Adding auto-formatting of code with black. (#14)

* Adding auto-formatting of code with black.

* Removing duplicate black dependency.

* Automatically reformatting code with black

* Reverting black version for py3.6 support in tests.

* Auto-formats generated OpenAPI code.

Co-authored-by: Auto-format Bot <runner@fv-az617-496.pwnmbzmyba5ezmab2zpzaqbtmg.cx.internal.cloudapp.net>

Author: robotrapta

.github/workflows/auto-format.yaml

@@ -0,0 +1,28 @@
+name: auto-format
+on: pull_request
+jobs:
+  format:
+    # Check if the PR is not from a fork
+    if: github.event.pull_request.head.repo.full_name == github.repository
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v1
+        with:
+          ref: ${{ github.head_ref }}
+      - name: black
+        #uses: lgeiger/black-action@v1.0.1
+        uses: psf/black@stable
+        with:
+          src: "."
+          options: "--verbose"
+      - name: Check for modified files
+        id: git-check
+        run: echo ::set-output name=modified::$(if git diff-index --quiet HEAD --; then echo "false"; else echo "true"; fi)
+      - name: Push changes
+        if: steps.git-check.outputs.modified == 'true'
+        run: |
+          git config --global user.name 'Auto-format Bot'
+          git remote set-url origin https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/${{ github.repository }}
+          git commit -am "Automatically reformatting code with black"
+          git push
+

Makefile

@@ -9,6 +9,8 @@ generate: install  ## Generate the SDK from our public openapi spec
 		-g python \
 		-o ./generated
 	poetry run datamodel-codegen  --input spec/public-api.yaml --output generated/model.py
+	poetry run black .
+
 
 test-local: install  ## Run integration tests against an API server running at http://localhost:8000/device-api (needs GROUNDLIGHT_API_TOKEN)
 	GROUNDLIGHT_TEST_API_ENDPOINT="http://localhost:8000/device-api" poetry run pytest --cov=src test --log-cli-level INFO

generated/model.py

@@ -14,91 +14,71 @@
 class ClassificationResult(BaseModel):
     confidence: Optional[confloat(ge=0.0, le=1.0)] = Field(
         None,
-        description='On a scale of 0 to 1, how confident are we in the predicted label?',
+        description="On a scale of 0 to 1, how confident are we in the predicted label?",
     )
-    label: str = Field(..., description='What is the predicted label?')
+    label: str = Field(..., description="What is the predicted label?")
 
 
 class DetectorCreationInput(BaseModel):
-    name: constr(max_length=200) = Field(
-        ..., description='A short, descriptive name for the detector.'
-    )
-    query: constr(max_length=300) = Field(
-        ..., description='A question about the image.'
-    )
+    name: constr(max_length=200) = Field(..., description="A short, descriptive name for the detector.")
+    query: constr(max_length=300) = Field(..., description="A question about the image.")
     group_name: Optional[constr(max_length=100)] = Field(
-        None, description='Which group should this detector be part of?'
+        None, description="Which group should this detector be part of?"
     )
     confidence_threshold: Optional[confloat(ge=0.0, le=1.0)] = Field(
         0.9,
         description="If the detector's prediction is below this confidence threshold, send the image query for human review.",
     )
     config_name: Optional[constr(max_length=100)] = Field(
         None,
-        description='(Advanced usage) If your account has multiple named ML configuration options enabled, you can use this field to specify which one you would like to use.',
+        description="(Advanced usage) If your account has multiple named ML configuration options enabled, you can use this field to specify which one you would like to use.",
     )
 
 
 class DetectorTypeEnum(Enum):
-    detector = 'detector'
+    detector = "detector"
 
 
 class ImageQueryTypeEnum(Enum):
-    image_query = 'image_query'
+    image_query = "image_query"
 
 
 class ResultTypeEnum(Enum):
-    binary_classification = 'binary_classification'
+    binary_classification = "binary_classification"
 
 
 class Detector(BaseModel):
-    id: str = Field(..., description='A unique ID for this object.')
-    type: DetectorTypeEnum = Field(..., description='The type of this object.')
-    created_at: datetime = Field(..., description='When this detector was created.')
-    name: constr(max_length=200) = Field(
-        ..., description='A short, descriptive name for the detector.'
-    )
-    query: str = Field(..., description='A question about the image.')
-    group_name: str = Field(
-        ..., description='Which group should this detector be part of?'
-    )
+    id: str = Field(..., description="A unique ID for this object.")
+    type: DetectorTypeEnum = Field(..., description="The type of this object.")
+    created_at: datetime = Field(..., description="When this detector was created.")
+    name: constr(max_length=200) = Field(..., description="A short, descriptive name for the detector.")
+    query: str = Field(..., description="A question about the image.")
+    group_name: str = Field(..., description="Which group should this detector be part of?")
     confidence_threshold: Optional[confloat(ge=0.0, le=1.0)] = Field(
         0.9,
         description="If the detector's prediction is below this confidence threshold, send the image query for human review.",
     )
 
 
 class ImageQuery(BaseModel):
-    id: str = Field(..., description='A unique ID for this object.')
-    type: ImageQueryTypeEnum = Field(..., description='The type of this object.')
-    created_at: datetime = Field(..., description='When was this detector created?')
-    query: str = Field(..., description='A question about the image.')
-    detector_id: str = Field(
-        ..., description='Which detector was used on this image query?'
-    )
-    result_type: ResultTypeEnum = Field(
-        ..., description='What type of result are we returning?'
-    )
+    id: str = Field(..., description="A unique ID for this object.")
+    type: ImageQueryTypeEnum = Field(..., description="The type of this object.")
+    created_at: datetime = Field(..., description="When was this detector created?")
+    query: str = Field(..., description="A question about the image.")
+    detector_id: str = Field(..., description="Which detector was used on this image query?")
+    result_type: ResultTypeEnum = Field(..., description="What type of result are we returning?")
     result: ClassificationResult
 
 
 class PaginatedDetectorList(BaseModel):
     count: Optional[int] = Field(None, example=123)
-    next: Optional[AnyUrl] = Field(
-        None, example='http://api.example.org/accounts/?page=4'
-    )
-    previous: Optional[AnyUrl] = Field(
-        None, example='http://api.example.org/accounts/?page=2'
-    )
+    next: Optional[AnyUrl] = Field(None, example="http://api.example.org/accounts/?page=4")
+    previous: Optional[AnyUrl] = Field(None, example="http://api.example.org/accounts/?page=2")
     results: Optional[List[Detector]] = None
 
 
 class PaginatedImageQueryList(BaseModel):
     count: Optional[int] = Field(None, example=123)
-    next: Optional[AnyUrl] = Field(
-        None, example='http://api.example.org/accounts/?page=4'
-    )
-    previous: Optional[AnyUrl] = Field(
-        None, example='http://api.example.org/accounts/?page=2'
-    )
+    next: Optional[AnyUrl] = Field(None, example="http://api.example.org/accounts/?page=4")
+    previous: Optional[AnyUrl] = Field(None, example="http://api.example.org/accounts/?page=2")
     results: Optional[List[ImageQuery]] = None