from typing import Optional, Dict, List, Literal
from pydantic import BaseModel, Field
from datetime import date
from domain.training.weekly_snapshot import WeeklySnapshot
from domain.training.weekly_trend import WeeklyTrend
from domain.runner_positioning import TrainingPhase
from _app.presentation.ui_text import get_text

class WeeklyPositioning(BaseModel):
    """
    Application-layer model for Positioning Intelligence v1.
    Narrative-focused assessment of the runner's current standing.
    """
    status: Literal["CONSTRUCTIVE_ADAPTATION", "PRODUCTIVE_LOAD", "STRAIN", "PLATEAU", "BASELINE_BUILDING"]
    signal_strength: float = 1.0 # 0.0 to 1.0
    rationale: str
    training_phase: TrainingPhase = TrainingPhase.BASE
    
    @property
    def status_value(self) -> str:
        return self.status

class PositioningEngine:
    """
    Engine to compute high-level positioning status from snapshots and trends.
    """
    def detect_training_phase(self, snapshot: WeeklySnapshot, trend: WeeklyTrend) -> TrainingPhase:
        """
        Infer training phase from workload and trend signals.
        """
        distance_delta = trend.distance_delta_pct or 0
        pace_delta = trend.pace_delta_s_per_km or 0
        run_count = snapshot.run_count
        # consistency = snapshot.consistency_score or 0

        # Recovery phase (very low activity and sharp drop in load)
        if run_count <= 1 and distance_delta < -80:
            return TrainingPhase.RECOVERY

        # Plateau phase (load dropped or adaptation stalled)
        if distance_delta < -20:
            return TrainingPhase.PLATEAU

        # Build phase (clear volume increase)
        if distance_delta > 5:
            return TrainingPhase.BUILD

        # Peak phase (performance improving without load reduction)
        if pace_delta < -5 and distance_delta >= 0:
            return TrainingPhase.PEAK

        # Default: base training
        return TrainingPhase.BASE

    def compute(self, snapshot: WeeklySnapshot, trend: WeeklyTrend) -> WeeklyPositioning:
        if not trend or not trend.comparison_available:
            return WeeklyPositioning(
                status="BASELINE_BUILDING",
                rationale="positioning_rationale_baseline",
                signal_strength=0.1,
                training_phase=TrainingPhase.BASE
            )

        # 1. Logic to determine status
        # This is a v1 heuristic mapping
        status = "PLATEAU"
        rationale_key = "positioning_rationale_plateau"
        
        if trend.comparison_available:
            # Constructive Adaptation: Increasing load with positive/stable consistency
            if trend.distance_delta_pct > 5.0 and trend.consistency_delta >= -5.0:
                 status = "CONSTRUCTIVE_ADAPTATION"
                 rationale_key = "positioning_rationale_constructive_adaptation"
            # Strain: High load increase or high HR increase
            elif trend.distance_delta_pct > 20.0 or (trend.hr_delta and trend.hr_delta > 5.0):
                status = "STRAIN"
                rationale_key = "positioning_rationale_strain"
            # Productive Load: Stable or slightly increasing load
            elif trend.distance_delta_pct >= -5.0:
                status = "PRODUCTIVE_LOAD"
                rationale_key = "positioning_rationale_productive_load"
        
        training_phase = self.detect_training_phase(snapshot, trend)

        return WeeklyPositioning(
            status=status,
            rationale=rationale_key, # Store key here
            signal_strength=1.0 if trend.comparison_available else 0.5,
            training_phase=training_phase
        )

def build_positioning_view(
    snapshot: WeeklySnapshot,
    trend: WeeklyTrend,
    positioning: WeeklyPositioning,
    goal_progress: Optional[dict] = None,
    language: str = "en"
) -> dict:
    """
    Aggregates snapshot, trend and positioning into a narrative-ready dictionary.
    """
    # Baseline Building deterministic return
    if positioning.status == "BASELINE_BUILDING":
        return {
            "headline": get_text("positioning_headline_baseline", language),
            "state": "⚪ " + get_text("positioning_status_baseline", language),
            "health_signal": "🟢 " + get_text("health_stable", language),
            "goal_trajectory": get_text("trajectory_establishing", language) if goal_progress else get_text("trajectory_no_goal", language),
            "training_phase": TrainingPhase.BASE.value,
            "forward_focus": get_text("positioning_forward_focus_baseline", language),
            "trajectory": get_text("positioning_trajectory_baseline", language),
            "insight": get_text("positioning_rationale_baseline", language),
            "evidence": None
        }

    # 5. Headline Mapping
    status_lower = positioning.status.lower()
    headline = get_text(f"positioning_headline_{status_lower}", language)
    
    # 6. Forward Focus Mapping
    forward_focus = get_text(f"positioning_forward_focus_{status_lower}", language)
    
    # 7. Trajectory Narrative Mapping
    trajectory_mapping = {
        "CONSTRUCTIVE_ADAPTATION": "building",
        "PRODUCTIVE_LOAD": "stable",
        "STRAIN": "fatigue",
        "PLATEAU": "plateau"
    }
    traj_key = trajectory_mapping.get(positioning.status, "plateau")
    trajectory_narrative = get_text(f"positioning_trajectory_{traj_key}", language)
    
    # 8. Icon Mappings (Presentation Step 1)
    status_icons = {
        "CONSTRUCTIVE_ADAPTATION": "🟢",
        "PRODUCTIVE_LOAD": "🟡",
        "STRAIN": "🔴",
        "PLATEAU": "⚪"
    }
    status_icon = status_icons.get(positioning.status, "⚪")
    
    # Get localized status name
    status_name = get_text(f"positioning_status_{status_lower}", language)
    
    # 9. Evidence Extraction
    evidence = {
        "distance": trend.distance_delta_pct,
        "pace": trend.pace_delta_s_per_km,
        "hr": trend.hr_delta or 0.0,
        "frequency": int(trend.frequency_delta),
        "consistency": trend.consistency_delta
    }

    # Goal Trajectory logic
    if goal_progress:
        goal_traj_text = f"🎯 {get_text('trajectory_improving', language)}" if positioning.status == "CONSTRUCTIVE_ADAPTATION" else f"🎯 {get_text('trajectory_maintaining', language)}"
    else:
        goal_traj_text = get_text("trajectory_no_goal", language)
    
    # Map to UI names
    return {
        "headline": headline,
        "state": f"{status_icon} {status_name}",
        "health_signal": f"🟢 {get_text('health_stable', language)}" if positioning.status != "STRAIN" else f"🔴 {get_text('health_strain', language)}",
        "goal_trajectory": goal_traj_text,
        "training_phase": positioning.training_phase.value if hasattr(positioning.training_phase, "value") else positioning.training_phase,
        "forward_focus": forward_focus,
        "trajectory": trajectory_narrative,
        "insight": get_text(positioning.rationale, language), # Resolve rationale key
        "evidence": evidence
    }