abstract annotation import. Format

Author: Matt Sokoloff

README.md

@@ -56,19 +56,30 @@ pip3 install -r requirements.txt
 Labelbox uses API keys to validate requests. You can create and manage API keys on [Labelbox](https://app.labelbox.com/account/api-keys). Pass your API key as an environment variable. Then, import and initialize the API Client.
 
 ```
-user@machine:~$ export LABELBOX_TEST_API_KEY_LOCAL="<your local api key here>"
+user@machine:~$ export LABELBOX_API_KEY="<your local api key here>"
 user@machine:~$ python3
 
 from labelbox import Client
-client = Client(api_key="your_key_here", endpoint="http://localhost:8080/_gql")
+client = Client()
+```
+* Update api_key and endpoint if not using the production cloud deployment
+```
+# On prem
+client = Client( endpoint = "<local deployment>")
+
+# Local
+client = Client(api_key=os.environ['LABELBOX_TEST_API_KEY_LOCAL'], endpoint="http://localhost:8080/graphql")
+
+# Staging
+client = Client(api_key=os.environ['LABELBOX_TEST_API_KEY_LOCAL'], endpoint="https://staging-api.labelbox.com/graphql")
 ```
 
 ## Contribution
 Please consult `CONTRIB.md`
 
 ## Testing
 1. Update the `Makefile` with your `local`, `staging`, `prod` API key. Ensure that docker has been installed on your system. Make sure the key is not from a free tier account.
-2. To test on `local`: 
+2. To test on `local`:
 ```
 user@machine:~$ export LABELBOX_TEST_API_KEY_LOCAL="<your local api key here>"
 make test-local  # with an optional flag: PATH_TO_TEST=tests/integration/...etc LABELBOX_TEST_API_KEY_LOCAL=specify_here_or_export_me

labelbox/client.py

@@ -192,7 +192,7 @@ def check_errors(keywords, *path):
                 if obj in keywords:
                     return error
             return None
-        
+
         def get_error_status_code(error):
             return error["extensions"]["exception"]["status"]
 

labelbox/schema/annotation_import.py

@@ -1,25 +1,26 @@
-from typing import Any, Dict, List, Union
 import functools
-import os
 import json
-import time
 import logging
+import os
+import time
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Union
 
 import backoff
 import ndjson
 import requests
 
 import labelbox
-from labelbox.schema.enums import AnnotationImportState
+from labelbox.orm import query
 from labelbox.orm.db_object import DbObject
 from labelbox.orm.model import Field, Relationship
-from labelbox.orm import query
+from labelbox.schema.enums import AnnotationImportState
 
 NDJSON_MIME_TYPE = "application/x-ndjson"
 logger = logging.getLogger(__name__)
 
 
-class AnnotationImport(DbObject):
+class AnnotationImport(DbObject, ABC):
     name = Field.String("name")
     state = Field.Enum(AnnotationImportState, "state")
     input_file_url = Field.String("input_file_url")
@@ -122,6 +123,10 @@ def _fetch_remote_ndjson(self, url: str) -> List[Dict[str, Any]]:
     def _make_file_name(parent_id: str, name: str) -> str:
         return f"{parent_id}__{name}.ndjson"
 
+    @abstractmethod
+    def from_name(cls, client, parent, name):
+        ...
+
     def refresh(self) -> None:
         """Synchronizes values of all fields with the database.
         """
@@ -240,11 +245,10 @@ def create_from_url(cls, client: "labelbox.Client", model_run_id: str,
                                     parent_id=model_run_id,
                                     name=name,
                                     url=url)
-                                    
+
     @classmethod
-    def from_name(
-            cls, client: "labelbox.Client", model_run_id: str,
-            name: str) -> "MEAPredictionImport":
+    def from_name(cls, client: "labelbox.Client", model_run_id: str,
+                  name: str) -> "MEAPredictionImport":
 
         query_str = """query getModelErrorAnalysisPredictionImportPyApi($modelRunId : ID!, $name: String!) {
             modelErrorAnalysisPredictionImport(
@@ -256,8 +260,9 @@ def from_name(
             "name": name,
         }
         response = client.execute(query_str, params)
-        if response is None: 
-           raise labelbox.exceptions.ResourceNotFoundError(MEAPredictionImport, params)
+        if response is None:
+            raise labelbox.exceptions.ResourceNotFoundError(
+                MEAPredictionImport, params)
 
         return cls(client, response["modelErrorAnalysisPredictionImport"])
 
@@ -293,9 +298,8 @@ def create_from_url(cls, client: "labelbox.Client", project_id: str,
                                     url=url)
 
     @classmethod
-    def from_name(
-            cls, client: "labelbox.Client", project_id: str,
-            name: str) -> "MALPredictionImport":
+    def from_name(cls, client: "labelbox.Client", project_id: str,
+                  name: str) -> "MALPredictionImport":
 
         query_str = """query getModelAssistedLabelingPredictionImportPyApi($projectId : ID!, $name: String!) {
             modelAssistedLabelingPredictionImport(
@@ -307,7 +311,8 @@ def from_name(
             "name": name,
         }
         response = client.execute(query_str, params)
-        if response is None: 
-           raise labelbox.exceptions.ResourceNotFoundError(MALPredictionImport, params)
+        if response is None:
+            raise labelbox.exceptions.ResourceNotFoundError(
+                MALPredictionImport, params)
 
         return cls(client, response["modelAssistedLabelingPredictionImport"])