Run black

deploy.py

@@ -4,48 +4,51 @@
 
 from botocore.exceptions import ClientError
 
-AWS_ACCESS_KEY_ID = os.getenv('AWS_ACCESS_KEY_ID')
-AWS_SECRET_ACCESS_KEY = os.getenv('AWS_SECRET_ACCESS_KEY')
+AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID")
+AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY")
 
 logger = logging.getLogger(__name__)
 logging.basicConfig(level=logging.DEBUG)
 
 
 class Deployment:
     def __init__(self):
-        self.b = boto3.Session(aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=AWS_SECRET_ACCESS_KEY)
-        self.s3 = self.b.resource('s3')
+        self.b = boto3.Session(
+            aws_access_key_id=AWS_ACCESS_KEY_ID,
+            aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
+        )
+        self.s3 = self.b.resource("s3")
 
     def upload_file(self, import_file):
         name = os.path.basename(import_file)
-        logger.info(f'Copy {import_file} => s3://labelstud.io/sdk/{name}')
+        logger.info(f"Copy {import_file} => s3://labelstud.io/sdk/{name}")
         try:
             self.s3.meta.client.upload_file(
                 import_file,
-                'labelstud.io',
-                'sdk/' + name,
-                ExtraArgs={'ContentType': "text/html", 'ACL': "public-read"}
+                "labelstud.io",
+                "sdk/" + name,
+                ExtraArgs={"ContentType": "text/html", "ACL": "public-read"},
             )
         except ClientError as e:
             logging.error(e)
             return False
         return True
 
     def upload_dir(self, root_dir):
-        logger.info('Copy ' + root_dir)
+        logger.info("Copy " + root_dir)
         for path in os.listdir(root_dir):
             if os.path.isdir(path):
                 continue
 
-            if path.endswith('.html'):
+            if path.endswith(".html"):
                 self.upload_file(os.path.join(root_dir, path))
 
     def run(self):
-        logger.info('\n\n\n===>  Deployment is running')
-        self.upload_dir('html/label_studio_sdk')
-        logger.info('\n\n\n===> Deployment is finished')
+        logger.info("\n\n\n===>  Deployment is running")
+        self.upload_dir("html/label_studio_sdk")
+        logger.info("\n\n\n===> Deployment is finished")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     d = Deployment()
     d.run()

examples/active_learning/active_learning.py

@@ -7,17 +7,17 @@
 from sklearn.linear_model import LogisticRegression
 from sklearn.pipeline import make_pipeline
 
-LABEL_STUDIO_URL = 'http://localhost:8080'
-API_KEY = '91b3b61589784ed069b138eae3d5a5fe1e909f57'
+LABEL_STUDIO_URL = "http://localhost:8080"
+API_KEY = "91b3b61589784ed069b138eae3d5a5fe1e909f57"
 
 
 ls = Client(url=LABEL_STUDIO_URL, api_key=API_KEY)
 ls.check_connection()
 
 
 project = ls.start_project(
-    title='AL Project Created from SDK',
-    label_config='''
+    title="AL Project Created from SDK",
+    label_config="""
     <View>
     <Text name="text" value="$text"/>
     <Choices name="sentiment" toName="text" choice="single" showInLine="true">
@@ -26,14 +26,14 @@
         <Choice value="Neutral"/>
     </Choices>
     </View>
-    ''',
+    """,
 )
 
 
 project.set_sampling(ProjectSampling.UNCERTAINTY)
 
 
-labels_map = {'Positive': 0, 'Negative': 1, 'Neutral': 2}
+labels_map = {"Positive": 0, "Negative": 1, "Neutral": 2}
 inv_labels_map = {idx: label for label, idx in labels_map.items()}
 
 
@@ -60,8 +60,8 @@ def get_model_predictions(model, input_texts):
 labeled_tasks = project.get_labeled_tasks()
 texts, labels = [], []
 for labeled_task in labeled_tasks:
-    texts.append(labeled_task['data']['text'])
-    labels.append(labeled_task['annotations'][0]['result'][0]['value']['choices'][0])
+    texts.append(labeled_task["data"]["text"])
+    labels.append(labeled_task["annotations"][0]["result"][0]["value"]["choices"][0])
 
 
 model = get_model()
@@ -73,25 +73,25 @@ def get_model_predictions(model, input_texts):
 unlabeled_tasks = project.get_tasks(selected_ids=batch_ids)
 
 
-texts = [task['data']['text'] for task in unlabeled_tasks]
+texts = [task["data"]["text"] for task in unlabeled_tasks]
 pred_labels, pred_scores = get_model_predictions(model, texts)
 
 
-model_version = f'model_{len(labeled_tasks)}'
+model_version = f"model_{len(labeled_tasks)}"
 
 
 predictions = []
 for task, pred_label, pred_score in zip(unlabeled_tasks, pred_labels, pred_scores):
     project.create_prediction(
-        task_id=task['id'],
+        task_id=task["id"],
         # alternatively you can use a simple form here:
         # result=pred_label,
         result=[
             {
-                'from_name': 'sentiment',
-                'to_name': 'text',
-                'type': 'choices',
-                'value': {'choices': [pred_label]},
+                "from_name": "sentiment",
+                "to_name": "text",
+                "type": "choices",
+                "value": {"choices": [pred_label]},
             }
         ],
         score=pred_score,

examples/annotate_data_from_gcs/annotate_data_from_gcs.py

@@ -3,37 +3,37 @@
 from google.cloud import storage as google_storage
 from label_studio_sdk import Client
 
-BUCKET_NAME = 'my-bucket'  # specify your bucket name here
+BUCKET_NAME = "my-bucket"  # specify your bucket name here
 GOOGLE_APPLICATION_CREDENTIALS = (
-    'my-service-account-credentials.json'  # specify your GCS credentials
+    "my-service-account-credentials.json"  # specify your GCS credentials
 )
-os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = GOOGLE_APPLICATION_CREDENTIALS
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = GOOGLE_APPLICATION_CREDENTIALS
 
 google_client = google_storage.Client()
 bucket = google_client.get_bucket(BUCKET_NAME)
 tasks = []
 for filename in bucket.list_blobs():
-    tasks.append({'image': f'gs://{BUCKET_NAME}/{filename}'})
+    tasks.append({"image": f"gs://{BUCKET_NAME}/{filename}"})
 
 
-LABEL_STUDIO_URL = 'http://localhost:8080'
-API_KEY = '91b3b61589784ed069b138eae3d5a5fe1e909f57'
+LABEL_STUDIO_URL = "http://localhost:8080"
+API_KEY = "91b3b61589784ed069b138eae3d5a5fe1e909f57"
 
 ls = Client(url=LABEL_STUDIO_URL, api_key=API_KEY)
 ls.check_connection()
 
 
 project = ls.start_project(
-    title='Image Annotation Project from SDK',
-    label_config='''
+    title="Image Annotation Project from SDK",
+    label_config="""
     <View>
         <Image name="image" value="$image"/>
         <RectangleLabels name="objects" toName="image">
             <Choice value="Airplane"/>
             <Choice value="Car"/>
         </RectangleLabels>
     </View>
-    ''',
+    """,
 )
 
 

examples/export_snapshots.py

@@ -2,9 +2,9 @@
 import time
 from label_studio_sdk import Client
 
-LABEL_STUDIO_URL = os.getenv('LABEL_STUDIO_URL', default='http://localhost:8080')
-API_KEY = os.getenv('LABEL_STUDIO_API_KEY')
-PROJECT_ID = int(os.getenv('LABEL_STUDIO_PROJECT_ID'))
+LABEL_STUDIO_URL = os.getenv("LABEL_STUDIO_URL", default="http://localhost:8080")
+API_KEY = os.getenv("LABEL_STUDIO_API_KEY")
+PROJECT_ID = int(os.getenv("LABEL_STUDIO_PROJECT_ID"))
 
 # connect to Label Studio
 ls = Client(url=LABEL_STUDIO_URL, api_key=API_KEY)
@@ -28,21 +28,21 @@
 
 # get the first tab
 views = project.get_views()
-task_filter_options = {'view': views[0]['id']} if views else {}
+task_filter_options = {"view": views[0]["id"]} if views else {}
 
 # create new export snapshot
 export_result = project.export_snapshot_create(
-    title='Export SDK Snapshot', task_filter_options=task_filter_options
+    title="Export SDK Snapshot", task_filter_options=task_filter_options
 )
-assert 'id' in export_result
-export_id = export_result['id']
+assert "id" in export_result
+export_id = export_result["id"]
 
 # wait until snapshot is ready
 while project.export_snapshot_status(export_id).is_in_progress():
     time.sleep(1.0)
 
 # download snapshot file
-status, file_name = project.export_snapshot_download(export_id, export_type='JSON')
+status, file_name = project.export_snapshot_download(export_id, export_type="JSON")
 assert status == 200
 assert file_name is not None
 print(f"Status of the export is {status}.\nFile name is {file_name}")

examples/import_preannotations/import_brush_predictions.py

@@ -6,9 +6,9 @@
 import label_studio_converter.brush as brush
 from label_studio_sdk import Client
 
-LABEL_STUDIO_URL = 'http://localhost:8080'
-LABEL_STUDIO_API_KEY = '<your-token>'
-LABEL = 'Mitochondria'
+LABEL_STUDIO_URL = "http://localhost:8080"
+LABEL_STUDIO_API_KEY = "<your-token>"
+LABEL = "Mitochondria"
 
 ls = Client(url=LABEL_STUDIO_URL, api_key=LABEL_STUDIO_API_KEY)
 ls.check_connection()
@@ -26,7 +26,7 @@
 )
 
 ids = project.import_tasks(
-    [{'image': f'http://example.com/data_{i:04}.png'} for i in range(64)]
+    [{"image": f"http://example.com/data_{i:04}.png"} for i in range(64)]
 )
 
 mask = (np.random.random([512, 512]) * 255).astype(np.uint8)  # just a random 2D mask
@@ -43,7 +43,7 @@
             "from_name": "brush_labels_tag",
             "to_name": "image",
             "type": "brushlabels",
-            'value': {"format": "rle", "rle": rle, "brushlabels": [LABEL]},
+            "value": {"format": "rle", "rle": rle, "brushlabels": [LABEL]},
         }
     ],
 )