Squashed 'utils_v2/' changes from ef9630d..271426c

271426c (20241010) Can now cache class methods by accessing a caching object from the class itself!
606b02a Merge commit 'b0e0760fc7bb6336debf621860d1286575a0b8cc'
3d2a723 (20241009) Work in progress.
58266e3 (20241009) 'last decorator' bug-fixed
53e7a39 (20241009) Work in progress.
dbfc17f (20241009) 'last decorator' bug-fixed
11875e6 (20241008) logging decorator and model improved.

git-subtree-dir: utils_v2
git-subtree-split: 271426c19bea8de5edec7ecb79ee04368c47b02f

ai/image_classification/async_blur.py

@@ -11,7 +11,7 @@
     OBJECTIVE:
 
         To provide an easy way to assess images for blurriness.
-        Tried and implemented using HuggingFace models.
+        Tried and implemented using HuggingFace behaviour_models.
 
     REFERENCES:
 
@@ -193,7 +193,7 @@ if __name__ == "__main__":
 
     async def main():
         image = r"/home/developer/Downloads/low-res-check.png"
-        assessor = AssessImageBlur(model = r"/home/developer/PycharmProjects/utils/data/ai/models/hugging_face/image_classification/BlurOrBokeh")
+        assessor = AssessImageBlur(model = r"/home/developer/PycharmProjects/utils/data/ai/behaviour_models/hugging_face/image_classification/BlurOrBokeh")
         usable = await assessor.is_ok(image)
         classes = await assessor.classify(image)
         print("IS OKAY:", usable)

ai/image_classification/async_nsfw.py

@@ -11,7 +11,7 @@
     OBJECTIVE:
 
         To provide an easy way to assess images for adult content.
-        Tried and implemented using HuggingFace models.
+        Tried and implemented using HuggingFace behaviour_models.
 
     REFERENCES:
 

ai/image_segmentation/async_dichotomous_segmentation.py

@@ -13,12 +13,12 @@
         To provide an easy way to get masks from dichotomous image segmentation.
 
         This code uses a very specific model from HuggingFace: "ZhengPeng7/BiRefNet-portrait".
-        You may experiment with other models too, but make sure that model is made for "dichotomous" behaviour. This
+        You may experiment with other behaviour_models too, but make sure that model is made for "dichotomous" behaviour. This
         means that the model should have only two classes like "foreground", and "background". The specified model was
         trained for implementing portrait mode style blurring of backgrounds.
 
         The originally tested model has an MIT license as per their GitHub page. The code in this file may or may not
-        support drop-in replacement for other models, please be aware about this.
+        support drop-in replacement for other behaviour_models, please be aware about this.
 
     REFERENCES:
 

ai/object_detection/yolo.py

@@ -10,7 +10,7 @@
 
     OBJECTIVE:
 
-        To provide a class to detect objects in images using YOLO models.
+        To provide a class to detect objects in images using YOLO behaviour_models.
 
     REFERENCES:
 
@@ -166,7 +166,7 @@ class YoloDetect:
 
 if __name__ == "__main__":
 
-    # model_file_path = os.path.join(constants.PROJECT_DIRECTORY, "ai", "yolo", "models", "yolov8x.pt")
+    # model_file_path = os.path.join(constants.PROJECT_DIRECTORY, "ai", "yolo", "behaviour_models", "yolov8x.pt")
     model_file_path = r"/home/developer/PycharmProjects/utils/data/ai/models/hugging_face/object_detection/YOLOv10-Document-Layout-Analysis/yolov10x_best.pt"
     # sample_image_path = r"/home/developer/Downloads/2_cats.jpg"
     sample_image_path = r"/home/developer/Downloads/flattened_image.jpg"