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from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
import os
import tempfile
import logging
import shutil
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def ensure_full_permissions(path):
"""Grant full permissions to a file or directory"""
try:
if os.path.isdir(path):
# Full permissions for directories (rwxrwxrwx)
os.chmod(path, 0o777)
# Apply to all contents recursively
for root, dirs, files in os.walk(path):
for d in dirs:
os.chmod(os.path.join(root, d), 0o777)
for f in files:
os.chmod(os.path.join(root, f), 0o666)
else:
# Full permissions for files (rw-rw-rw-)
os.chmod(path, 0o666)
return True
except Exception as e:
logger.error(f"Error setting permissions for {path}: {e}")
return False
def check_directory_permissions(path):
"""Check if directory exists and has correct permissions"""
try:
if not os.path.exists(path):
logger.warning(f"Directory does not exist: {path}")
return False
# Set full permissions
ensure_full_permissions(path)
return True
except Exception as e:
logger.error(f"Error checking permissions for {path}: {e}")
return False
def get_cache_dir():
"""Get a user-accessible cache directory"""
try:
# Try user's home directory first
home_dir = os.path.expanduser('~')
if not os.path.exists(home_dir):
raise Exception(f"Home directory does not exist: {home_dir}")
cache_dir = os.path.join(home_dir, '.cache', 'answer_grading_app')
logger.info(f"Attempting to use cache directory: {cache_dir}")
# Create directory with full permissions
os.makedirs(cache_dir, mode=0o777, exist_ok=True)
ensure_full_permissions(cache_dir)
logger.info(f"Successfully created and verified cache directory: {cache_dir}")
return cache_dir
except Exception as e:
logger.warning(f"Could not use home directory cache: {e}")
# Try temp directory
try:
temp_dir = os.path.join(tempfile.gettempdir(), 'answer_grading_app')
logger.info(f"Attempting to use temporary directory: {temp_dir}")
os.makedirs(temp_dir, mode=0o777, exist_ok=True)
ensure_full_permissions(temp_dir)
logger.info(f"Using temporary directory: {temp_dir}")
return temp_dir
except Exception as e:
logger.warning(f"Could not use temp directory: {e}")
# Last resort: use current directory
try:
current_dir = os.path.join(os.getcwd(), '.cache')
logger.info(f"Attempting to use current directory: {current_dir}")
os.makedirs(current_dir, mode=0o777, exist_ok=True)
ensure_full_permissions(current_dir)
logger.info(f"Using current directory: {current_dir}")
return current_dir
except Exception as e:
logger.error(f"Could not create any cache directory: {e}")
# If all else fails, use a new temporary directory
temp_dir = tempfile.mkdtemp()
ensure_full_permissions(temp_dir)
logger.info(f"Created temporary directory as last resort: {temp_dir}")
return temp_dir
class ModelSingleton:
_instance = None
_initialized = False
_models = {}
_reference_counts = {}
def __new__(cls):
if cls._instance is None:
cls._instance = super().__new__(cls)
return cls._instance
def __init__(self):
if not self._initialized:
try:
logger.info("Initializing ModelSingleton...")
# Set up cache directories
self.cache_dir = get_cache_dir()
logger.info(f"Using main cache directory: {self.cache_dir}")
# Define and create all cache directories
self.cache_dirs = {
'transformers': os.path.join(self.cache_dir, 'transformers'),
'huggingface': os.path.join(self.cache_dir, 'huggingface'),
'torch': os.path.join(self.cache_dir, 'torch'),
'cache': os.path.join(self.cache_dir, 'cache'),
'sentence_transformers': os.path.join(self.cache_dir, 'sentence_transformers'),
'fasttext': os.path.join(self.cache_dir, 'fasttext')
}
# Create and verify each cache directory with full permissions
for name, path in self.cache_dirs.items():
try:
# Create directory with full permissions
os.makedirs(path, mode=0o777, exist_ok=True)
ensure_full_permissions(path)
logger.info(f"Successfully created {name} cache directory: {path}")
# Create a test file to verify write permissions
test_file = os.path.join(path, '.write_test')
try:
with open(test_file, 'w') as f:
f.write('test')
os.chmod(test_file, 0o666) # Full read/write for test file
os.remove(test_file) # Clean up
logger.info(f"Verified write permissions for {name} cache directory")
except Exception as e:
logger.error(f"Failed to verify write permissions for {name} cache directory: {e}")
# Try to fix permissions
ensure_full_permissions(path)
except Exception as e:
logger.error(f"Error creating {name} cache directory: {e}")
# Try to create in temp directory as fallback
temp_path = os.path.join(tempfile.gettempdir(), 'answer_grading_app', name)
os.makedirs(temp_path, mode=0o777, exist_ok=True)
ensure_full_permissions(temp_path)
self.cache_dirs[name] = temp_path
logger.info(f"Using fallback directory for {name}: {temp_path}")
# Set environment variables with verified directories
os.environ['TRANSFORMERS_CACHE'] = self.cache_dirs['transformers']
os.environ['HF_HOME'] = self.cache_dirs['huggingface']
os.environ['TORCH_HOME'] = self.cache_dirs['torch']
os.environ['XDG_CACHE_HOME'] = self.cache_dirs['cache']
os.environ['SENTENCE_TRANSFORMERS_HOME'] = self.cache_dirs['sentence_transformers']
# Verify environment variables are set correctly
for env_var, path in [
('TRANSFORMERS_CACHE', 'transformers'),
('HF_HOME', 'huggingface'),
('TORCH_HOME', 'torch'),
('XDG_CACHE_HOME', 'cache'),
('SENTENCE_TRANSFORMERS_HOME', 'sentence_transformers')
]:
if os.environ.get(env_var) != self.cache_dirs[path]:
logger.warning(f"Environment variable {env_var} does not match expected path")
os.environ[env_var] = self.cache_dirs[path]
# Get device
self.device = "cuda" if torch.cuda.is_available() else "cpu"
logger.info(f"Using device: {self.device}")
# Initialize with None values
self.similarity_tokenizer = None
self.similarity_model = None
self.flan_tokenizer = None
self.flan_model = None
self.trocr_processor = None
self.trocr_model = None
self.vit_model = None
self.vit_processor = None
# Initialize reference counts
self._reference_counts = {
'similarity': 0,
'flan': 0,
'trocr': 0,
'vit': 0
}
self._initialized = True
logger.info("ModelSingleton initialization completed successfully")
except Exception as e:
logger.error(f"Error during ModelSingleton initialization: {e}")
raise
def get_similarity_model(self):
"""Get sentence transformer model with reference counting"""
try:
if self.similarity_model is None:
logger.info("Loading sentence transformer model...")
SENTENCE_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
self.similarity_tokenizer = AutoTokenizer.from_pretrained(
SENTENCE_MODEL,
cache_dir=os.getenv('TRANSFORMERS_CACHE')
)
self.similarity_model = SentenceTransformer(
SENTENCE_MODEL,
cache_folder=os.getenv('TRANSFORMERS_CACHE')
)
self.similarity_model.to(self.device)
logger.info("Sentence transformer model loaded successfully")
self._reference_counts['similarity'] += 1
return self.similarity_model
except Exception as e:
logger.error(f"Error loading sentence transformer model: {e}")
raise
def get_flan_model(self):
"""Get Flan-T5 model with reference counting"""
try:
if self.flan_model is None:
logger.info("Loading Flan-T5 model...")
FLAN_MODEL = "google/flan-t5-xl"
self.flan_tokenizer = AutoTokenizer.from_pretrained(
FLAN_MODEL,
cache_dir=os.getenv('TRANSFORMERS_CACHE')
)
self.flan_model = AutoModelForSeq2SeqLM.from_pretrained(
FLAN_MODEL,
cache_dir=os.getenv('TRANSFORMERS_CACHE'),
torch_dtype=torch.float16 if self.device == "cuda" else torch.float32,
low_cpu_mem_usage=True
)
self.flan_model.to(self.device)
logger.info("Flan-T5 model loaded successfully")
self._reference_counts['flan'] += 1
return self.flan_model
except Exception as e:
logger.error(f"Error loading Flan-T5 model: {e}")
raise
def get_trocr_model(self):
"""Get TrOCR model with reference counting"""
try:
if self.trocr_model is None:
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
logger.info("Loading TrOCR model...")
MODEL_NAME = "microsoft/trocr-large-handwritten"
self.trocr_processor = TrOCRProcessor.from_pretrained(MODEL_NAME)
self.trocr_model = VisionEncoderDecoderModel.from_pretrained(MODEL_NAME)
self.trocr_model.to(self.device)
logger.info("TrOCR model loaded successfully")
self._reference_counts['trocr'] += 1
return self.trocr_model, self.trocr_processor
except Exception as e:
logger.error(f"Error loading TrOCR model: {e}")
raise
def get_vit_model(self):
"""Get ViT model using only local files - no downloads"""
try:
if self.vit_model is None:
from transformers import ViTConfig, ViTImageProcessor, ViTForImageClassification
logger.info("Loading local ViT model from files...")
# Get absolute path to model directory
model_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
model_path = os.path.join(model_root, 'models', 'vit-base-beans')
logger.info(f"Using local model directory: {model_path}")
# Check model directory exists
if not os.path.exists(model_path):
raise FileNotFoundError(f"Local model directory not found at: {model_path}")
# Get paths to required files
model_file = os.path.join(model_path, 'model.safetensors')
config_file = os.path.join(model_path, 'config.json')
# Verify files exist
if not os.path.exists(model_file):
raise FileNotFoundError(f"Local model weights file not found at: {model_file}")
if not os.path.exists(config_file):
raise FileNotFoundError(f"Local model config file not found at: {config_file}")
logger.info("Found all required local model files:")
logger.info(f"- Using model weights: {model_file}")
logger.info(f"- Using config file: {config_file}")
# Load config directly from file
logger.info("Loading model configuration from local file...")
config = ViTConfig.from_json_file(config_file)
# Create processor from local config
logger.info("Creating image processor from local config...")
self.vit_processor = ViTImageProcessor(
do_resize=True,
size=config.image_size,
do_normalize=True
)
# Load model directly from local files
logger.info("Loading model weights from local file...")
self.vit_model = ViTForImageClassification.from_pretrained(
model_path,
config=config,
local_files_only=True,
use_safetensors=True,
trust_remote_code=False,
from_tf=False,
_fast_init=True
)
logger.info(f"Moving model to {self.device}...")
self.vit_model.to(self.device)
self.vit_model.eval()
logger.info("Local model loaded successfully!")
self._reference_counts['vit'] += 1
return self.vit_model, self.vit_processor
except Exception as e:
logger.error(f"Error loading local ViT model: {str(e)}")
raise
def release_similarity_model(self):
"""Release reference to similarity model"""
self._reference_counts['similarity'] -= 1
if self._reference_counts['similarity'] <= 0:
self._cleanup_similarity_model()
def release_flan_model(self):
"""Release reference to Flan-T5 model"""
self._reference_counts['flan'] -= 1
if self._reference_counts['flan'] <= 0:
self._cleanup_flan_model()
def release_trocr_model(self):
"""Release reference to TrOCR model"""
self._reference_counts['trocr'] -= 1
if self._reference_counts['trocr'] <= 0:
self._cleanup_trocr_model()
def release_vit_model(self):
"""Release reference to ViT model"""
self._reference_counts['vit'] -= 1
if self._reference_counts['vit'] <= 0:
self._cleanup_vit_model()
def _cleanup_similarity_model(self):
"""Clean up similarity model resources"""
if self.similarity_model is not None:
del self.similarity_model
self.similarity_model = None
self.similarity_tokenizer = None
torch.cuda.empty_cache()
logger.info("Similarity model resources cleaned up")
def _cleanup_flan_model(self):
"""Clean up Flan-T5 model resources"""
if self.flan_model is not None:
del self.flan_model
self.flan_model = None
self.flan_tokenizer = None
torch.cuda.empty_cache()
logger.info("Flan-T5 model resources cleaned up")
def _cleanup_trocr_model(self):
"""Clean up TrOCR model resources"""
if self.trocr_model is not None:
del self.trocr_model
del self.trocr_processor
self.trocr_model = None
self.trocr_processor = None
torch.cuda.empty_cache()
logger.info("TrOCR model resources cleaned up")
def _cleanup_vit_model(self):
"""Clean up ViT model resources"""
if self.vit_model is not None:
del self.vit_model
del self.vit_processor
self.vit_model = None
self.vit_processor = None
torch.cuda.empty_cache()
logger.info("ViT model resources cleaned up")
def cleanup(self):
"""Clean up all model resources"""
try:
logger.info("Starting model cleanup...")
# Clean up each model type
if self._reference_counts.get('similarity', 0) > 0:
self._cleanup_similarity_model()
if self._reference_counts.get('flan', 0) > 0:
self._cleanup_flan_model()
if self._reference_counts.get('trocr', 0) > 0:
self._cleanup_trocr_model()
if self._reference_counts.get('vit', 0) > 0:
self._cleanup_vit_model()
# Reset reference counts
for model_type in self._reference_counts:
self._reference_counts[model_type] = 0
# Force CUDA cache cleanup
if torch.cuda.is_available():
torch.cuda.empty_cache()
logger.info("Model cleanup completed successfully")
except Exception as e:
logger.error(f"Error during model cleanup: {e}")
# Continue cleanup even if there's an error
# Create global instance
models = ModelSingleton()
# Add cleanup function to the global instance
def cleanup_models():
models.cleanup() |