routes/ai_monitoring.py

# backend/routes/ai_monitoring.py
# TODO: Review pricing after 2026-05-31
from datetime import datetime, timezone
from fastapi import APIRouter, Depends, HTTPException, Request
import logging

from config.ai_pricing import get_active_pricing, get_full_pricing, DEEPSEEK_PRICING
from services.cost_calculator import calculate_feature_cost, calculate_full_price_cost

logger = logging.getLogger("mathpulse.ai_monitoring")

router = APIRouter(prefix="/api/admin/ai-monitoring", tags=["admin", "ai-monitoring"])


def require_admin(request: Request):
    user = getattr(request.state, "user", None)
    if user is None:
        raise HTTPException(status_code=401, detail="Authentication required")
    if user.role not in ("admin", "superadmin"):
        raise HTTPException(status_code=403, detail="Admin access required")
    return user


def _build_pricing_meta(model_id: str = "deepseek-v4-pro") -> dict:
    """Build pricingMeta block for response."""
    pricing = get_active_pricing(model_id)
    full = get_full_pricing(model_id)
    now = datetime.now(timezone.utc)
    promo_config = DEEPSEEK_PRICING.get(model_id, {}).get("promotional", {})
    expires = promo_config.get("expires_utc", now)
    days_remaining = max(0, (expires - now).days) if pricing.get("is_promotional") else 0

    return {
        "activeModel": model_id,
        "isPromotional": pricing.get("is_promotional", False),
        "promoExpiresUtc": expires.isoformat() if pricing.get("is_promotional") else None,
        "daysUntilPromoEnds": days_remaining,
        "currentInputCacheMissRate": pricing["input_cache_miss_per_1m"],
        "currentOutputRate": pricing["output_per_1m"],
        "fullPriceInputRate": full["input_cache_miss_per_1m"],
        "fullPriceOutputRate": full["output_per_1m"],
    }


def _aggregate_summary() -> dict:
    """
    Aggregate AI monitoring summary from in-memory/mock data.
    In production, this reads from Firestore ai_usage_logs collection.
    """
    # TODO: Replace with actual Firestore aggregation when usage logging is wired
    model_id = "deepseek-v4-pro"
    pricing = get_active_pricing(model_id)

    # Feature definitions with estimated token distributions
    features_config = [
        {"id": "ai_chat_tutor", "name": "AI Chat Tutor", "model": model_id, "share": 0.35, "cache_hit_rate": 0.62, "icon": "MessageCircle"},
        {"id": "hint_generation", "name": "Hint Generation", "model": model_id, "share": 0.28, "cache_hit_rate": 0.58, "icon": "Lightbulb"},
        {"id": "lesson_generation", "name": "Lesson Generation", "model": model_id, "share": 0.18, "cache_hit_rate": 0.35, "icon": "GraduationCap"},
        {"id": "learning_paths", "name": "Learning Paths", "model": model_id, "share": 0.09, "cache_hit_rate": 0.40, "icon": "Target"},
        {"id": "quiz_generation", "name": "Quiz Generation", "model": model_id, "share": 0.09, "cache_hit_rate": 0.38, "icon": "PenTool"},
        {"id": "other", "name": "Other AI Features", "model": model_id, "share": 0.01, "cache_hit_rate": 0.50, "icon": "Zap"},
    ]

    total_requests = 6900
    total_input_tokens = 8_500_000
    total_output_tokens = 3_200_000

    features = []
    total_cost = 0.0
    total_full_price_cost = 0.0
    total_cache_hit_tokens = 0
    total_cache_miss_tokens = 0

    for fc in features_config:
        req_count = int(total_requests * fc["share"])
        input_share = int(total_input_tokens * fc["share"])
        output_share = int(total_output_tokens * fc["share"])
        cache_hit = int(input_share * fc["cache_hit_rate"])
        cache_miss = input_share - cache_hit

        cost = calculate_feature_cost(fc["model"], cache_hit, cache_miss, output_share)
        full_cost = calculate_full_price_cost(fc["model"], cache_hit, cache_miss, output_share)

        total_cost += cost["total_usd"]
        total_full_price_cost += full_cost
        total_cache_hit_tokens += cache_hit
        total_cache_miss_tokens += cache_miss

        features.append({
            "featureId": fc["id"],
            "featureName": fc["name"],
            "modelId": fc["model"],
            "monthlyCost": round(cost["total_usd"], 4),
            "costShare": round(fc["share"] * 100, 1),
            "totalRequests": req_count,
            "totalInputTokens": input_share,
            "totalOutputTokens": output_share,
            "cacheHitRate": fc["cache_hit_rate"],
            "isMostActive": fc["id"] == "ai_chat_tutor",
            "isTopSpending": fc["id"] == "ai_chat_tutor",
            "icon": fc["icon"],
        })

    overall_cache_hit_rate = total_cache_hit_tokens / (total_cache_hit_tokens + total_cache_miss_tokens) if (total_cache_hit_tokens + total_cache_miss_tokens) > 0 else 0

    # Cost breakdown
    total_cache_hit_cost = (total_cache_hit_tokens / 1_000_000) * pricing["input_cache_hit_per_1m"]
    total_cache_miss_cost = (total_cache_miss_tokens / 1_000_000) * pricing["input_cache_miss_per_1m"]
    total_output_cost = (total_output_tokens / 1_000_000) * pricing["output_per_1m"]

    summary = {
        "systemStatus": "healthy",
        "actionRequired": False,
        "hasPerformanceIssues": False,
        "monthlyCost": round(total_cost, 4),
        "projectedMonthlyCost": round(total_cost * 1.1, 4),
        "billingCycleLabel": "Current Billable Cycle",
        "costBreakdown": {
            "cacheHitCost": round(total_cache_hit_cost, 6),
            "cacheMissCost": round(total_cache_miss_cost, 6),
            "outputCost": round(total_output_cost, 6),
        },
        "totalUsage": total_requests,
        "totalInputTokens": total_cache_hit_tokens + total_cache_miss_tokens,
        "totalOutputTokens": total_output_tokens,
        "cacheHitRate": round(overall_cache_hit_rate, 4),
        "activeEngine": "DeepSeek-V4 Pro",
        "activeEngineModelId": model_id,
        "engineTier": "High-Performance LLM",
        "promotionalPricingActive": pricing.get("is_promotional", False),
        "promotionalPriceExpiresUtc": pricing.get("promo_expires_utc", ""),
        "estimatedCostAfterPromo": round(total_full_price_cost, 4),
        "lastUpdated": datetime.now(timezone.utc).isoformat(),
    }

    return {"summary": summary, "features": features}


@router.get("/summary")
def get_monitoring_summary(_admin=Depends(require_admin)):
    """Returns AI monitoring summary + feature metrics + pricing metadata."""
    data = _aggregate_summary()
    return {
        **data["summary"],
        "features": data["features"],
        "pricingMeta": _build_pricing_meta(),
    }


@router.post("/refresh")
def refresh_monitoring(_admin=Depends(require_admin)):
    """Re-aggregate usage metrics and recalculate costs."""
    data = _aggregate_summary()
    # TODO: Write to Firestore ai_monitoring/summary when Firestore admin SDK is available
    pricing = get_active_pricing("deepseek-v4-pro")
    return {
        "success": True,
        "updatedAt": datetime.now(timezone.utc).isoformat(),
        "pricingUsed": pricing,
    }