defer transformers and pytesseract imports to when used. this shortens startup time of all other commands quite a bit

crawler/rec/floorplan.py

@@ -2,11 +2,10 @@ import re
 from PIL import Image
 import cv2
 import numpy as np
-from transformers import Pix2StructProcessor, Pix2StructForConditionalGeneration
-import pytesseract
 
 
 def inference(image_path):
+    from transformers import Pix2StructProcessor, Pix2StructForConditionalGeneration
     image = Image.open(image_path)
     question = "How many living rooms are displayed on this floor plan?"  # not sure if it even has an effect
     processor = Pix2StructProcessor.from_pretrained("google/deplot")
@@ -38,13 +37,13 @@ def calculate_model(image_path):
 def improve_img_for_ocr(img: Image):
     img2 = np.array(img.convert('L'))
     cv2.resize(img2, None, fx=1.2, fy=1.2, interpolation=cv2.INTER_CUBIC)
-    thresh = cv2.adaptiveThreshold(
-        img2, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2
-    )
+    thresh = cv2.adaptiveThreshold(img2, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
+                                   cv2.THRESH_BINARY, 11, 2)
     return Image.fromarray(thresh)
 
 
 def calculate_ocr(image_path):
+    import pytesseract
     img = Image.open(image_path)
     text = pytesseract.image_to_string(img)
     estimated_sqm = extract_total_sqm(text)
@@ -53,7 +52,9 @@ def calculate_ocr(image_path):
         text2 = pytesseract.image_to_string(improved_img)
         estimated_sqm2 = extract_total_sqm(text2)
         with open("recalculating.log", "a") as f:
-            f.write(f"before: {estimated_sqm} after: {estimated_sqm2} - {image_path}\n")
+            f.write(
+                f"before: {estimated_sqm} after: {estimated_sqm2} - {image_path}\n"
+            )
         return estimated_sqm2, text2
 
     return estimated_sqm, text