Fix lint errors.

Author: favyen2

landsat/example_request.py

@@ -1,4 +1,4 @@
-""" Use this script to inference the API with locally stored data"""
+"""Use this script to inference the API with locally stored data."""
 
 import json
 import os
@@ -8,18 +8,20 @@
 PORT = os.getenv("Landsat_PORT", default=5555)
 LANDSAT_ENDPOINT = f"http://localhost:{PORT}/detections"
 TIMEOUT_SECONDS = 60000
-SCENE_ID = 'LC09_L1GT_106084_20241002_20241002_02_T2'
-CROP_PATH = 'temp_crops'
+SCENE_ID = "LC09_L1GT_106084_20241002_20241002_02_T2"
+CROP_PATH = "temp_crops"
 
-def sample_request() -> None:
-    """Sample request for files stored locally"""
 
+def sample_request() -> None:
+    """Sample request for files stored locally."""
     REQUEST_BODY = {
         "scene_id": SCENE_ID,
         "crop_path": CROP_PATH,
     }
 
-    response = requests.post(LANDSAT_ENDPOINT, json=REQUEST_BODY, timeout=TIMEOUT_SECONDS)
+    response = requests.post(
+        LANDSAT_ENDPOINT, json=REQUEST_BODY, timeout=TIMEOUT_SECONDS
+    )
     output_filename = os.path.join(
         os.path.dirname(os.path.realpath(__file__)), "sample_response.json"
     )

rslp/landsat_vessels/predict_pipeline.py

@@ -2,6 +2,7 @@
 
 import json
 from datetime import datetime, timedelta
+from tempfile import TemporaryDirectory
 from typing import Any
 
 import numpy as np
@@ -183,8 +184,8 @@ def run_classifier(
 
 def predict_pipeline(
     crop_path: str,
-    scratch_path: Optional[str] = None,
-    json_path: Optional[str] = None,
+    scratch_path: str | None = None,
+    json_path: str | None = None,
     image_files: dict[str, str] | None = None,
     scene_id: str | None = None,
 ) -> dict[str, Any]:
@@ -203,8 +204,6 @@ def predict_pipeline(
         scene_id: Landsat scene ID. Exactly one of image_files or scene_id should be
             specified.
     """
-
-
     if not scratch_path:
         tmp_dir = TemporaryDirectory()
         scratch_path = tmp_dir.name