chore: address comments on the PR

Author: cdragos

Makefile

@@ -10,7 +10,7 @@ style:
 check_code_quality:
 	ruff format $(check_dirs) --check
 	ruff check $(check_dirs)
-	pyright $(check_dirs)
+	mypy $(check_dirs)
 
 publish:
 	python setup.py sdist bdist_wheel

pyproject.toml

@@ -92,11 +92,23 @@ convention = "google"
 # Preserve types, even if a file imports `from __future__ import annotations`.
 keep-runtime-typing = true
 
-[tool.pyright]
-pythonVersion = "3.8"
-exclude = ["build", ".venv", "venv*", "docs", "Tests"]
-venvPath = "."
-venv = ".venv"
-reportMissingImports = false
-reportUnboundVariable = false
-reportIncompatibleMethodOverride = false
+[tool.mypy]
+python_version = "3.8"
+exclude = ["^build/"]
+
+[[tool.mypy.overrides]]
+module = [
+    "_datetime.*",
+    "filetype.*",
+    # IPython is an optional dependency
+    "IPython.display.*",
+    # ipywidgets is an optional dependency
+    "ipywidgets.*",
+    # matplotlib typing is not available for Python 3.8
+    # remove this when we stop supporting Python 3.8
+    "matplotlib.*",
+    "requests_toolbelt.*",
+    "torch.*",
+    "ultralytics.*",
+]
+ignore_missing_imports = true

roboflow/__init__.py

@@ -3,6 +3,7 @@
 import sys
 import time
 from getpass import getpass
+from pathlib import Path
 from urllib.parse import urlparse
 
 import requests
@@ -59,20 +60,23 @@ def check_key(api_key, model, notebook, num_retries=0):
         return "onboarding"
 
 
+def auth(api_key):
+    r = check_key(api_key)
+    w = r["workspace"]
+
+    return Roboflow(api_key, w)
+
+
 def login(workspace=None, force=False):
     os_name = os.name
 
     if os_name == "nt":
-        default_path = os.path.join(os.getenv("USERPROFILE", ""), "roboflow/config.json")
+        default_path = str(Path.home() / "roboflow" / "config.json")
     else:
-        default_path = os.path.join(os.getenv("HOME", ""), ".config/roboflow/config.json")
+        default_path = str(Path.home() / ".config" / "roboflow" / "config.json")
 
     # default configuration location
-    conf_location = os.getenv(
-        "ROBOFLOW_CONFIG_DIR",
-        default=default_path,
-    )
-
+    conf_location = os.getenv("ROBOFLOW_CONFIG_DIR", default=default_path)
     if os.path.isfile(conf_location) and not force:
         write_line("You are already logged into Roboflow. To make a different login," "run roboflow.login(force=True).")
         return None
@@ -134,10 +138,7 @@ def initialize_roboflow(the_workspace=None):
 
     global active_workspace
 
-    conf_location = os.getenv(
-        "ROBOFLOW_CONFIG_DIR",
-        default=os.getenv("HOME", "") + "/.config/roboflow/config.json",
-    )
+    conf_location = os.getenv("ROBOFLOW_CONFIG_DIR", default=str(Path.home() / ".config" / "roboflow" / "config.json"))
 
     if not os.path.isfile(conf_location):
         raise RuntimeError("To use this method, you must first login - run roboflow.login()")
@@ -275,7 +276,7 @@ def project(self, project_name, the_workspace=None):
 
         dataset_info = dataset_info.json()["project"]
 
-        return Project(self.api_key or "", dataset_info)
+        return Project(self.api_key, dataset_info)
 
     def __str__(self):
         """to string function"""

roboflow/adapters/rfapi.py

@@ -1,7 +1,7 @@
 import json
 import os
+import urllib
 from typing import Optional
-from urllib import parse
 
 import requests
 from requests_toolbelt.multipart.encoder import MultipartEncoder
@@ -164,7 +164,7 @@ def _upload_url(api_key, project_url, **kwargs):
     url = f"{API_URL}/dataset/{project_url}/upload?api_key={api_key}"
 
     if kwargs:
-        querystring = parse.urlencode(kwargs, doseq=True)
+        querystring = urllib.parse.urlencode(kwargs, doseq=True)
         url += f"&{querystring}"
 
     return url

roboflow/models/classification.py

@@ -2,8 +2,8 @@
 import io
 import json
 import os
+import urllib
 from typing import Optional
-from urllib import parse
 
 import requests
 from PIL import Image
@@ -63,7 +63,7 @@ def __init__(
             print(f"initalizing local classification model hosted at : {local}")
             self.base_url = local
 
-    def predict(self, image_path, hosted=False):
+    def predict(self, image_path, hosted=False):  # type: ignore[override]
         """
         Run inference on an image.
 
@@ -106,7 +106,7 @@ def predict(self, image_path, hosted=False):
             )
         else:
             # Create API URL for hosted image (slightly different)
-            self.api_url += "&image=" + parse.quote_plus(image_path)
+            self.api_url += "&image=" + urllib.parse.quote_plus(image_path)
             # POST to the API
             resp = requests.post(self.api_url)
             img_dims = {"width": "0", "height": "0"}

roboflow/models/inference.py

@@ -2,8 +2,8 @@
 import json
 import os
 import time
+import urllib
 from typing import Optional, Tuple
-from urllib import parse
 from urllib.parse import urljoin
 
 import requests
@@ -72,7 +72,7 @@ def __get_image_params(self, image_path):
         """
         validate_image_path(image_path)
 
-        hosted_image = parse.urlparse(image_path).scheme in ("http", "https")
+        hosted_image = urllib.parse.urlparse(image_path).scheme in ("http", "https")
 
         if hosted_image:
             image_dims = {"width": "Undefined", "height": "Undefined"}
@@ -121,7 +121,7 @@ def predict(self, image_path, prediction_type=None, **kwargs):
         params["api_key"] = self.__api_key
 
         params.update(**kwargs)
-        url = f"{self.api_url}?{parse.urlencode(params)}"  # type: ignore[attr-defined]
+        url = f"{self.api_url}?{urllib.parse.urlencode(params)}"  # type: ignore[attr-defined]
         response = requests.post(url, **request_kwargs)
         response.raise_for_status()
 

roboflow/models/instance_segmentation.py

@@ -35,7 +35,7 @@ def __init__(
         self.colors = {} if colors is None else colors
         self.preprocessing = {} if preprocessing is None else preprocessing
 
-    def predict(self, image_path, confidence=40):
+    def predict(self, image_path, confidence=40):  # type: ignore[override]
         """
         Infers detections based on image from a specified model and image path.
 

roboflow/models/keypoint_detection.py

@@ -2,8 +2,8 @@
 import io
 import json
 import os
+import urllib
 from typing import Optional
-from urllib import parse
 
 import requests
 from PIL import Image
@@ -58,7 +58,7 @@ def __init__(
             print(f"initalizing local keypoint detection model hosted at : {local}")
             self.base_url = local
 
-    def predict(self, image_path, hosted=False):
+    def predict(self, image_path, hosted=False):  # type: ignore[override]
         """
         Run inference on an image.
 
@@ -101,7 +101,7 @@ def predict(self, image_path, hosted=False):
             )
         else:
             # Create API URL for hosted image (slightly different)
-            self.api_url += "&image=" + parse.quote_plus(image_path)
+            self.api_url += "&image=" + urllib.parse.quote_plus(image_path)
             # POST to the API
             resp = requests.post(self.api_url)
             img_dims = {"width": "0", "height": "0"}

roboflow/models/object_detection.py

@@ -4,7 +4,7 @@
 import json
 import os
 import random
-from urllib import parse
+import urllib
 
 import cv2
 import numpy as np
@@ -125,7 +125,7 @@ def load_model(
             format=format,
         )
 
-    def predict(
+    def predict(  # type: ignore[override]
         self,
         image_path,
         hosted=False,
@@ -234,7 +234,7 @@ def predict(
                 raise ValueError("image_path must be a string or a numpy array.")
         else:
             # Create API URL for hosted image (slightly different)
-            self.api_url += "&image=" + parse.quote_plus(image_path)
+            self.api_url += "&image=" + urllib.parse.quote_plus(image_path)
             image_dims = {"width": "0", "height": "0"}
             # POST to the API
             resp = requests.post(self.api_url)

roboflow/models/semantic_segmentation.py

@@ -18,7 +18,7 @@ def __init__(self, api_key: str, version_id: str):
         super().__init__(api_key, version_id)
         self.api_url = f"{SEMANTIC_SEGMENTATION_URL}/{self.dataset_id}/{self.version}"
 
-    def predict(self, image_path: str, confidence: int = 50):
+    def predict(self, image_path: str, confidence: int = 50):  # type: ignore[override]
         """
         Infers detections based on image from a specified model and image path.