"""Phase 19: Hardware-Aware Inference Optimization.

Auto-tunes models for specific user hardware (M4 Max, GPUs, etc.) to ensure local-first performance.
"""

import logging
import json
from typing import List, Dict, Any
from agent.gemini_adapter import GeminiAdapter

logger = logging.getLogger(__name__)

class HardwareOptimizer:
    def __init__(self):
        self.adapter = GeminiAdapter()

    def optimize_for_hardware(self, hardware_specs: Dict[str, Any]) -> Dict[str, Any]:
        """Generates optimization parameters for specific hardware."""
        logger.info(f"Optimizing inference for hardware: {hardware_specs.get('model', 'unknown')}")
        
        prompt = f"""
Hardware Specifications:
{json.dumps(hardware_specs, indent=2)}

Please perform a 'Deep Optimization' analysis for this hardware.
Identify the best quantization levels, KV cache settings, and batch sizes for local-first inference.
Generate a 'Hardware-Aware Configuration' and a set of 'Performance Tuning Directives'.

Format the output as JSON:
{{
  "hardware_profile": "...",
  "quantization_strategy": "...",
  "kv_cache_config": {{...}},
  "batch_size_optimization": "...",
  "performance_tuning_directives": [...],
  "projected_latency_improvement": "..."
}}
"""
        result = self.adapter.generate(
            model="gemini-3.1-pro-preview",
            prompt=prompt,
            system_instruction="You are Timmy's Hardware Optimizer. Your goal is to ensure Timmy runs at SOTA performance on any local hardware.",
            thinking=True,
            response_mime_type="application/json"
        )
        
        optimization_data = json.loads(result["text"])
        return optimization_data