Swap to new texify model

marker/models.py

@@ -1,41 +1,12 @@
 import os
-
-from marker.settings import settings
-
-os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" # Transformers uses .isin for a simple op, which is not supported on MPS
-
-from typing import List
-from PIL import Image
+os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" # Transformers uses .isin for an op, which is not supported on MPS
 
 from surya.detection import DetectionPredictor
 from surya.layout import LayoutPredictor
 from surya.ocr_error import OCRErrorPredictor
 from surya.recognition import RecognitionPredictor
 from surya.table_rec import TableRecPredictor
-
-from texify.model.model import load_model as load_texify_model
-from texify.model.processor import load_processor as load_texify_processor
-from texify.inference import batch_inference
-
-class TexifyPredictor:
-    def __init__(self, device=None, dtype=None):
-        if not device:
-            device = settings.TORCH_DEVICE_MODEL
-        if not dtype:
-            dtype = settings.TEXIFY_DTYPE
-
-        self.model = load_texify_model(checkpoint=settings.TEXIFY_MODEL_NAME, device=device, dtype=dtype)
-        self.processor = load_texify_processor()
-        self.device = device
-        self.dtype = dtype
-
-    def __call__(self, batch_images: List[Image.Image], max_tokens: int):
-        return batch_inference(
-            batch_images,
-            self.model,
-            self.processor,
-            max_tokens=max_tokens
-        )
+from surya.texify import TexifyPredictor
 
 
 def create_model_dict(device=None, dtype=None) -> dict: