"""
api.routers.predict_v2
======================
v2 prediction & recommendation endpoints.

Bug fixes over v1:
- Removed avg_temp auto-correction that corrupted inputs
- Recommendation baseline uses user-provided current_soh for RUL estimation
- Version-aware model loading from artifacts/v2/
"""

from __future__ import annotations

from fastapi import APIRouter, HTTPException

from api.model_registry import registry_v2, classify_degradation, soh_to_color
from api.schemas import (
    PredictRequest, PredictResponse,
    BatchPredictRequest, BatchPredictResponse,
    RecommendationRequest, RecommendationResponse, SingleRecommendation,
)

router = APIRouter(prefix="/api/v2", tags=["v2-prediction"])


# ── Single prediction ────────────────────────────────────────────────────────
@router.post("/predict", response_model=PredictResponse)
async def predict_v2(req: PredictRequest):
    """Predict SOH for a single cycle using v2 models."""
    features = req.model_dump(exclude={"battery_id"})
    features["voltage_range"] = features["peak_voltage"] - features["min_voltage"]
    # v2 FIX: no avg_temp auto-correction — trust the user's input

    try:
        result = registry_v2.predict(features)
    except Exception as exc:
        raise HTTPException(status_code=500, detail=str(exc))

    return PredictResponse(
        battery_id=req.battery_id,
        cycle_number=req.cycle_number,
        soh_pct=result["soh_pct"],
        rul_cycles=result["rul_cycles"],
        degradation_state=result["degradation_state"],
        confidence_lower=result["confidence_lower"],
        confidence_upper=result["confidence_upper"],
        model_used=result["model_used"],
        model_version=result.get("model_version", "2.0.0"),
    )


# ── Batch prediction ─────────────────────────────────────────────────────────
@router.post("/predict/batch", response_model=BatchPredictResponse)
async def predict_batch_v2(req: BatchPredictRequest):
    """Predict SOH for multiple cycles using v2 models."""
    results = registry_v2.predict_batch(req.battery_id, req.cycles)
    predictions = [
        PredictResponse(
            battery_id=req.battery_id,
            cycle_number=r["cycle_number"],
            soh_pct=r["soh_pct"],
            rul_cycles=r["rul_cycles"],
            degradation_state=r["degradation_state"],
            confidence_lower=r.get("confidence_lower"),
            confidence_upper=r.get("confidence_upper"),
            model_used=r["model_used"],
            model_version=r.get("model_version", "2.0.0"),
        )
        for r in results
    ]
    return BatchPredictResponse(battery_id=req.battery_id, predictions=predictions)


# ── Recommendations (v2 — fixed) ────────────────────────────────────────────
@router.post("/recommend", response_model=RecommendationResponse)
async def recommend_v2(req: RecommendationRequest):
    """Get operational recommendations using v2 models.

    v2 FIX: Uses user-provided current_soh to compute baseline RUL instead
    of re-predicting SOH from default features (which caused ~0 cycle
    improvement in v1).
    """
    import itertools

    temps = [4.0, 24.0, 43.0]
    currents = [0.5, 1.0, 2.0, 4.0]
    cutoffs = [2.0, 2.2, 2.5, 2.7]

    # v2 FIX: compute baseline RUL from user-provided current_soh
    # Data-driven: linear degradation at a realistic rate (~0.2%/cycle)
    EOL_THRESHOLD = 70.0
    deg_rate = 0.2  # conservative NASA-calibrated %/cycle
    if req.current_soh > EOL_THRESHOLD:
        baseline_rul = (req.current_soh - EOL_THRESHOLD) / deg_rate
    else:
        baseline_rul = 0.0

    base_features = {
        "cycle_number": req.current_cycle,
        "ambient_temperature": req.ambient_temperature,
        "peak_voltage": 4.19,
        "min_voltage": 2.61,
        "voltage_range": 4.19 - 2.61,
        "avg_current": 1.82,
        "avg_temp": req.ambient_temperature + 8.0,
        "temp_rise": 15.0,
        "cycle_duration": 3690.0,
        "Re": 0.045,
        "Rct": 0.069,
        "delta_capacity": -0.005,
    }

    candidates = []
    for t, c, v in itertools.product(temps, currents, cutoffs):
        feat = {**base_features, "ambient_temperature": t, "avg_current": c,
                "min_voltage": v, "voltage_range": 4.19 - v,
                "avg_temp": t + 8.0}
        result = registry_v2.predict(feat)
        rul = result.get("rul_cycles", 0) or 0
        candidates.append((rul, t, c, v, result["soh_pct"]))

    candidates.sort(reverse=True)
    top = candidates[: req.top_k]

    recs = []
    for rank, (rul, t, c, v, soh) in enumerate(top, 1):
        improvement = rul - baseline_rul
        pct = (improvement / baseline_rul * 100) if baseline_rul > 0 else 0
        recs.append(SingleRecommendation(
            rank=rank,
            ambient_temperature=t,
            discharge_current=c,
            cutoff_voltage=v,
            predicted_rul=rul,
            rul_improvement=improvement,
            rul_improvement_pct=round(pct, 1),
            explanation=f"Operate at {t}°C, {c}A, cutoff {v}V for ~{rul:.0f} cycles RUL",
        ))

    return RecommendationResponse(
        battery_id=req.battery_id,
        current_soh=req.current_soh,
        recommendations=recs,
    )


# ── Model listing ────────────────────────────────────────────────────────────
@router.get("/models")
async def list_models_v2():
    """List all v2 registered models."""
    return registry_v2.list_models()