v1.0 - Version stable: multi-PC, détection UI-DETR-1, 3 modes exécution
- Frontend v4 accessible sur réseau local (192.168.1.40) - Ports ouverts: 3002 (frontend), 5001 (backend), 5004 (dashboard) - Ollama GPU fonctionnel - Self-healing interactif - Dashboard confiance Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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265
core/gpu/ollama_manager.py
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265
core/gpu/ollama_manager.py
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"""
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Ollama Manager - Manages VLM model lifecycle via Ollama API
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Handles:
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- Loading/unloading models to/from VRAM
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- Health checks and availability detection
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- Keep-alive management for model persistence
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"""
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import asyncio
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import logging
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from typing import List, Optional
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import aiohttp
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logger = logging.getLogger(__name__)
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class OllamaManager:
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"""
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Manages Ollama VLM model lifecycle.
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Uses Ollama's REST API to control model loading/unloading.
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Example:
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>>> manager = OllamaManager()
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>>> await manager.load_model()
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>>> is_loaded = await manager.is_model_loaded()
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>>> await manager.unload_model()
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"""
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def __init__(
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self,
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endpoint: str = "http://localhost:11434",
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model: str = "qwen3-vl:8b",
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default_keep_alive: str = "5m"
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):
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"""
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Initialize OllamaManager.
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Args:
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endpoint: Ollama API endpoint
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model: Model name to manage
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default_keep_alive: Default keep-alive duration
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"""
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self._endpoint = endpoint.rstrip("/")
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self._model = model
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self._default_keep_alive = default_keep_alive
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self._session: Optional[aiohttp.ClientSession] = None
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async def _get_session(self) -> aiohttp.ClientSession:
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"""Get or create aiohttp session."""
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if self._session is None or self._session.closed:
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self._session = aiohttp.ClientSession(
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timeout=aiohttp.ClientTimeout(total=60)
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)
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return self._session
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async def close(self) -> None:
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"""Close the HTTP session."""
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if self._session and not self._session.closed:
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await self._session.close()
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# =========================================================================
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# Health Check
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# =========================================================================
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def is_available(self) -> bool:
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"""
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Check if Ollama service is available (synchronous).
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Returns:
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True if Ollama is reachable
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"""
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import requests
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try:
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response = requests.get(f"{self._endpoint}/api/tags", timeout=5)
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return response.status_code == 200
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except Exception:
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return False
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async def is_available_async(self) -> bool:
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"""
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Check if Ollama service is available (async).
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Returns:
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True if Ollama is reachable
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"""
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try:
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session = await self._get_session()
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async with session.get(f"{self._endpoint}/api/tags") as response:
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return response.status == 200
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except Exception:
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return False
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# =========================================================================
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# Model Management
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# =========================================================================
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async def load_model(self, keep_alive: Optional[str] = None) -> bool:
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"""
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Load the model into VRAM.
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Uses a minimal generate request to trigger model loading.
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Args:
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keep_alive: How long to keep model loaded (e.g., "5m", "1h")
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Returns:
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True if model loaded successfully
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"""
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keep_alive = keep_alive or self._default_keep_alive
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try:
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session = await self._get_session()
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# Send a minimal request to load the model
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# Pour Qwen3, utiliser /nothink pour désactiver le thinking mode
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prompt = "/nothink " if "qwen" in self._model.lower() else ""
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payload = {
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"model": self._model,
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"prompt": prompt,
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"keep_alive": keep_alive,
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"stream": False,
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"options": {
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"temperature": 0.0, # Déterministe pour la classification
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"top_k": 1 # Plus rapide pour les tâches de classification
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}
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}
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logger.debug(f"Loading model {self._model} with keep_alive={keep_alive}")
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async with session.post(
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f"{self._endpoint}/api/generate",
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json=payload
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) as response:
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if response.status == 200:
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logger.info(f"Model {self._model} loaded successfully")
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return True
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else:
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text = await response.text()
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logger.error(f"Failed to load model: {response.status} - {text}")
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return False
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except asyncio.TimeoutError:
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logger.error("Timeout loading model")
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return False
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except Exception as e:
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logger.error(f"Error loading model: {e}")
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return False
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async def unload_model(self) -> bool:
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"""
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Unload the model from VRAM.
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Sets keep_alive to 0 to trigger immediate unload.
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Returns:
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True if model unloaded successfully
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"""
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try:
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session = await self._get_session()
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# Send request with keep_alive=0 to unload
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payload = {
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"model": self._model,
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"prompt": "",
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"keep_alive": 0,
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"stream": False
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}
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logger.debug(f"Unloading model {self._model}")
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async with session.post(
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f"{self._endpoint}/api/generate",
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json=payload
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) as response:
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if response.status == 200:
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logger.info(f"Model {self._model} unloaded successfully")
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return True
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else:
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text = await response.text()
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logger.error(f"Failed to unload model: {response.status} - {text}")
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return False
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except asyncio.TimeoutError:
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logger.error("Timeout unloading model")
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return False
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except Exception as e:
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logger.error(f"Error unloading model: {e}")
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return False
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async def is_model_loaded(self) -> bool:
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"""
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Check if the model is currently loaded in VRAM.
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Returns:
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True if model is loaded
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"""
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try:
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session = await self._get_session()
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async with session.get(f"{self._endpoint}/api/ps") as response:
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if response.status == 200:
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data = await response.json()
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models = data.get("models", [])
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for model_info in models:
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if model_info.get("name", "").startswith(self._model.split(":")[0]):
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return True
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return False
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else:
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logger.warning(f"Failed to check loaded models: {response.status}")
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return False
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except Exception as e:
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logger.error(f"Error checking loaded models: {e}")
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return False
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async def list_loaded_models(self) -> List[str]:
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"""
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List all currently loaded models.
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Returns:
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List of loaded model names
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"""
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try:
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session = await self._get_session()
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async with session.get(f"{self._endpoint}/api/ps") as response:
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if response.status == 200:
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data = await response.json()
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models = data.get("models", [])
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return [m.get("name", "") for m in models]
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else:
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return []
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except Exception as e:
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logger.error(f"Error listing loaded models: {e}")
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return []
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async def list_available_models(self) -> List[str]:
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"""
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List all available models (downloaded).
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Returns:
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List of available model names
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"""
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try:
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session = await self._get_session()
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async with session.get(f"{self._endpoint}/api/tags") as response:
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if response.status == 200:
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data = await response.json()
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models = data.get("models", [])
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return [m.get("name", "") for m in models]
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else:
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return []
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except Exception as e:
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logger.error(f"Error listing available models: {e}")
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return []
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