fix: CI mypy errors

unstructured_inference/inference/layout.py

@@ -140,7 +140,7 @@ def __init__(
     ):
         if detection_model is not None and element_extraction_model is not None:
             raise ValueError("Only one of detection_model and extraction_model should be passed.")
-        self.image = image
+        self.image: Optional[Image.Image] = image
         if image_metadata is None:
             image_metadata = {}
         self.image_metadata = image_metadata
@@ -167,6 +167,7 @@ def get_elements_using_image_extraction(
             raise ValueError(
                 "Cannot get elements using image extraction, no image extraction model defined",
             )
+        assert self.image is not None
         elements = self.element_extraction_model(self.image)
         if inplace:
             self.elements = elements
@@ -188,6 +189,7 @@ def get_elements_with_detection_model(
 
         # NOTE(mrobinson) - We'll want make this model inference step some kind of
         # remote call in the future.
+        assert self.image is not None
         inferred_layout: List[LayoutElement] = self.detection_model(self.image)
         inferred_layout = self.detection_model.deduplicate_detected_elements(
             inferred_layout,

unstructured_inference/models/chipper.py

@@ -463,7 +463,7 @@ def get_bounding_box(
                 np.asarray(
                     [
                         agg_heatmap,
-                        cv2.resize(
+                        cv2.resize(  # type: ignore
                             hmap,
                             (final_w, final_h),
                             interpolation=cv2.INTER_LINEAR_EXACT,  # cv2.INTER_CUBIC
@@ -620,7 +620,7 @@ def reduce_bbox_no_overlap(
         ):
             return input_bbox
 
-        nimage = np.array(image.crop(input_bbox))
+        nimage = np.array(image.crop(input_bbox))  # type: ignore
 
         nimage = self.remove_horizontal_lines(nimage)
 
@@ -669,7 +669,7 @@ def reduce_bbox_overlap(
         ):
             return input_bbox
 
-        nimage = np.array(image.crop(input_bbox))
+        nimage = np.array(image.crop(input_bbox))  # type: ignore
 
         nimage = self.remove_horizontal_lines(nimage)
 
@@ -773,7 +773,7 @@ def largest_margin(
         ):
             return None
 
-        nimage = np.array(image.crop(input_bbox))
+        nimage = np.array(image.crop(input_bbox))  # type: ignore
 
         if nimage.shape[0] * nimage.shape[1] == 0:
             return None

unstructured_inference/models/detectron2.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from pathlib import Path
 from typing import Any, Dict, Final, List, Optional, Union
 
@@ -7,7 +9,7 @@
     is_detectron2_available,
 )
 from layoutparser.models.model_config import LayoutModelConfig
-from PIL import Image
+from PIL import Image as PILImage
 
 from unstructured_inference.constants import ElementType
 from unstructured_inference.inference.layoutelement import LayoutElement
@@ -65,7 +67,7 @@
 class UnstructuredDetectronModel(UnstructuredObjectDetectionModel):
     """Unstructured model wrapper for Detectron2LayoutModel."""
 
-    def predict(self, x: Image):
+    def predict(self, x: PILImage.Image):
         """Makes a prediction using detectron2 model."""
         super().predict(x)
         prediction = self.model.detect(x)

unstructured_inference/models/donut.py

@@ -3,7 +3,7 @@
 from typing import Optional, Union
 
 import torch
-from PIL import Image
+from PIL import Image as PILImage
 from transformers import (
     DonutProcessor,
     VisionEncoderDecoderConfig,
@@ -16,7 +16,7 @@
 class UnstructuredDonutModel(UnstructuredModel):
     """Unstructured model wrapper for Donut image transformer."""
 
-    def predict(self, x: Image):
+    def predict(self, x: PILImage.Image):
         """Make prediction using donut model"""
         super().predict(x)
         return self.run_prediction(x)
@@ -50,7 +50,7 @@ def initialize(
             raise ImportError("Review the parameters to initialize a UnstructuredDonutModel obj")
         self.model.to(device)
 
-    def run_prediction(self, x: Image):
+    def run_prediction(self, x: PILImage.Image):
         """Internal prediction method."""
         pixel_values = self.processor(x, return_tensors="pt").pixel_values
         decoder_input_ids = self.processor.tokenizer(

unstructured_inference/models/tables.py

@@ -8,7 +8,7 @@
 import cv2
 import numpy as np
 import torch
-from PIL import Image
+from PIL import Image as PILImage
 from transformers import DetrImageProcessor, TableTransformerForObjectDetection
 
 from unstructured_inference.config import inference_config
@@ -27,7 +27,7 @@ class UnstructuredTableTransformerModel(UnstructuredModel):
     def __init__(self):
         pass
 
-    def predict(self, x: Image, ocr_tokens: Optional[List[Dict]] = None):
+    def predict(self, x: PILImage.Image, ocr_tokens: Optional[List[Dict]] = None):
         """Predict table structure deferring to run_prediction with ocr tokens
 
         Note:
@@ -70,7 +70,7 @@ def initialize(
 
     def get_structure(
         self,
-        x: Image,
+        x: PILImage.Image,
         pad_for_structure_detection: int = inference_config.TABLE_IMAGE_BACKGROUND_PAD,
     ) -> dict:
         """get the table structure as a dictionary contaning different types of elements as
@@ -87,7 +87,7 @@ def get_structure(
 
     def run_prediction(
         self,
-        x: Image,
+        x: PILImage.Image,
         pad_for_structure_detection: int = inference_config.TABLE_IMAGE_BACKGROUND_PAD,
         ocr_tokens: Optional[List[Dict]] = None,
         result_format: Optional[str] = "html",
@@ -155,7 +155,7 @@ def get_class_map(data_type: str):
 }
 
 
-def recognize(outputs: dict, img: Image, tokens: list):
+def recognize(outputs: dict, img: PILImage.Image, tokens: list):
     """Recognize table elements."""
     str_class_name2idx = get_class_map("structure")
     str_class_idx2name = {v: k for k, v in str_class_name2idx.items()}
@@ -655,7 +655,7 @@ def cells_to_html(cells):
     return str(ET.tostring(table, encoding="unicode", short_empty_elements=False))
 
 
-def zoom_image(image: Image, zoom: float) -> Image:
+def zoom_image(image: PILImage.Image, zoom: float) -> PILImage.Image:
     """scale an image based on the zoom factor using cv2; the scaled image is post processed by
     dilation then erosion to improve edge sharpness for OCR tasks"""
     if zoom <= 0:
@@ -673,4 +673,4 @@ def zoom_image(image: Image, zoom: float) -> Image:
     new_image = cv2.dilate(new_image, kernel, iterations=1)
     new_image = cv2.erode(new_image, kernel, iterations=1)
 
-    return Image.fromarray(new_image)
+    return PILImage.fromarray(new_image)

unstructured_inference/models/yolox.py

@@ -10,7 +10,7 @@
 import onnxruntime
 from huggingface_hub import hf_hub_download
 from onnxruntime.capi import _pybind_state as C
-from PIL import Image
+from PIL import Image as PILImage
 
 from unstructured_inference.constants import ElementType, Source
 from unstructured_inference.inference.layoutelement import LayoutElement
@@ -60,7 +60,7 @@
 
 
 class UnstructuredYoloXModel(UnstructuredObjectDetectionModel):
-    def predict(self, x: Image):
+    def predict(self, x: PILImage.Image):
         """Predict using YoloX model."""
         super().predict(x)
         return self.image_processing(x)
@@ -86,7 +86,7 @@ def initialize(self, model_path: str, label_map: dict):
 
     def image_processing(
         self,
-        image: Image = None,
+        image: PILImage.Image,
     ) -> List[LayoutElement]:
         """Method runing YoloX for layout detection, returns a PageLayout
         parameters