import time
from backend.gaming.game_context_prompts import DQI_SCORING_PROMPTS, PROBABILITY_SIM_PROMPTS
from backend.voice.tts import TTSPipeline
from backend.ws.agent_ws import ws_manager

tts = TTSPipeline()

async def score_decision_quality(frame_sequence: list, audio_chunk: bytes, game_context: str) -> dict:
    if game_context not in DQI_SCORING_PROMPTS:
        return {"dqi_score": 50, "decision_type": "unknown", "reasoning": "Unsupported game", "timestamp": time.time()}
    
    # In full production, we'd build a prompt and pass images/bytes to gemini_flash_vision_analyze:
    # prompt = DQI_SCORING_PROMPTS[game_context]["dqi_prompt"]
    # analysis = await gemini_flash_vision_analyze(frames=frame_sequence, audio_context=audio_chunk, prompt=prompt)
    
    analysis = {
        "score": 75,
        "decision_type": "engage",
        "reasoning": "Decent crosshair placement, but rotated a bit late."
    }
    
    return {
        "dqi_score": analysis["score"],
        "decision_type": analysis["decision_type"],
        "reasoning": analysis["reasoning"],
        "timestamp": time.time()
    }

async def simulate_engagement_probability(game_state: dict, game_context: str) -> dict:
    if game_context not in PROBABILITY_SIM_PROMPTS:
        return {"win_probability_pct": 50, "key_factors": []}
        
    # In full production, build and pass the sim prompt:
    # sim_prompt = PROBABILITY_SIM_PROMPTS[game_context].format(**game_state)
    # result = await gemini_flash_analyze(sim_prompt)
    result = {
        "probability": 65,
        "factors": ["health deficit", "good utility available"]
    }
    
    return {
        "win_probability_pct": result["probability"],
        "key_factors": result["factors"]
    }

async def deliver_coaching_callout(text: str, active_persona: str):
    audio_bytes = await tts.synthesize(text, personality=active_persona, context="coaching")
    
    # Push to WS for playback
    await ws_manager.broadcast({
        "type": "audio",
        "payload": {
            "audio_data": audio_bytes.hex(),
            "mime_type": "audio/wav",
            "persona": active_persona
        }
    })