ai_analysis_old.py

"""
AI Tactical Analysis System
Uses Qwen2.5-Coder-1.5B via shared model manager
ONLY uses the single shared LLM instance - NO separate process fallback
"""
import os
import re
import json
import time
from typing import Optional, Dict, Any, List
from pathlib import Path

# Import shared model manager (REQUIRED - no fallback)
from model_manager import get_shared_model

USE_SHARED_MODEL = True  # Always true now

# Global model download status (polled by server for UI)
_MODEL_DOWNLOAD_STATUS: Dict[str, Any] = {
    'status': 'idle',   # idle | starting | downloading | retrying | done | error
    'percent': 0,
    'note': '',
    'path': ''
}

def _update_model_download_status(update: Dict[str, Any]) -> None:
    try:
        _MODEL_DOWNLOAD_STATUS.update(update)
    except Exception:
        pass

def get_model_download_status() -> Dict[str, Any]:
    return dict(_MODEL_DOWNLOAD_STATUS)


# OLD _llama_worker function REMOVED
# This function loaded a SECOND LLM instance in a separate process
# Caused: "falling back to process isolation" + duplicate 1GB model load
# Now we ONLY use the shared model manager - single LLM instance


class AIAnalyzer:


def _llama_worker(result_queue, model_path, prompt, messages, max_tokens, temperature):
    """
    Worker process for LLM inference.
    
    Runs in separate process to isolate native library crashes.
    """
    try:
        from typing import cast
        from llama_cpp import Llama, ChatCompletionRequestMessage
    except Exception as exc:
        result_queue.put({'status': 'error', 'message': f"llama-cpp import failed: {exc}"})
        return
    
    # Try loading the model with best-suited chat template for Qwen2.5
    n_threads = max(1, min(4, os.cpu_count() or 2))
    last_exc = None
    llama = None
    for chat_fmt in ('qwen2', 'qwen', None):
        try:
            kwargs: Dict[str, Any] = dict(
                model_path=model_path,
                n_ctx=4096,
                n_threads=n_threads,
                verbose=False,
            )
            if chat_fmt is not None:
                kwargs['chat_format'] = chat_fmt  # type: ignore[index]
            llama = Llama(**kwargs)  # type: ignore[arg-type]
            break
        except Exception as exc:
            last_exc = exc
            llama = None
            continue
    if llama is None:
        result_queue.put({'status': 'error', 'message': f"Failed to load model: {last_exc}"})
        return
    
    try:
        # Build message payload
        payload: List[ChatCompletionRequestMessage] = []
        if messages:
            for msg in messages:
                if not isinstance(msg, dict):
                    continue
                role = msg.get('role')
                content = msg.get('content')
                if not isinstance(role, str) or not isinstance(content, str):
                    continue
                payload.append(cast(ChatCompletionRequestMessage, {
                    'role': role,
                    'content': content
                }))
        
        if not payload:
            base_prompt = prompt or ''
            if base_prompt:
                payload = [cast(ChatCompletionRequestMessage, {
                    'role': 'user',
                    'content': base_prompt
                })]
            else:
                payload = [cast(ChatCompletionRequestMessage, {
                    'role': 'user',
                    'content': ''
                })]
        
        # Try chat completion
        try:
            resp = llama.create_chat_completion(
                messages=payload,
                max_tokens=max_tokens,
                temperature=temperature,
            )
        except Exception:
            resp = None
        
        # Extract text from response
        text = None
        if isinstance(resp, dict):
            choices = resp.get('choices') or []
            if choices:
                parts = []
                for choice in choices:
                    if isinstance(choice, dict):
                        part = (
                            choice.get('text') or
                            (choice.get('message') or {}).get('content') or
                            ''
                        )
                        parts.append(str(part))
                text = '\n'.join(parts).strip()
            if not text and 'text' in resp:
                text = str(resp.get('text'))
        elif resp is not None:
            text = str(resp)
        
        # Fallback to direct generation if chat failed
        if not text:
            try:
                raw_resp = llama(
                    prompt or '',
                    max_tokens=max_tokens,
                    temperature=temperature,
                    stop=["</s>", "<|endoftext|>"]
                )
            except Exception:
                raw_resp = None
            
            if isinstance(raw_resp, dict):
                choices = raw_resp.get('choices') or []
                if choices:
                    parts = []
                    for choice in choices:
                        if isinstance(choice, dict):
                            part = (
                                choice.get('text') or
                                (choice.get('message') or {}).get('content') or
                                ''
                            )
                            parts.append(str(part))
                    text = '\n'.join(parts).strip()
                if not text and 'text' in raw_resp:
                    text = str(raw_resp.get('text'))
            elif raw_resp is not None:
                text = str(raw_resp)
        
        if not text:
            text = ''
        
        # Clean up response text
        cleaned = text.replace('<</SYS>>', ' ').replace('[/INST]', ' ').replace('[INST]', ' ')
        cleaned = re.sub(r'</s><s>', ' ', cleaned)
        cleaned = re.sub(r'</?s>', ' ', cleaned)
        cleaned = re.sub(r'```\w*', '', cleaned)
        cleaned = cleaned.replace('```', '')
        
        # Remove thinking tags (Qwen models)
        cleaned = re.sub(r'<think>.*?</think>', '', cleaned, flags=re.DOTALL)
        cleaned = re.sub(r'<think>.*', '', cleaned, flags=re.DOTALL)
        cleaned = cleaned.strip()
        
        # Try to extract JSON objects
        def extract_json_objects(s: str):
            objs = []
            stack = []
            start = None
            for idx, ch in enumerate(s):
                if ch == '{':
                    if not stack:
                        start = idx
                    stack.append('{')
                elif ch == '}':
                    if stack:
                        stack.pop()
                        if not stack and start is not None:
                            candidate = s[start:idx + 1]
                            objs.append(candidate)
                            start = None
            return objs
        
        parsed_json = None
        try:
            for candidate in extract_json_objects(cleaned):
                try:
                    parsed = json.loads(candidate)
                    parsed_json = parsed
                    break
                except Exception:
                    continue
        except Exception:
            parsed_json = None
        
        if parsed_json is not None:
            result_queue.put({'status': 'ok', 'data': parsed_json})
        else:
            result_queue.put({'status': 'ok', 'data': {'raw': cleaned}})
    
    except Exception as exc:
        result_queue.put({'status': 'error', 'message': f"Generation failed: {exc}"})


class AIAnalyzer:
    """
    AI Tactical Analysis System
    
    Provides battlefield analysis using Qwen2.5-0.5B model.
    Uses shared model manager to avoid duplicate loading with NL interface.
    """
    
    def __init__(self, model_path: Optional[str] = None):
        """Initialize AI analyzer with model path"""
        if model_path is None:
            # Try default locations (existing files)
            possible_paths = [
                Path("./qwen2.5-coder-1.5b-instruct-q4_0.gguf"),
                Path("../qwen2.5-coder-1.5b-instruct-q4_0.gguf"),
                Path.home() / "rts" / "qwen2.5-coder-1.5b-instruct-q4_0.gguf",
                Path.home() / ".cache" / "rts" / "qwen2.5-coder-1.5b-instruct-q4_0.gguf",
                Path("/data/qwen2.5-coder-1.5b-instruct-q4_0.gguf"),
                Path("/tmp/rts/qwen2.5-coder-1.5b-instruct-q4_0.gguf"),
            ]

            for path in possible_paths:
                try:
                    if path.exists():
                        model_path = str(path)
                        break
                except Exception:
                    continue
        
        self.model_path = model_path
        self.model_available = model_path is not None and Path(model_path).exists()
        
        # Use shared model manager if available
        self.use_shared = USE_SHARED_MODEL
        self.shared_model = None
        if self.use_shared:
            try:
                self.shared_model = get_shared_model()
                # Ensure model is loaded
                if self.model_available and model_path:
                    success, error = self.shared_model.load_model(Path(model_path).name)
                    if success:
                        print(f"✓ AI Analysis using SHARED model: {Path(model_path).name}")
                    else:
                        print(f"⚠️ Failed to load shared model: {error}")
                        self.use_shared = False
            except Exception as e:
                print(f"⚠️ Shared model unavailable: {e}")
                self.use_shared = False
        
        if not self.model_available:
            print(f"⚠️ AI Model not found. Attempting automatic download...")
            
            # Try to download the model automatically
            try:
                import sys
                import urllib.request
                
                model_url = "https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF/resolve/main/qwen2.5-coder-1.5b-instruct-q4_0.gguf"
                # Fallback URL (blob with download param)
                alt_url = "https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF/blob/main/qwen2.5-coder-1.5b-instruct-q4_0.gguf?download=1"
                # Choose a writable destination directory
                filename = "qwen2.5-coder-1.5b-instruct-q4_0.gguf"
                candidate_dirs = [
                    Path(os.getenv("RTS_MODEL_DIR", "")),
                    Path.cwd(),
                    Path(__file__).resolve().parent,                 # /web
                    Path(__file__).resolve().parent.parent,          # repo root
                    Path.home() / "rts",
                    Path.home() / ".cache" / "rts",
                    Path("/data"),
                    Path("/tmp") / "rts",
                ]
                default_path: Path = Path.cwd() / filename
                for d in candidate_dirs:
                    try:
                        if not str(d):
                            continue
                        d.mkdir(parents=True, exist_ok=True)
                        test_file = d / (".write_test")
                        with open(test_file, 'w') as tf:
                            tf.write('ok')
                        test_file.unlink(missing_ok=True)  # type: ignore[arg-type]
                        default_path = d / filename
                        break
                    except Exception:
                        continue
                
                _update_model_download_status({
                    'status': 'starting',
                    'percent': 0,
                    'note': 'starting',
                    'path': str(default_path)
                })
                print(f"📦 Downloading model (~350 MB)...")
                print(f"   From: {model_url}")
                print(f"   To: {default_path}")
                print(f"   This may take a few minutes...")
                
                # Simple progress callback
                def progress_callback(block_num, block_size, total_size):
                    if total_size > 0 and block_num % 100 == 0:
                        downloaded = block_num * block_size
                        percent = min(100, (downloaded / total_size) * 100)
                        mb_downloaded = downloaded / (1024 * 1024)
                        mb_total = total_size / (1024 * 1024)
                        _update_model_download_status({
                            'status': 'downloading',
                            'percent': round(percent, 1),
                            'note': f"{mb_downloaded:.1f}/{mb_total:.1f} MB",
                            'path': str(default_path)
                        })
                        print(f"   Progress: {percent:.1f}% ({mb_downloaded:.1f}/{mb_total:.1f} MB)", end='\r')
                
                # Ensure destination directory exists (should already be validated)
                try:
                    default_path.parent.mkdir(parents=True, exist_ok=True)
                except Exception:
                    pass
                
                success = False
                for attempt in range(3):
                    try:
                        # Try urllib first
                        urllib.request.urlretrieve(model_url, default_path, reporthook=progress_callback)
                        success = True
                        break
                    except Exception:
                        # Fallback to requests streaming
                        # Attempt streaming with requests if available
                        used_requests = False
                        try:
                            try:
                                import requests  # type: ignore
                            except Exception:
                                requests = None  # type: ignore
                            if requests is not None:  # type: ignore
                                with requests.get(model_url, stream=True, timeout=60) as r:  # type: ignore
                                    r.raise_for_status()
                                    total = int(r.headers.get('Content-Length', 0))
                                    downloaded = 0
                                    with open(default_path, 'wb') as f:
                                        for chunk in r.iter_content(chunk_size=1024 * 1024):  # 1MB
                                            if not chunk:
                                                continue
                                            f.write(chunk)
                                            downloaded += len(chunk)
                                            if total > 0:
                                                percent = min(100, downloaded * 100 / total)
                                                _update_model_download_status({
                                                    'status': 'downloading',
                                                    'percent': round(percent, 1),
                                                    'note': f"{downloaded/1048576:.1f}/{total/1048576:.1f} MB",
                                                    'path': str(default_path)
                                                })
                                                print(f"   Progress: {percent:.1f}% ({downloaded/1048576:.1f}/{total/1048576:.1f} MB)", end='\r')
                                success = True
                                used_requests = True
                                break
                        except Exception:
                            # ignore and try alternative below
                            pass
                        # Last chance this attempt: alternative URL via urllib
                        try:
                            urllib.request.urlretrieve(alt_url, default_path, reporthook=progress_callback)
                            success = True
                            break
                        except Exception as e:
                            wait = 2 ** attempt
                            _update_model_download_status({
                                'status': 'retrying',
                                'percent': 0,
                                'note': f"attempt {attempt+1} failed: {e}",
                                'path': str(default_path)
                            })
                            print(f"   Download attempt {attempt+1}/3 failed: {e}. Retrying in {wait}s...")
                            time.sleep(wait)
                
                print()  # New line after progress
                
                # Verify download
                if success and default_path.exists():
                    size_mb = default_path.stat().st_size / (1024 * 1024)
                    print(f"✅ Model downloaded successfully! ({size_mb:.1f} MB)")
                    self.model_path = str(default_path)
                    self.model_available = True
                    _update_model_download_status({
                        'status': 'done',
                        'percent': 100,
                        'note': f"{size_mb:.1f} MB",
                        'path': str(default_path)
                    })
                else:
                    print(f"❌ Download failed. Tactical analysis disabled.")
                    print(f"   Manual download: https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF")
                    _update_model_download_status({
                        'status': 'error',
                        'percent': 0,
                        'note': 'download failed',
                        'path': str(default_path)
                    })
                    
            except Exception as e:
                print(f"❌ Auto-download failed: {e}")
                print(f"   Tactical analysis disabled.")
                print(f"   Manual download: https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF")
                _update_model_download_status({
                    'status': 'error',
                    'percent': 0,
                    'note': str(e),
                    'path': ''
                })
    
    def generate_response(
        self, 
        prompt: Optional[str] = None, 
        messages: Optional[List[Dict]] = None,
        max_tokens: int = 200,  # Reduced for faster analysis
        temperature: float = 0.7,
        timeout: float = 15.0  # Shorter timeout to avoid blocking game
    ) -> Dict[str, Any]:
        """
        Generate LLM response (uses shared model if available, falls back to separate process).
        
        Args:
            prompt: Direct prompt string
            messages: Chat-style messages [{"role": "user", "content": "..."}]
            max_tokens: Maximum tokens to generate
            temperature: Sampling temperature
            timeout: Timeout in seconds
            
        Returns:
            Dict with 'status' and 'data' or 'message'
        """
        if not self.model_available:
            return {
                'status': 'error',
                'message': 'Model not available'
            }
        
        # ONLY use shared model - NO fallback to separate process
        # This prevents loading a second LLM instance
        if self.use_shared and self.shared_model and self.shared_model.model_loaded:
            try:
                # Convert prompt to messages if needed
                msg_list = messages if messages else [{"role": "user", "content": prompt or ""}]
                
                success, response_text, error = self.shared_model.generate(
                    messages=msg_list,
                    max_tokens=max_tokens,
                    temperature=temperature,
                    timeout=timeout
                )
                
                if success and response_text:
                    # Try to parse JSON from response
                    try:
                        cleaned = response_text.strip()
                        # Try to extract JSON
                        match = re.search(r'\{[^{}]*\}', cleaned, re.DOTALL)
                        if match:
                            parsed = json.loads(match.group(0))
                            return {'status': 'ok', 'data': parsed}
                        else:
                            return {'status': 'ok', 'data': {'raw': cleaned}}
                    except:
                        return {'status': 'ok', 'data': {'raw': response_text}}
                else:
                    # If shared model busy/timeout, return error (caller will use heuristic)
                    print(f"⚠️ Shared model unavailable: {error} (will use heuristic analysis)")
                    return {'status': 'error', 'message': f'Shared model busy: {error}'}
            except Exception as e:
                print(f"⚠️ Shared model error: {e} (will use heuristic analysis)")
                return {'status': 'error', 'message': f'Shared model error: {str(e)}'}
        
        # No shared model available
        return {'status': 'error', 'message': 'Shared model not loaded'}
        
        # OLD CODE REMOVED: Fallback multiprocess that loaded a second LLM
        # This caused the "falling back to process isolation" message
        # and loaded a duplicate 1GB model, causing lag and memory waste
        
        worker_process.start()
        
        try:
            result = result_queue.get(timeout=timeout)
            worker_process.join(timeout=5.0)
            return result
        except queue.Empty:
            worker_process.terminate()
            worker_process.join(timeout=5.0)
            if worker_process.is_alive():
                worker_process.kill()
                worker_process.join()
            return {'status': 'error', 'message': 'Generation timeout'}
        except Exception as exc:
            worker_process.terminate()
            worker_process.join(timeout=5.0)
            return {'status': 'error', 'message': str(exc)}
    
    def _heuristic_analysis(self, game_state: Dict, language_code: str) -> Dict[str, Any]:
        """Lightweight, deterministic analysis when LLM is unavailable."""
        from localization import LOCALIZATION
        lang = language_code or "en"
        lang_name = LOCALIZATION.get_ai_language_name(lang)

        player_units = sum(1 for u in game_state.get('units', {}).values() if u.get('player_id') == 0)
        enemy_units = sum(1 for u in game_state.get('units', {}).values() if u.get('player_id') == 1)
        player_buildings = sum(1 for b in game_state.get('buildings', {}).values() if b.get('player_id') == 0)
        enemy_buildings = sum(1 for b in game_state.get('buildings', {}).values() if b.get('player_id') == 1)
        player = game_state.get('players', {}).get(0, {})
        credits = int(player.get('credits', 0) or 0)
        power = int(player.get('power', 0) or 0)
        power_cons = int(player.get('power_consumption', 0) or 0)

        advantage = 'even'
        score = (player_units - enemy_units) + 0.5 * (player_buildings - enemy_buildings)
        if score > 1:
            advantage = 'ahead'
        elif score < -1:
            advantage = 'behind'

        # Localized templates (concise)
        summaries = {
            'en': {
                'ahead': f"{lang_name}: You hold the initiative. Maintain pressure and expand.",
                'even': f"{lang_name}: Battlefield is balanced. Scout and take map control.",
                'behind': f"{lang_name}: You're under pressure. Stabilize and defend key assets.",
            },
            'fr': {
                'ahead': f"{lang_name} : Vous avez l'initiative. Maintenez la pression et étendez-vous.",
                'even': f"{lang_name} : Situation équilibrée. Éclairez et prenez le contrôle de la carte.",
                'behind': f"{lang_name} : Sous pression. Stabilisez et défendez les actifs clés.",
            },
            'zh-TW': {
                'ahead': f"{lang_name}：佔據主動。保持壓力並擴張。",
                'even': f"{lang_name}：局勢均衡。偵察並掌控地圖。",
                'behind': f"{lang_name}：處於劣勢。穩住陣腳並防守關鍵建築。",
            }
        }
        summary = summaries.get(lang, summaries['en'])[advantage]

        tips: List[str] = []
        # Power management tips
        if power_cons > 0 and power < power_cons:
            tips.append({
                'en': 'Build a Power Plant to restore production speed',
                'fr': 'Construisez une centrale pour rétablir la production',
                'zh-TW': '建造發電廠以恢復生產速度'
            }.get(lang, 'Build a Power Plant to restore production speed'))

        # Economy tips
        if credits < 300:
            tips.append({
                'en': 'Protect Harvester and secure more ore',
                'fr': 'Protégez le collecteur et sécurisez plus de minerai',
                'zh-TW': '保護採礦車並確保更多礦石'
            }.get(lang, 'Protect Harvester and secure more ore'))

        # Army composition tips
        if player_buildings > 0:
            if player_units < enemy_units:
                tips.append({
                    'en': 'Train Infantry and add Tanks for frontline',
                    'fr': 'Entraînez de l’infanterie et ajoutez des chars en première ligne',
                    'zh-TW': '訓練步兵並加入坦克作為前線'
                }.get(lang, 'Train Infantry and add Tanks for frontline'))
            else:
                tips.append({
                    'en': 'Scout enemy base and pressure weak flanks',
                    'fr': 'Éclairez la base ennemie et mettez la pression sur les flancs faibles',
                    'zh-TW': '偵察敵方基地並壓制薄弱側翼'
                }.get(lang, 'Scout enemy base and pressure weak flanks'))

        # Defense tip if buildings disadvantage
        if player_buildings < enemy_buildings:
            tips.append({
                'en': 'Fortify around HQ and key production buildings',
                'fr': 'Fortifiez autour du QG et des bâtiments de production',
                'zh-TW': '在總部與生產建築周圍加強防禦'
            }.get(lang, 'Fortify around HQ and key production buildings'))

        # Coach line
        coach = {
            'en': 'Keep your economy safe and strike when you see an opening.',
            'fr': 'Protégez votre économie et frappez dès qu’une ouverture se présente.',
            'zh-TW': '保護經濟，抓住機會果斷出擊。'
        }.get(lang, 'Keep your economy safe and strike when you see an opening.')

        return { 'summary': summary, 'tips': tips[:4] or ['Build more units'], 'coach': coach, 'source': 'heuristic' }

    def summarize_combat_situation(
        self, 
        game_state: Dict, 
        language_code: str = "en"
    ) -> Dict[str, Any]:
        """
        Generate tactical analysis of current battle.
        
        Args:
            game_state: Current game state dictionary
            language_code: Language for response (en, fr, zh-TW)
            
        Returns:
            Dict with keys: summary, tips, coach
        """
        # If LLM is not available, return heuristic result
        if not self.model_available:
            return self._heuristic_analysis(game_state, language_code)
        
        # Import here to avoid circular dependency
        from localization import LOCALIZATION
        
        language_name = LOCALIZATION.get_ai_language_name(language_code)
        
        # Build tactical summary prompt
        player_units = sum(1 for u in game_state.get('units', {}).values() 
                          if u.get('player_id') == 0)
        enemy_units = sum(1 for u in game_state.get('units', {}).values() 
                         if u.get('player_id') == 1)
        player_buildings = sum(1 for b in game_state.get('buildings', {}).values() 
                              if b.get('player_id') == 0)
        enemy_buildings = sum(1 for b in game_state.get('buildings', {}).values() 
                             if b.get('player_id') == 1)
        player_credits = game_state.get('players', {}).get(0, {}).get('credits', 0)
        
        example_summary = LOCALIZATION.get_ai_example_summary(language_code)
        
        prompt = (
            f"You are an expert RTS (Red Alert style) commentator & coach. Return ONLY one <json>...</json> block.\n"
            f"JSON keys: summary (string concise tactical overview), tips (array of 1-4 short imperative build/composition suggestions), coach (1 motivational/adaptive sentence).\n"
            f"No additional keys. No text outside tags. Language: {language_name}.\n"
            f"\n"
            f"Battle state: Player {player_units} units vs Enemy {enemy_units} units. "
            f"Player {player_buildings} buildings vs Enemy {enemy_buildings} buildings. "
            f"Credits: {player_credits}.\n"
            f"\n"
            f"Example JSON:\n"
            f'{{"summary": "{example_summary}", '
            f'"tips": ["Build more tanks", "Defend north base", "Scout enemy position"], '
            f'"coach": "You are doing well; keep pressure on the enemy."}}\n'
            f"\n"
            f"Generate tactical analysis in {language_name}:"
        )
        
        result = self.generate_response(
            prompt=prompt,
            max_tokens=200,  # Reduced for faster response
            temperature=0.7,
            timeout=15.0  # Shorter timeout
        )
        
        if result.get('status') != 'ok':
            # Fallback to heuristic on error
            return self._heuristic_analysis(game_state, language_code)
        
        data = result.get('data', {})
        
        # Try to extract fields from structured JSON first
        summary = str(data.get('summary') or '').strip()
        tips_raw = data.get('tips') or []
        coach = str(data.get('coach') or '').strip()
        
        # If no structured data, try to parse raw text
        if not summary and 'raw' in data:
            raw_text = str(data.get('raw', '')).strip()
            # Use the first sentence or the whole text as summary
            sentences = raw_text.split('.')
            if sentences:
                summary = sentences[0].strip() + '.'
            else:
                summary = raw_text[:150]  # Max 150 chars
            
            # Try to extract tips from remaining text
            # Look for patterns like "Build X", "Defend Y", etc.
            import re
            tip_patterns = [
                r'Build [^.]+',
                r'Defend [^.]+',
                r'Attack [^.]+',
                r'Scout [^.]+',
                r'Expand [^.]+',
                r'Protect [^.]+',
                r'Train [^.]+',
                r'Produce [^.]+',
            ]
            
            found_tips = []
            for pattern in tip_patterns:
                matches = re.findall(pattern, raw_text, re.IGNORECASE)
                found_tips.extend(matches[:2])  # Max 2 per pattern
            
            if found_tips:
                tips_raw = found_tips[:4]  # Max 4 tips
            
            # Use remaining text as coach message
            if len(sentences) > 1:
                coach = '. '.join(sentences[1:3]).strip()  # 2nd and 3rd sentences
        
        # Validate tips is array
        tips = []
        if isinstance(tips_raw, list):
            for tip in tips_raw:
                if isinstance(tip, str):
                    tips.append(tip.strip())
        
        # Fallbacks
        if not summary or not tips or not coach:
            fallback = self._heuristic_analysis(game_state, language_code)
            summary = summary or fallback['summary']
            tips = tips or fallback['tips']
            coach = coach or fallback['coach']
        
        return {
            'summary': summary,
            'tips': tips[:4],  # Max 4 tips
            'coach': coach,
            'source': 'llm'
        }


# Singleton instance (lazy initialization)
_ai_analyzer_instance: Optional[AIAnalyzer] = None

def get_ai_analyzer() -> AIAnalyzer:
    """Get singleton AI analyzer instance"""
    global _ai_analyzer_instance
    if _ai_analyzer_instance is None:
        _ai_analyzer_instance = AIAnalyzer()
    return _ai_analyzer_instance