1. HARDWARE SETUP 
# --------------------------------------------------------- 
# Button: Connected to GP22 with Internal Pull-Up
button = Pin(22, Pin.IN, Pin.PULL_UP) 
# LEDs: Red for 'Waiting', Green for 'Success/Registered'
red_led = Pin(27, Pin.OUT) 
green_led = Pin(17, Pin.OUT) 

file_name = "attendance.csv" # To store guest data

# --------------------------------------------------------- 
# 2. DRINKS DATABASE (Knowledge Base)
# --------------------------------------------------------- 
# Includes health-focused options aligned with UAE hospitality
drinks = [ 
    {"Name": "Orange Juice", "Type": "Fruit", "Sugar": 20, "Cals": 120}, 
    {"Name": "Lemon Water", "Type": "Fruit", "Sugar": 2, "Cals": 5}, 
    {"Name": "Green Tea", "Type": "Tea", "Sugar": 0, "Cals": 5}, 
    {"Name": "Arabic Coffee", "Type": "Coffee", "Sugar": 0, "Cals": 2}, 
    {"Name": "Chocolate Milk", "Type": "Milk", "Sugar": 22, "Cals": 150}, 
    {"Name": "Water", "Type": "None", "Sugar": 0, "Cals": 0} 
] 

# --------------------------------------------------------- 
# 3. AI & ANALYSIS LOGIC
# --------------------------------------------------------- 

# Feature: Analyze popularity from previous logs (Social Proof)
def get_popular_drinks(): 
    popularity = {} 
    try: 
        with open(file_name, "r") as f: 
            for line in f: 
                parts = line.strip().split(",") 
                if len(parts) >= 5: 
                    chosen = parts[4] # Index 4 is the recommended drink
                    popularity[chosen] = popularity.get(chosen, 0) + 1 
    except: 
        pass 
    return popularity 

# Feature: Multi-factor Recommendation Engine
def smart_recommend(pref_type, health_pref): 
    pop_data = get_popular_drinks() 
    recommendations = [] 
    
    p_type = pref_type.lower().strip() 
    h_pref = health_pref.lower().strip() 

    for d in drinks: 
        score = 0 
        reasons = [] 

        # Logic A: Preference Match (+3 pts) 
        if p_type in d["Type"].lower(): 
            score += 3 
            reasons.append("Matches your style") 

        # Logic B: Health Analysis (Priority) (+5 pts)
        if h_pref == "low sugar" and d["Sugar"] < 5: 
            score += 5 
            reasons.append("Healthy low-sugar option") 
        elif h_pref == "low calories" and d["Cals"] < 50: 
            score += 5 
            reasons.append("Low-calorie choice") 

        # Logic C: Social Proof (Popularity) (+1 pt) 
        if d["Name"] in pop_data: 
            score += 1 
            reasons.append("Popular among guests") 

        recommendations.append({ 
            "Name": d["Name"], 
            "Score": score, 
            "Reason": " & ".join(reasons) if reasons else "Safe choice" 
        }) 

    # Sort: Highest score first 
    recommendations.sort(key=lambda x: x["Score"], reverse=True) 
    return recommendations[:3] 

# --------------------------------------------------------- 
# 4. MAIN OPERATIONAL LOOP 
# --------------------------------------------------------- 
print("="*45) 
print(" SMART AI GUEST SYSTEM - FULL VERSION") 
print("="*45) 

while True: 
    # --- STANDBY MODE --- 
    green_led.value(0)  
    red_led.value(1) # Red light indicates waiting for guest
    print("\n[READY] System Waiting... Press the button to start.") 

    # Wait for button press (Value 0 means pressed in PULL_UP)
    while button.value() == 1: 
        time.sleep(0.05) 

    # --- ACTIVE MODE --- 
    red_led.value(0) 
    green_led.value(1) # Green light indicates processing/success
    print("\n[EVENT] Guest detected! Initializing AI Analysis...") 
    time.sleep(1) # Simulated processing time

    # Data Input (User Interface Simulation)
    name = input(" > Guest Name: ").strip() 
    p_drink = input(" > Drink Preference (Fruit/Tea/Milk/Coffee): ") 
    p_health = input(" > Health Needs (Low Sugar/Low Calories/None): ") 

    print("\n[AI] Analyzing health data and generating recommendation...")
    time.sleep(1.5)

    # Run AI Logic
    results = smart_recommend(p_drink, p_health) 
    top_pick = results[0]["Name"] 

    # Save to CSV (Registration)
    timestamp = time.localtime()
    time_str = "{}:{}:{}".format(timestamp[3], timestamp[4], timestamp[5])
    
    try: 
        with open(file_name, "a") as f: 
            # Format: Name, Time, Preference, Health, Recommendation
            f.write("{},{},{},{},{}\n".format(name, time_str, p_drink, p_health, top_pick)) 
        print("✅ Attendance recorded & saved to attendance.csv") 
    except Exception as e: 
        print("❌ Data Error:", e) 

    # Display Smart Recommendations
    print("\n" + "-"*40) 
    print("AI TOP RECOMMENDATIONS FOR {}:".format(name.upper())) 
    for i, res in enumerate(results, 1): 
        print("{}. {} (Score: {})".format(i, res["Name"], res["Score"])) 
        print("   Reason: {}".format(res["Reason"])) 
    
    time.sleep(4) # Pause to allow reading results before resetting