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import logging
import torch
from typing import Optional
from functools import lru_cache
logging.basicConfig(level=logging.INFO)
_models = {
"expert_model": None,
"expert_tokenizer": None,
"multimodal_model": None,
"multimodal_processor": None,
"translation_model": None,
"translation_tokenizer": None,
"embedder": None,
"lang_identifier": None,
"classifier": None,
}
_device = "cpu"
def get_device():
return _device
def load_expert_model(model_name: str, use_quantization: bool = True):
if _models["expert_model"] is not None:
return _models["expert_tokenizer"], _models["expert_model"]
from transformers import AutoTokenizer, AutoModelForCausalLM
from app.utils import config
logging.info(f"Loading expert model ({model_name})...")
cache_dir = getattr(config, 'hf_cache', '/models/huggingface')
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
cache_dir=cache_dir
)
model_kwargs = {
"torch_dtype": torch.float32,
"device_map": "cpu",
"low_cpu_mem_usage": True,
}
logging.info("Loading model in float32 for CPU compatibility")
cache_dir = getattr(config, 'hf_cache', '/models/huggingface')
model = AutoModelForCausalLM.from_pretrained(
model_name,
cache_dir=cache_dir,
**model_kwargs
)
model.eval()
_models["expert_model"] = model
_models["expert_tokenizer"] = tokenizer
logging.info("Expert model loaded successfully")
return tokenizer, model
def load_multimodal_model(model_name: str):
"""
Lazy load multimodal Qwen2-VL model (vision-language).
Used for photo/video-aware advisory.
"""
if _models["multimodal_model"] is not None:
return _models["multimodal_processor"], _models["multimodal_model"]
# With latest transformers + qwen-vl-utils, Qwen2VLForConditionalGeneration
# and AutoProcessor support full image/video chat as in official docs.
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from app.utils import config
logging.info(f"Loading multimodal expert model ({model_name})...")
cache_dir = getattr(config, "hf_cache", "/models/huggingface")
try:
processor = AutoProcessor.from_pretrained(
model_name,
cache_dir=cache_dir,
)
model = Qwen2VLForConditionalGeneration.from_pretrained(
model_name,
torch_dtype=torch.float32, # CPU deployment
cache_dir=cache_dir,
device_map="cpu",
low_cpu_mem_usage=True,
)
model.eval()
_models["multimodal_model"] = model
_models["multimodal_processor"] = processor
logging.info("Multimodal expert model loaded successfully")
return processor, model
except Exception as e:
logging.error(
f"Failed to load multimodal model {model_name}: {e}. "
"Falling back to text-only expert model."
)
_models["multimodal_model"] = None
_models["multimodal_processor"] = None
return None, None
def load_translation_model(model_name: str):
"""Lazy load translation model."""
if _models["translation_model"] is not None:
return _models["translation_tokenizer"], _models["translation_model"]
from transformers import AutoModelForSeq2SeqLM, NllbTokenizer
from app.utils import config
logging.info(f"Loading translation model ({model_name})...")
cache_dir = getattr(config, 'hf_cache', '/models/huggingface')
tokenizer = NllbTokenizer.from_pretrained(
model_name,
cache_dir=cache_dir
)
model = AutoModelForSeq2SeqLM.from_pretrained(
model_name,
torch_dtype=torch.float32, # CPU uses float32
cache_dir=cache_dir,
device_map="cpu",
low_cpu_mem_usage=True
)
model.eval()
_models["translation_model"] = model
_models["translation_tokenizer"] = tokenizer
logging.info("Translation model loaded successfully")
return tokenizer, model
def load_embedder(model_name: str):
"""Lazy load sentence transformer embedder."""
if _models["embedder"] is not None:
return _models["embedder"]
from sentence_transformers import SentenceTransformer
from app.utils import config
logging.info(f"Loading embedder ({model_name})...")
cache_folder = getattr(config, 'hf_cache', '/models/huggingface')
embedder = SentenceTransformer(
model_name,
device=_device,
cache_folder=cache_folder
)
_models["embedder"] = embedder
logging.info("Embedder loaded successfully")
return embedder
def load_lang_identifier(repo_id: str, filename: str = "model.bin"):
"""Lazy load FastText language identifier."""
if _models["lang_identifier"] is not None:
return _models["lang_identifier"]
import fasttext
from huggingface_hub import hf_hub_download
from app.utils import config
logging.info(f"Loading language identifier ({repo_id})...")
cache_dir = getattr(config, 'hf_cache', '/models/huggingface')
lang_model_path = hf_hub_download(
repo_id=repo_id,
filename=filename,
cache_dir=cache_dir
)
lang_identifier = fasttext.load_model(lang_model_path)
_models["lang_identifier"] = lang_identifier
logging.info("Language identifier loaded successfully")
return lang_identifier
def load_classifier(classifier_path: str):
"""Lazy load intent classifier."""
if _models["classifier"] is not None:
return _models["classifier"]
import joblib
from pathlib import Path
logging.info(f"Loading classifier ({classifier_path})...")
if not Path(classifier_path).exists():
logging.warning(f"Classifier not found at {classifier_path}")
return None
try:
classifier = joblib.load(classifier_path)
_models["classifier"] = classifier
logging.info("Classifier loaded successfully")
return classifier
except Exception as e:
logging.error(f"Failed to load classifier: {e}")
return None
def clear_model_cache():
"""Clear all loaded models from memory."""
global _models
for key in _models:
if _models[key] is not None:
del _models[key]
_models[key] = None
import gc
gc.collect()
logging.info("Model cache cleared")
def get_model_memory_usage():
"""Get approximate memory usage of loaded models."""
usage = {}
if _models["expert_model"] is not None:
# Rough estimate: 4B params * 4 bytes = 16 GB
usage["expert_model"] = "~16 GB"
if _models["translation_model"] is not None:
usage["translation_model"] = "~2-5 GB"
if _models["embedder"] is not None:
usage["embedder"] = "~1 GB"
if _models["lang_identifier"] is not None:
usage["lang_identifier"] = "~200 MB"
return usage
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