src/services/performance_card_service.py

from dataclasses import dataclass
from typing import Optional, Any
from datetime import date
import logging

from domain.training.weekly_trend import WeeklyTrend
from domain.training.weekly_snapshot import WeeklySnapshot


logger = logging.getLogger(__name__)

@dataclass
class CardData:
    week_start_date: date
    total_distance: float
    delta_vs_avg: float
    pace_delta: float
    hr_delta: Optional[float]
    consistency_score: float
    performance_direction: str  # IMPROVING, STABLE, RECOVERY, LOAD, RISK, ONBOARDING
    risk_flag: bool
    indicator_icon: str
    comparison_type: str = "none"

@dataclass
class CardViewModel:
    data: CardData
    insight_paragraph: str
    forward_focus: str
    language: str
    llm_used: bool = False

class PerformanceCardService:
    """Service for building and managing the Weekly Performance Card. Stateless and pure."""

    def build_card_data(
        self, 
        snapshot: WeeklySnapshot, 
        trend: WeeklyTrend
    ) -> CardData:
        """Builds deterministic metric payload for the performance card using hybrid trends."""
        
        # Determine direction and icon based on hybrid trend
        if not trend.comparison_available:
            direction, icon, risk_flag = "ONBOARDING", "🚀", False
        else:
            direction, icon, risk_flag = self.compute_indicator(
                trend.distance_delta_pct, 
                trend.pace_delta_s_per_km, 
                trend.hr_delta, 
                snapshot.total_distance_km
            )

        return CardData(
            week_start_date=snapshot.week_start_date,
            total_distance=snapshot.total_distance_km,
            delta_vs_avg=trend.distance_delta_pct,
            pace_delta=trend.pace_delta_s_per_km,
            hr_delta=trend.hr_delta,
            consistency_score=snapshot.consistency_score,
            performance_direction=direction,
            risk_flag=risk_flag,
            indicator_icon=icon,
            comparison_type=trend.comparison_type
        )

    def compute_indicator(
        self, 
        delta_pct: float, 
        pace_delta: float, 
        hr_delta: Optional[float],
        total_distance: float
    ) -> tuple[str, str, bool]:
        """
        Deterministic logic for performance indicators.
        Returns (direction_key, icon, risk_flag)
        """
        # Thresholds
        STABLE_THRESHOLD = 5.0  # % for distance
        HR_STABLE_THRESHOLD = 2.0 # bpm
        VOLUME_SPIKE_THRESHOLD = 25.0 # %
        HR_SPIKE_THRESHOLD = 5.0 # bpm

        # 1. Elevated Risk (Red)
        if (hr_delta is not None and hr_delta > HR_SPIKE_THRESHOLD) or delta_pct > VOLUME_SPIKE_THRESHOLD:
             return "RISK", "🔴", True

        # 2. Elevated Load (Orange)
        if delta_pct > 15.0 or (hr_delta is not None and hr_delta > HR_STABLE_THRESHOLD):
            return "LOAD", "🟠", False

        # 3. Improving (Green)
        # Improving if distance is stable/up and HR is stable/down
        if delta_pct > 2.0 and (hr_delta is None or hr_delta <= 0):
             return "IMPROVING", "🟢", False
             
        # 4. Recovery (Blue)
        if delta_pct < -10.0 and (hr_delta is None or hr_delta <= 0):
            return "RECOVERY", "🔵", False

        # 5. Stable (Yellow) - Fallback
        return "STABLE", "🟡", False

    async def generate(
        self,
        snapshot: WeeklySnapshot,
        trend: WeeklyTrend,
        brief_service: Any,
        language: str = "en"
    ) -> CardViewModel:
        """
        Main entry point for generating a performance card.
        Decides between LLM insight or deterministic onboarding.
        Stateless logic only. Observability managed by caller.
        """
        card_data = self.build_card_data(snapshot, trend)
        
        llm_used = False
        if trend.comparison_available:
            # Proceed with LLM brief generation
            insight, focus = await brief_service.generate_performance_card_insight(
                card_data, language=language
            )
            llm_used = True
        else:
            # Generate deterministic onboarding message (NO LLM)
            from _app.presentation.ui_text import get_text
            insight = get_text("performance_first_week_body", language)
            focus = get_text("performance_first_week_focus", language)
            llm_used = False

        return CardViewModel(
            data=card_data,
            insight_paragraph=insight,
            forward_focus=focus,
            language=language,
            llm_used=llm_used
        )