Use Optional[T] instead of T | None for old Python compatibility

Author: iurisilvio

roboflow/adapters/rfapi.py

@@ -1,6 +1,7 @@
 import json
 import os
 import urllib
+from typing import Optional
 
 import requests
 from requests_toolbelt.multipart.encoder import MultipartEncoder
@@ -43,8 +44,8 @@ def upload_image(
     split: str = "train",
     batch_name: str = DEFAULT_BATCH_NAME,
     tag_names: list = [],
-    sequence_number: int | None = None,
-    sequence_size: int | None = None,
+    sequence_number: Optional[int] = None,
+    sequence_size: Optional[int] = None,
     **kwargs,
 ):
     """

roboflow/core/project.py

@@ -4,6 +4,7 @@
 import sys
 import time
 import warnings
+from typing import Optional
 
 import requests
 from PIL import Image, UnidentifiedImageError
@@ -30,7 +31,7 @@ class Project:
     A Roboflow Project.
     """
 
-    def __init__(self, api_key: str, a_project: dict, model_format: str | None = None):
+    def __init__(self, api_key: str, a_project: dict, model_format: Optional[str] = None):
         """
         Create a Project object that represents a Project associated with a Workspace.
 
@@ -283,7 +284,7 @@ def train(
 
         return new_model
 
-    def version(self, version_number: int, local: str | None = None):
+    def version(self, version_number: int, local: Optional[str] = None):
         """
         Retrieves information about a specific version and returns a Version() object.
 
@@ -358,12 +359,12 @@ def check_valid_image(self, image_path: str):
     def upload(
         self,
         image_path: str,
-        annotation_path: str | None = None,
+        annotation_path: Optional[str] = None,
         hosted_image: bool = False,
-        image_id: str | None = None,
+        image_id: Optional[str] = None,
         split: str = "train",
         num_retry_uploads: int = 0,
-        batch_name: str | None = None,
+        batch_name: Optional[str] = None,
         tag_names: list = [],
         is_prediction: bool = False,
         **kwargs,
@@ -549,15 +550,15 @@ def _annotation_params(self, annotation_path):
 
     def search(
         self,
-        like_image: str | None = None,
-        prompt: str | None = None,
+        like_image: Optional[str] = None,
+        prompt: Optional[str] = None,
         offset: int = 0,
         limit: int = 100,
-        tag: str | None = None,
-        class_name: str | None = None,
-        in_dataset: str | None = None,
+        tag: Optional[str] = None,
+        class_name: Optional[str] = None,
+        in_dataset: Optional[str] = None,
         batch: bool = False,
-        batch_id: str | None = None,
+        batch_id: Optional[str] = None,
         fields: list = ["id", "created", "name", "labels"],
     ):
         """
@@ -627,15 +628,15 @@ def search(
 
     def search_all(
         self,
-        like_image: str | None = None,
-        prompt: str | None = None,
+        like_image: Optional[str] = None,
+        prompt: Optional[str] = None,
         offset: int = 0,
         limit: int = 100,
-        tag: str | None = None,
-        class_name: str | None = None,
-        in_dataset: str | None = None,
+        tag: Optional[str] = None,
+        class_name: Optional[str] = None,
+        in_dataset: Optional[str] = None,
         batch: bool = False,
-        batch_id: str | None = None,
+        batch_id: Optional[str] = None,
         fields: list = ["id", "created"],
     ):
         """

roboflow/models/classification.py

@@ -3,6 +3,7 @@
 import json
 import os
 import urllib
+from typing import Optional
 
 import requests
 from PIL import Image
@@ -23,11 +24,11 @@ def __init__(
         self,
         api_key: str,
         id: str,
-        name: str | None = None,
-        version: int | None = None,
+        name: Optional[str] = None,
+        version: Optional[int] = None,
         local: bool = False,
-        colors: dict | None = None,
-        preprocessing: dict | None = None,
+        colors: Optional[dict] = None,
+        preprocessing: Optional[dict] = None,
     ):
         """
         Create a ClassificationModel object through which you can run inference.

roboflow/models/inference.py

@@ -3,7 +3,7 @@
 import os
 import time
 import urllib
-from typing import List
+from typing import Optional
 from urllib.parse import urljoin
 
 import requests
@@ -140,7 +140,7 @@ def predict_video(
         fps: int = 5,
         additional_models: list = [],
         prediction_type: str = "batch-video",
-    ) -> tuple[str, str, str | None]:
+    ) -> tuple[str, str, Optional[str]]:
         """
         Infers detections based on image from specified model and image path.
 
@@ -280,7 +280,7 @@ def predict_video(
 
         return job_id, signed_url, signed_url_expires
 
-    def poll_for_video_results(self, job_id: str | None = None) -> dict:
+    def poll_for_video_results(self, job_id: Optional[str] = None) -> dict:
         """
         Polls the Roboflow API to check if video inference is complete.
 

roboflow/models/instance_segmentation.py

@@ -1,3 +1,5 @@
+from typing import Optional
+
 from roboflow.config import INSTANCE_SEGMENTATION_MODEL, INSTANCE_SEGMENTATION_URL
 from roboflow.models.inference import InferenceModel
 
@@ -12,8 +14,8 @@ def __init__(
         self,
         api_key: str,
         version_id: str,
-        colors: dict | None = None,
-        preprocessing: dict | None = None,
+        colors: Optional[dict] = None,
+        preprocessing: Optional[dict] = None,
         local: bool = None,
     ):
         """

roboflow/models/video.py

@@ -1,6 +1,6 @@
 import json
 import time
-from typing import List
+from typing import Optional
 from urllib.parse import urljoin
 
 import magic
@@ -61,7 +61,7 @@ def predict(
         video_path: str,
         inference_type: str,
         fps: int = 5,
-        additional_models: list | None = None,
+        additional_models: Optional[list] = None,
     ) -> tuple[str, str]:
         """
         Infers detections based on image from specified model and image path.
@@ -141,7 +141,7 @@ def predict(
 
         return job_id, signed_url
 
-    def poll_for_results(self, job_id: str | None = None) -> dict:
+    def poll_for_results(self, job_id: Optional[str] = None) -> dict:
         """
         Polls the Roboflow API to check if video inference is complete.