"""
Prompt template for strategy brainstorming.
"""
from typing import List
from models.input_models import EnrichedTelemetryWebhook, RaceContext
from utils.validators import TelemetryAnalyzer
from config import get_settings


def build_brainstorm_prompt_fast(
    enriched_telemetry: List[EnrichedTelemetryWebhook],
    race_context: RaceContext
) -> str:
    """Build a faster, more concise prompt for quicker responses."""
    latest = max(enriched_telemetry, key=lambda x: x.lap)
    tire_rate = TelemetryAnalyzer.calculate_tire_degradation_rate(enriched_telemetry)
    tire_cliff = TelemetryAnalyzer.project_tire_cliff(enriched_telemetry, race_context.race_info.current_lap)
    
    return f"""Generate 20 F1 race strategies for {race_context.driver_state.driver_name} at {race_context.race_info.track_name}.

CURRENT: Lap {race_context.race_info.current_lap}/{race_context.race_info.total_laps}, P{race_context.driver_state.current_position}, {race_context.driver_state.current_tire_compound} tires ({race_context.driver_state.tire_age_laps} laps old)

TELEMETRY: Aero {latest.aero_efficiency:.2f}, Tire deg {latest.tire_degradation_index:.2f} (rate {tire_rate:.3f}/lap, cliff lap {tire_cliff}), ERS {latest.ers_charge:.2f}, Fuel {latest.fuel_optimization_score:.2f}, Consistency {latest.driver_consistency:.2f}

Generate 20 strategies: 4 conservative (1-stop), 6 standard (1-2 stop), 6 aggressive (undercut/overcut), 2 reactive, 2 contingency (SC/rain).

Rules: Pit laps {race_context.race_info.current_lap + 1}-{race_context.race_info.total_laps - 1}, min 2 compounds.

JSON format:
{{"strategies": [{{"strategy_id": 1, "strategy_name": "name", "stop_count": 1, "pit_laps": [32], "tire_sequence": ["medium", "hard"], "brief_description": "one sentence", "risk_level": "low|medium|high|critical", "key_assumption": "main assumption"}}]}}"""


def build_brainstorm_prompt(
    enriched_telemetry: List[EnrichedTelemetryWebhook],
    race_context: RaceContext
) -> str:
    """
    Build the brainstorm prompt for Gemini.
    
    Args:
        enriched_telemetry: Recent enriched telemetry data
        race_context: Current race context
        
    Returns:
        Formatted prompt string
    """
    # Generate telemetry summary
    telemetry_summary = TelemetryAnalyzer.generate_telemetry_summary(enriched_telemetry)
    
    # Calculate key metrics
    tire_rate = TelemetryAnalyzer.calculate_tire_degradation_rate(enriched_telemetry)
    tire_cliff_lap = TelemetryAnalyzer.project_tire_cliff(
        enriched_telemetry,
        race_context.race_info.current_lap
    )
    
    # Format telemetry data
    telemetry_data = []
    for t in sorted(enriched_telemetry, key=lambda x: x.lap, reverse=True)[:10]:
        telemetry_data.append({
            "lap": t.lap,
            "aero_efficiency": round(t.aero_efficiency, 3),
            "tire_degradation_index": round(t.tire_degradation_index, 3),
            "ers_charge": round(t.ers_charge, 3),
            "fuel_optimization_score": round(t.fuel_optimization_score, 3),
            "driver_consistency": round(t.driver_consistency, 3),
            "weather_impact": t.weather_impact
        })
    
    # Format competitors
    competitors_data = []
    for c in race_context.competitors:
        competitors_data.append({
            "position": c.position,
            "driver": c.driver,
            "tire_compound": c.tire_compound,
            "tire_age_laps": c.tire_age_laps,
            "gap_seconds": round(c.gap_seconds, 1)
        })
    
    prompt = f"""You are an expert F1 strategist. Generate 20 diverse race strategies.

TELEMETRY METRICS:
- aero_efficiency: <0.6 problem, >0.8 optimal
- tire_degradation_index: >0.7 degrading, >0.85 cliff
- ers_charge: >0.7 attack, <0.3 depleted
- fuel_optimization_score: <0.7 save fuel
- driver_consistency: <0.75 risky
- weather_impact: severity level

RACE STATE:
Track: {race_context.race_info.track_name}
Current Lap: {race_context.race_info.current_lap} / {race_context.race_info.total_laps}
Weather: {race_context.race_info.weather_condition}
Track Temperature: {race_context.race_info.track_temp_celsius}°C

DRIVER STATE:
Driver: {race_context.driver_state.driver_name}
Position: P{race_context.driver_state.current_position}
Current Tires: {race_context.driver_state.current_tire_compound} ({race_context.driver_state.tire_age_laps} laps old)
Fuel Remaining: {race_context.driver_state.fuel_remaining_percent}%

COMPETITORS:
{competitors_data}

ENRICHED TELEMETRY (Last {len(telemetry_data)} laps, newest first):
{telemetry_data}

TELEMETRY ANALYSIS:
{telemetry_summary}

KEY INSIGHTS:
- Tire degradation rate: {tire_rate:.3f} per lap
- Projected tire cliff: Lap {tire_cliff_lap}
- Laps remaining: {race_context.race_info.total_laps - race_context.race_info.current_lap}

TASK: Generate exactly 20 diverse strategies.

DIVERSITY: Conservative (1-stop), Standard (balanced), Aggressive (undercut), Reactive (competitor), Contingency (safety car)

RULES:
- Pit laps: {race_context.race_info.current_lap + 1} to {race_context.race_info.total_laps - 1}
- Min 2 tire compounds (F1 rule)
- Time pits before tire cliff (projected lap {tire_cliff_lap})

For each strategy provide:
- strategy_id: 1-20
- strategy_name: Short descriptive name
- stop_count: 1, 2, or 3
- pit_laps: [array of lap numbers]
- tire_sequence: [array of compounds: "soft", "medium", "hard"]
- brief_description: One sentence rationale
- risk_level: "low", "medium", "high", or "critical"
- key_assumption: Main assumption this strategy relies on

OUTPUT FORMAT (JSON only, no markdown):
{{
  "strategies": [
    {{
      "strategy_id": 1,
      "strategy_name": "Conservative 1-Stop",
      "stop_count": 1,
      "pit_laps": [32],
      "tire_sequence": ["medium", "hard"],
      "brief_description": "Extend mediums to lap 32, safe finish on hards",
      "risk_level": "low",
      "key_assumption": "Tire degradation stays below 0.85 until lap 32"
    }}
  ]
}}"""
    
    return prompt