"""
Advanced Risk Engine - Explains crowd predictions with reasoning and recommendations
"""

from typing import Dict, List

RISK_CONFIG = {
    "Low": {
        "score": 15,
        "color": "#10b981",
        "bg": "#d1fae5",
        "emoji": "🟢",
        "badge": "LOW RISK",
        "advice": "Comfortable travel expected. Enjoy your journey!",
    },
    "Medium": {
        "score": 50,
        "color": "#f59e0b",
        "bg": "#fef3c7",
        "emoji": "🟡",
        "badge": "MODERATE",
        "advice": "Moderate crowd. Book window seat in advance if possible.",
    },
    "Extreme": {
        "score": 90,
        "color": "#ef4444",
        "bg": "#fee2e2",
        "emoji": "🔴",
        "badge": "EXTREME",
        "advice": "Very crowded! Consider alternate timing or route.",
    },
}

HOUR_LABELS = {
    5: "Early Morning", 6: "Early Morning",
    7: "Morning Rush", 8: "Peak Morning Rush", 9: "Morning Rush",
    10: "Mid Morning", 11: "Late Morning",
    12: "Noon", 13: "Afternoon", 14: "Afternoon", 15: "Mid Afternoon", 16: "Late Afternoon",
    17: "Evening Rush", 18: "Peak Evening Rush", 19: "Evening Rush",
    20: "Late Evening", 21: "Night", 22: "Late Night",
}

WESTERN_STATIONS_ORDER = [
    "Churchgate", "Marine Lines", "Charni Road", "Grant Road",
    "Mumbai Central", "Mahalaxmi", "Lower Parel", "Dadar",
    "Bandra", "Andheri", "Borivali", "Virar"
]

CENTRAL_STATIONS_ORDER = [
    "CSMT", "Masjid", "Sandhurst Road", "Byculla",
    "Dadar", "Kurla", "Ghatkopar", "Thane", "Kalyan"
]

HARBOUR_STATIONS_ORDER = [
    "CSMT", "Masjid", "Dockyard Road", "Wadala Road",
    "Kurla", "Vashi", "Nerul", "Panvel"
]


def get_risk_info(crowd_level: str) -> Dict:
    return RISK_CONFIG.get(crowd_level, RISK_CONFIG["Low"])


def build_reasoning(
    line: str, station: str, destination: str,
    direction: str, hour: int, day_of_week: str,
    crowd_level: str
) -> List[str]:
    reasons = []
    is_weekday = day_of_week not in ["Saturday", "Sunday"]
    is_morning_peak = 7 <= hour <= 9
    is_evening_peak = 17 <= hour <= 19
    is_southbound = direction == "Southbound"
    hour_label = HOUR_LABELS.get(hour, "")

    # Time-based
    if is_morning_peak:
        reasons.append(f"⏰ {hour_label} ({hour}:00) — peak office hours create high demand")
    elif is_evening_peak:
        reasons.append(f"⏰ {hour_label} ({hour}:00) — peak return hours, trains are packed")
    elif hour < 6:
        reasons.append(f"⏰ Early morning ({hour}:00) — very few commuters at this hour")
    elif hour > 21:
        reasons.append(f"⏰ Late night ({hour}:00) — minimal traffic")
    else:
        reasons.append(f"⏰ {hour_label} ({hour}:00) — off-peak period")

    # Day type
    if is_weekday:
        reasons.append(f"📅 {day_of_week} is a working day — 20-30% more passengers than weekends")
    else:
        reasons.append(f"📅 {day_of_week} is a weekend — lighter traffic overall")

    # Directional logic
    if is_morning_peak and is_southbound and is_weekday:
        reasons.append("🚆 Southbound during morning rush — commuters flooding into the city")
    elif is_morning_peak and not is_southbound and is_weekday:
        reasons.append("🚆 Northbound during morning rush — outbound direction, relatively lighter")
    elif is_evening_peak and not is_southbound and is_weekday:
        reasons.append("🚆 Northbound during evening rush — commuters heading home to suburbs")
    elif is_evening_peak and is_southbound and is_weekday:
        reasons.append("🚆 Southbound during evening — reverse commute, lighter than northbound")

    # Line-specific
    if line == "Western":
        reasons.append("🛤️ Western Line — highest ridership line in Mumbai (approx. 4M+ daily)")
    elif line == "Central":
        reasons.append("🛤️ Central Line — second busiest, serves eastern suburbs & Thane district")
    elif line == "Harbour":
        reasons.append("🛤️ Harbour Line — connects Navi Mumbai, generally less crowded")

    return reasons


def get_best_hours(
    model, le_station, le_dest,
    line: str, station: str, destination: str, direction: str, day: str,
    predict_fn
) -> List[Dict]:
    results = []
    for h in range(5, 23):
        pred = predict_fn(model, le_station, le_dest, line, station, destination, direction, h, day)
        results.append({
            "hour": h,
            "label": f"{h:02d}:00",
            "crowd_level": pred["crowd_level"],
            "risk_score": RISK_CONFIG[pred["crowd_level"]]["score"],
            "confidence": pred["confidence"]
        })
    return results


def get_travel_tip(crowd_level: str, direction: str, hour: int) -> str:
    tips = {
        "Low": [
            "Perfect time to travel! You'll likely get a seat.",
            "Great window! Trains are comfortable right now.",
            "Off-peak bliss — smooth, uncrowded journey ahead.",
        ],
        "Medium": [
            "Manageable crowd — you may have to stand for a while.",
            "Arrive 5 mins early to board comfortably.",
            "It's busy but not terrible. Avoid the first bogie.",
        ],
        "Extreme": [
            "Packed trains! If flexible, shift by 30–60 minutes.",
            "Consider the Ladies' Special or next train.",
            "Carry water, maintain patience — it's Mumbai rush!",
        ]
    }
    import random
    opts = tips.get(crowd_level, tips["Medium"])
    return opts[hour % len(opts)]