89 lines
1.9 KiB
Python
89 lines
1.9 KiB
Python
from transformers import AutoTokenizer
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from transformers import AutoModelForSequenceClassification
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from app.config.settings import settings
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from app.config.logging import logger
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from transformers import AutoImageProcessor
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from transformers import AutoModelForImageClassification
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import torch
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class ModelManager:
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def __init__(self):
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self.device = None
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self.text_model = None
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self.text_tokenizer = None
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self.image_model = None
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self.image_processor = None
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def load_device(self):
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if settings.DEVICE == "auto":
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self.device = torch.device(
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"cuda" if torch.cuda.is_available() else "cpu"
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)
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else:
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self.device = torch.device(settings.DEVICE)
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def load_tokenizer(self):
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print("Loading tokenizer...")
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self.text_tokenizer = AutoTokenizer.from_pretrained(
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settings.TEXT_MODEL,
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cache_dir=settings.MODEL_CACHE_DIR
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)
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print("Tokenizer loaded.")
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def load_model(self):
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print("Loading model...")
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self.text_model = AutoModelForSequenceClassification.from_pretrained(
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settings.TEXT_MODEL,
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cache_dir=settings.MODEL_CACHE_DIR
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)
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self.text_model.to(self.device)
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self.text_model.eval()
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print(self.text_model.config.id2label)
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print("Text model loaded.")
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def initialize(self):
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self.load_device()
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print(f"Using device: {self.device}")
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self.load_tokenizer()
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self.load_model()
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self.load_image_model()
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def load_image_model(self):
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print("Loading image model...")
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self.image_processor = (
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AutoImageProcessor.from_pretrained(
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settings.IMAGE_MODEL,
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cache_dir=settings.MODEL_CACHE_DIR
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)
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)
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self.image_model = (
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AutoModelForImageClassification.from_pretrained(
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settings.IMAGE_MODEL,
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cache_dir=settings.MODEL_CACHE_DIR
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)
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)
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self.image_model.to(self.device)
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self.image_model.eval()
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print("Image model loaded.")
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model_manager = ModelManager() |