chore: pre commit (#29)

* fix: ruff formatting

* fix: install pre-commit hook properly

* chore: add gh action for linting / testing

.github/workflows/pull-request.yml

@@ -0,0 +1,39 @@
+name: Pull Request workflow
+
+on:
+  pull_request:
+  workflow_dispatch:
+
+env:
+  PYTHON: 3.11.6
+
+concurrency:
+  group: active-private-${{ github.ref }}-pr
+  cancel-in-progress: true
+
+jobs:
+  # This will cache the virtual env for the following jobs
+  setup-venv:
+    runs-on: ubuntu-latest
+    name: Set up a cached VENV
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Setup root poetry environment
+        uses: ./.github/actions/setup-root-poetry-environment
+
+  pre-commit:
+    needs: [setup-venv]
+    name: Linting and type checking
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Setup root poetry environment
+        uses: ./.github/actions/setup-root-poetry-environment
+
+      - name: Run linting, type checking
+        uses: pre-commit/[email protected]
+        with:
+          extra_args: "--all-files --hook-stage=push"
+        timeout-minutes: 60

.pre-commit-config.yaml

@@ -28,4 +28,6 @@ repos:
         types_or: [python, pyi]
         language: system
         pass_filenames: false
+
 default_stages: [pre-push]
+default_install_hook_types: [pre-push]

docs/code_examples/fastapi/frame_classification.py

@@ -3,6 +3,11 @@
 import numpy as np
 from anthropic import Anthropic
 from encord.objects.ontology_labels_impl import LabelRowV2
+from fastapi import Depends, FastAPI, Form
+from fastapi.middleware.cors import CORSMiddleware
+from numpy.typing import NDArray
+from typing_extensions import Annotated
+
 from encord_agents.core.data_model import Frame
 from encord_agents.core.ontology import OntologyDataModel
 from encord_agents.core.utils import get_user_client
@@ -11,11 +16,6 @@
     dep_label_row,
     dep_single_frame,
 )
-from numpy.typing import NDArray
-from typing_extensions import Annotated
-
-from fastapi import Depends, FastAPI, Form
-from fastapi.middleware.cors import CORSMiddleware
 
 # Initialize FastAPI app
 app = FastAPI()
@@ -67,9 +67,7 @@ async def classify_frame(
     try:
         classifications = data_model(message.content[0].text)
         for clf in classifications:
-            clf.set_for_frames(
-                frame_data.frame, confidence=0.5, manual_annotation=False
-            )
+            clf.set_for_frames(frame_data.frame, confidence=0.5, manual_annotation=False)
             lr.add_classification_instance(clf)
     except Exception:
         import traceback

docs/code_examples/fastapi/object_classification.py

@@ -2,6 +2,10 @@
 
 from anthropic import Anthropic
 from encord.objects.ontology_labels_impl import LabelRowV2
+from fastapi import Depends, FastAPI, Form
+from fastapi.middleware.cors import CORSMiddleware
+from typing_extensions import Annotated
+
 from encord_agents.core.data_model import InstanceCrop
 from encord_agents.core.ontology import OntologyDataModel
 from encord_agents.core.utils import get_user_client
@@ -10,10 +14,6 @@
     dep_label_row,
     dep_object_crops,
 )
-from typing_extensions import Annotated
-
-from fastapi import Depends, FastAPI, Form
-from fastapi.middleware.cors import CORSMiddleware
 
 # Initialize FastAPI app
 app = FastAPI()
@@ -76,9 +76,7 @@ async def classify_objects(
         try:
             instance = data_model(message.content[0].text)
 
-            coordinates = crop.instance.get_annotation(
-                frame=frame_data.frame
-            ).coordinates
+            coordinates = crop.instance.get_annotation(frame=frame_data.frame).coordinates
             instance.set_for_frames(
                 coordinates=coordinates,
                 frames=frame_data.frame,

docs/code_examples/gcp/add_bounding_box.py

@@ -17,6 +17,7 @@
 
 from encord.objects.coordinates import BoundingBoxCoordinates
 from encord.objects.ontology_labels_impl import LabelRowV2
+
 from encord_agents.core.data_model import FrameData
 from encord_agents.gcp import editor_agent
 
@@ -26,9 +27,7 @@ def add_bounding_box(frame_data: FrameData, label_row: LabelRowV2) -> None:
     ins = label_row.ontology_structure.objects[0].create_instance()
     ins.set_for_frames(
         frames=frame_data.frame,
-        coordinates=BoundingBoxCoordinates(
-            top_left_x=0.2, top_left_y=0.2, width=0.6, height=0.6
-        ),
+        coordinates=BoundingBoxCoordinates(top_left_x=0.2, top_left_y=0.2, width=0.6, height=0.6),
     )
     label_row.add_object_instance(ins)
     label_row.save()

docs/code_examples/gcp/frame_classification.py

@@ -2,13 +2,14 @@
 
 from anthropic import Anthropic
 from encord.objects.ontology_labels_impl import LabelRowV2
+from numpy.typing import NDArray
+from typing_extensions import Annotated
+
 from encord_agents.core.ontology import OntologyDataModel
 from encord_agents.core.utils import get_user_client
 from encord_agents.core.video import Frame
 from encord_agents.gcp import Depends, editor_agent
 from encord_agents.gcp.dependencies import FrameData, dep_single_frame
-from numpy.typing import NDArray
-from typing_extensions import Annotated
 
 client = get_user_client()
 project = client.get_project("<your_project_hash>")
@@ -53,9 +54,7 @@ def agent(
     try:
         classifications = data_model(message.content[0].text)
         for clf in classifications:
-            clf.set_for_frames(
-                frame_data.frame, confidence=0.5, manual_annotation=False
-            )
+            clf.set_for_frames(frame_data.frame, confidence=0.5, manual_annotation=False)
             lr.add_classification_instance(clf)
     except Exception:
         import traceback

docs/code_examples/gcp/object_classification.py

@@ -2,11 +2,12 @@
 
 from anthropic import Anthropic
 from encord.objects.ontology_labels_impl import LabelRowV2
+from typing_extensions import Annotated
+
 from encord_agents.core.ontology import OntologyDataModel
 from encord_agents.core.utils import get_user_client
 from encord_agents.gcp import Depends, editor_agent
 from encord_agents.gcp.dependencies import FrameData, InstanceCrop, dep_object_crops
-from typing_extensions import Annotated
 
 # User client
 client = get_user_client()
@@ -62,9 +63,7 @@ def agent(
         try:
             instance = data_model(message.content[0].text)
 
-            coordinates = crop.instance.get_annotation(
-                frame=frame_data.frame
-            ).coordinates
+            coordinates = crop.instance.get_annotation(frame=frame_data.frame).coordinates
             instance.set_for_frames(
                 coordinates=coordinates,
                 frames=frame_data.frame,

docs/code_examples/tasks/prelabel_videos.py

@@ -15,11 +15,12 @@
 from encord.objects.coordinates import BoundingBoxCoordinates
 from encord.objects.ontology_labels_impl import LabelRowV2
 from encord.project import Project
+from numpy.typing import NDArray
+from typing_extensions import Annotated
+
 from encord_agents.core.data_model import Frame
 from encord_agents.tasks import Depends, Runner
 from encord_agents.tasks.dependencies import dep_video_iterator
-from numpy.typing import NDArray
-from typing_extensions import Annotated
 
 runner = Runner(project_hash="<project_hash>")
 
@@ -64,9 +65,7 @@ def run_something(
         outputs = model(frame.content)
         for output in outputs:
             ins = ontology.objects[output.label].create_instance()
-            ins.set_for_frames(
-                frames=frame.frame, coordinates=output.coords, confidence=output.conf
-            )
+            ins.set_for_frames(frames=frame.frame, coordinates=output.coords, confidence=output.conf)
 
             lr.add_object_instance(ins)
 

docs/code_examples/tasks/prioritize_by_data_title.py

@@ -1,4 +1,5 @@
 from encord.objects.ontology_labels_impl import LabelRowV2
+
 from encord_agents.tasks import Runner
 
 runner = Runner(project_hash="<your_project_hash>")

docs/code_examples/tasks/prioritize_by_data_title_specific.py

@@ -1,4 +1,5 @@
 from encord.objects.ontology_labels_impl import LabelRowV2
+
 from encord_agents.tasks import Runner
 
 runner = Runner(project_hash="<your_project_hash>")

docs/code_examples/tasks/twin_project.py

@@ -1,9 +1,10 @@
 from encord.objects.ontology_labels_impl import LabelRowV2
 from encord.objects.options import Option
 from encord.workflow.stages.agent import AgentTask
+from typing_extensions import Annotated
+
 from encord_agents.tasks import Depends, Runner
 from encord_agents.tasks.dependencies import Twin, dep_twin_label_row
-from typing_extensions import Annotated
 
 # 1. Setup the runner
 runner = Runner(project_hash="<project_hash_a>")
@@ -19,9 +20,7 @@
 @runner.stage(stage="<transfer_agent_stage_uuid>")
 def copy_labels(
     manually_annotated_lr: LabelRowV2,
-    twin: Annotated[
-        Twin, Depends(dep_twin_label_row(twin_project_hash="<project_hash_b>"))
-    ],
+    twin: Annotated[Twin, Depends(dep_twin_label_row(twin_project_hash="<project_hash_b>"))],
 ) -> str | None:
     # 4. Reading the checkboxes that have been set
     instance = manually_annotated_lr.get_classification_instances()[0]

docs/code_examples/tasks/wrong_stage_and_pathway_names.py

@@ -1,4 +1,5 @@
 from encord.objects.ontology_labels_impl import LabelRowV2
+
 from encord_agents.tasks import Runner
 
 r = Runner()

encord_agents/core/settings.py

@@ -84,4 +84,4 @@ def ssh_key(self) -> str:
             if self.ssh_key_file is None:
                 raise ValueError("Both ssh key content and ssh key file is None")
             self.ssh_key_content = self.ssh_key_file.read_text()
-        return self.ssh_key_content 
+        return self.ssh_key_content

encord_agents/core/types.py

@@ -1,5 +1,3 @@
 from typing import Literal
 
 Base64Formats = Literal[".jpeg", ".jpg", ".png"]
-
-

encord_agents/core/vision.py

@@ -37,7 +37,7 @@ def rbb_to_poly(
             [(x + w) * img_width, (y + h) * img_height],
             [x * img_width, (y + h) * img_height],
         ],
-        dtype=np.float32
+        dtype=np.float32,
     )
     angle = rbb.theta  # [0; 360]
     center = tuple(bbox_not_rotated.mean(0).tolist())
@@ -110,6 +110,6 @@ def crop_to_object(image: NDArray[np.uint8], coordinates: CroppableCoordinates)
     return crop_to_bbox(image, box)
 
 
-def b64_encode_image(img: NDArray[np.uint8], format: Base64Formats =".jpg") -> str:
+def b64_encode_image(img: NDArray[np.uint8], format: Base64Formats = ".jpg") -> str:
     _, encoded_image = cv2.imencode(format, img)
     return base64.b64encode(encoded_image).decode("utf-8")  # type: ignore

encord_agents/fastapi/dependencies.py

@@ -147,7 +147,9 @@ def my_route(
     return get_initialised_label_row(frame_data)
 
 
-def dep_single_frame(lr: Annotated[LabelRowV2, Depends(dep_label_row)], frame_data: Annotated[FrameData, Form()]) -> NDArray[np.uint8]:
+def dep_single_frame(
+    lr: Annotated[LabelRowV2, Depends(dep_label_row)], frame_data: Annotated[FrameData, Form()]
+) -> NDArray[np.uint8]:
     """
     Dependency to inject the underlying asset of the frame data.
 
@@ -214,7 +216,9 @@ def my_route(
         yield iter_video(asset)
 
 
-def dep_project(frame_data: Annotated[FrameData, Form()], client: Annotated[EncordUserClient, Depends(dep_client)]) -> Project:
+def dep_project(
+    frame_data: Annotated[FrameData, Form()], client: Annotated[EncordUserClient, Depends(dep_client)]
+) -> Project:
     r"""
     Dependency to provide an instantiated
     [Project](https://docs.encord.com/sdk-documentation/sdk-references/LabelRowV2){ target="\_blank", rel="noopener noreferrer" }.

encord_agents/tasks/runner.py

@@ -278,7 +278,7 @@ def _execute_tasks(
                                     task.proceed(pathway_name=next_stage, bundle=bundle)
 
                             if pbar_update is not None:
-                                pbar_update(1.)
+                                pbar_update(1.0)
                             break
 
                         except KeyboardInterrupt:
@@ -474,7 +474,9 @@ def {fn_name}(...):
                                 for lr in batch_lrs:
                                     if lr:
                                         lr.initialise_labels(bundle=lr_bundle)
-                        self._execute_tasks(project, zip(batch, batch_lrs), runner_agent, num_retries, pbar_update=pbar.update)
+                        self._execute_tasks(
+                            project, zip(batch, batch_lrs), runner_agent, num_retries, pbar_update=pbar.update
+                        )
         except (PrintableError, AssertionError) as err:
             if self.was_called_from_cli:
                 panel = Panel(err.args[0], width=None)