Merge pull request #996 from roboflow/block_copy

Update Block Copy

inference/core/workflows/core_steps/analytics/line_counter/v1.py

@@ -65,11 +65,11 @@ class LineCounterManifest(WorkflowBlockManifest):
     )
 
     line_segment: Union[list, Selector(kind=[LIST_OF_VALUES_KIND]), Selector(kind=[LIST_OF_VALUES_KIND])] = Field(  # type: ignore
-        description="Line in the format [[x1, y1], [x2, y2]] consisting of exactly two points. For line [[0, 100], [100, 100]] line will count objects entering from the bottom as IN",
+        description="Line consisting of exactly two points. For line [[0, 100], [100, 100]], objects entering from the bottom will count as IN.",
         examples=[[[0, 50], [500, 50]], "$inputs.zones"],
     )
     triggering_anchor: Union[str, Selector(kind=[STRING_KIND]), Literal[tuple(sv.Position.list())]] = Field(  # type: ignore
-        description=f"Point from the detection for triggering line crossing.",
+        description=f"The point on the detection that must cross the line to be counted.",
         default="CENTER",
         examples=["CENTER"],
     )

inference/core/workflows/core_steps/analytics/line_counter/v2.py

@@ -62,16 +62,16 @@ class LineCounterManifest(WorkflowBlockManifest):
             INSTANCE_SEGMENTATION_PREDICTION_KIND,
         ]
     ) = Field(  # type: ignore
-        description="Predictions",
+        description="Model predictions to count line crossings for.",
         examples=["$steps.object_detection_model.predictions"],
     )
 
     line_segment: Union[list, Selector(kind=[LIST_OF_VALUES_KIND]), Selector(kind=[LIST_OF_VALUES_KIND])] = Field(  # type: ignore
-        description="Line in the format [[x1, y1], [x2, y2]] consisting of exactly two points. For line [[0, 100], [100, 100]] line will count objects entering from the bottom as IN",
+        description="Line consisting of exactly two points. For line [[0, 100], [100, 100]], objects entering from the bottom will count as IN.",
         examples=[[[0, 50], [500, 50]], "$inputs.zones"],
     )
     triggering_anchor: Union[str, Selector(kind=[STRING_KIND]), Literal[tuple(sv.Position.list())]] = Field(  # type: ignore
-        description=f"Point from the detection for triggering line crossing.",
+        description=f"The point on the detection that must cross the line to be counted.",
         default="CENTER",
         examples=["CENTER"],
     )

inference/core/workflows/core_steps/analytics/path_deviation/v1.py

@@ -62,7 +62,7 @@ class PathDeviationManifest(WorkflowBlockManifest):
         examples=["$steps.object_detection_model.predictions"],
     )
     triggering_anchor: Union[str, Selector(kind=[STRING_KIND]), Literal[tuple(sv.Position.list())]] = Field(  # type: ignore
-        description=f"Triggering anchor. Allowed values: {', '.join(sv.Position.list())}",
+        description=f"Point on the detection that will be used to calculate the Frechet distance.",
         default="CENTER",
         examples=["CENTER"],
     )

inference/core/workflows/core_steps/analytics/time_in_zone/v1.py

@@ -67,25 +67,25 @@ class TimeInZoneManifest(WorkflowBlockManifest):
             INSTANCE_SEGMENTATION_PREDICTION_KIND,
         ]
     ) = Field(  # type: ignore
-        description="Predictions",
+        description="Model predictions to calculate the time spent in zone for.",
         examples=["$steps.object_detection_model.predictions"],
     )
     zone: Union[list, Selector(kind=[LIST_OF_VALUES_KIND]), Selector(kind=[LIST_OF_VALUES_KIND])] = Field(  # type: ignore
-        description="Zones (one for each batch) in a format [(x1, y1), (x2, y2), (x3, y3), ...]",
+        description="Coordinates of the target zone.",
         examples=["$inputs.zones"],
     )
-    triggering_anchor: Union[str, Selector(kind=[STRING_KIND])] = Field(  # type: ignore
-        description=f"Triggering anchor. Allowed values: {', '.join(sv.Position.list())}",
+    triggering_anchor: Union[str, Selector(kind=[STRING_KIND]), Literal[tuple(sv.Position.list())]] = Field(  # type: ignore
+        description=f"The point on the detection that must be inside the zone.",
         default="CENTER",
         examples=["CENTER"],
     )
     remove_out_of_zone_detections: Union[bool, Selector(kind=[BOOLEAN_KIND])] = Field(  # type: ignore
-        description=f"If true, detections found outside of zone will be filtered out",
+        description=f"If true, detections found outside of zone will be filtered out.",
         default=True,
         examples=[True, False],
     )
     reset_out_of_zone_detections: Union[bool, Selector(kind=[BOOLEAN_KIND])] = Field(  # type: ignore
-        description=f"If true, detections found outside of zone will have time reset",
+        description=f"If true, detections found outside of zone will have time reset.",
         default=True,
         examples=[True, False],
     )

inference/core/workflows/core_steps/analytics/time_in_zone/v2.py

@@ -64,25 +64,25 @@ class TimeInZoneManifest(WorkflowBlockManifest):
             INSTANCE_SEGMENTATION_PREDICTION_KIND,
         ]
     ) = Field(  # type: ignore
-        description="Predictions",
+        description="Model predictions to calculate the time spent in zone for.",
         examples=["$steps.object_detection_model.predictions"],
     )
     zone: Union[list, Selector(kind=[LIST_OF_VALUES_KIND]), Selector(kind=[LIST_OF_VALUES_KIND])] = Field(  # type: ignore
-        description="Zones (one for each batch) in a format [(x1, y1), (x2, y2), (x3, y3), ...]",
-        examples=["$inputs.zones"],
+        description="Coordinates of the target zone.",
+        examples=[[(100, 100), (100, 200), (300, 200), (300, 100)], "$inputs.zones"],
     )
-    triggering_anchor: Union[str, Selector(kind=[STRING_KIND])] = Field(  # type: ignore
-        description=f"Triggering anchor. Allowed values: {', '.join(sv.Position.list())}",
+    triggering_anchor: Union[str, Selector(kind=[STRING_KIND]), Literal[tuple(sv.Position.list())]] = Field(  # type: ignore
+        description=f"The point on the detection that must be inside the zone.",
         default="CENTER",
         examples=["CENTER"],
     )
     remove_out_of_zone_detections: Union[bool, Selector(kind=[BOOLEAN_KIND])] = Field(  # type: ignore
-        description=f"If true, detections found outside of zone will be filtered out",
+        description=f"If true, detections found outside of zone will be filtered out.",
         default=True,
         examples=[True, False],
     )
     reset_out_of_zone_detections: Union[bool, Selector(kind=[BOOLEAN_KIND])] = Field(  # type: ignore
-        description=f"If true, detections found outside of zone will have time reset",
+        description=f"If true, detections found outside of zone will have time reset.",
         default=True,
         examples=[True, False],
     )

inference/core/workflows/core_steps/analytics/velocity/v1.py

@@ -49,6 +49,11 @@ class VelocityManifest(WorkflowBlockManifest):
             "long_description": LONG_DESCRIPTION,
             "license": "Apache-2.0",
             "block_type": "analytics",
+            "ui_manifest": {
+                "section": "video",
+                "icon": "far fa-gauge",
+                "blockPriority": 2.5,
+            },
         }
     )
     type: Literal["roboflow_core/velocity@v1"]
@@ -59,7 +64,7 @@ class VelocityManifest(WorkflowBlockManifest):
             INSTANCE_SEGMENTATION_PREDICTION_KIND,
         ]
     ) = Field(  # type: ignore
-        description="Predictions",
+        description="Model predictions to calculate the velocity for.",
         examples=["$steps.object_detection_model.predictions"],
     )
     smoothing_alpha: Union[float, Selector(kind=[FLOAT_KIND])] = Field(  # type: ignore

inference/core/workflows/core_steps/classical_cv/size_measurement/v1.py

@@ -75,23 +75,24 @@ class SizeMeasurementManifest(WorkflowBlockManifest):
     )
     type: Literal["roboflow_core/size_measurement@v1"]
 
-    reference_predictions: Selector(
+    object_predictions: Selector(
         kind=[
             INSTANCE_SEGMENTATION_PREDICTION_KIND,
             OBJECT_DETECTION_PREDICTION_KIND,
         ]
     ) = Field(
-        description="Predictions from the reference object model",
-        examples=["$segmentation.reference_predictions"],
+        description="Model predictions to measure the dimensions of.",
+        examples=["$segmentation.object_predictions"],
     )
-    object_predictions: Selector(
+
+    reference_predictions: Selector(
         kind=[
             INSTANCE_SEGMENTATION_PREDICTION_KIND,
             OBJECT_DETECTION_PREDICTION_KIND,
         ]
     ) = Field(
-        description="Predictions from the model that detects the object to measure",
-        examples=["$segmentation.object_predictions"],
+        description="Reference object used to calculate the dimensions of the specified objects. If multiple objects are provided, the highest confidence prediction will be used.",
+        examples=["$segmentation.reference_predictions"],
     )
     reference_dimensions: Union[
         str,
@@ -101,8 +102,8 @@ class SizeMeasurementManifest(WorkflowBlockManifest):
             kind=[STRING_KIND, LIST_OF_VALUES_KIND],
         ),
     ] = Field(
-        description="Dimensions of the reference object (width, height) in desired units (e.g., inches) as a string in the format 'width,height' or as a tuple (width, height)",
-        examples=["5.0,5.0", (5.0, 5.0), "$inputs.reference_dimensions"],
+        description="Dimensions of the reference object in desired units, (e.g. inches). Will be used to convert the pixel dimensions of the other objects to real-world units.",
+        examples=[(4.5, 3.0), "5.0,5.0", "$inputs.reference_dimensions"],
     )
 
     @classmethod

inference/core/workflows/core_steps/flow_control/continue_if/v1.py

@@ -62,19 +62,20 @@ class BlockManifest(WorkflowBlockManifest):
     )
     type: Literal["roboflow_core/continue_if@v1", "ContinueIf"]
     condition_statement: StatementGroup = Field(
-        description="Workflows UQL definition of conditional logic.",
+        title="Conditional Statement",
+        description="Define the conditional logic.",
         examples=[CONDITION_STATEMENT_EXAMPLE],
     )
     evaluation_parameters: Dict[
         str,
         Selector(),
     ] = Field(
-        description="References to additional parameters that may be provided in runtime to parametrise operations",
+        description="Data to be used in the conditional logic.",
         examples=[{"left": "$inputs.some"}],
         default_factory=lambda: {},
     )
     next_steps: List[StepSelector] = Field(
-        description="Reference to step which shall be executed if expression evaluates to true",
+        description="Steps to execute if the condition evaluates to true.",
         examples=[["$steps.on_true"]],
     )
 

inference/core/workflows/core_steps/flow_control/delta_filter/v1.py

@@ -66,11 +66,11 @@ class DeltaFilterManifest(WorkflowBlockManifest):
     image: WorkflowImageSelector
     value: Selector(kind=[WILDCARD_KIND]) = Field(
         title="Input Value",
-        description="The input value for this step. Flow will be allowed to continue only if this value changes between frames.",
+        description="Flow will be allowed to continue only if this value changes between frames.",
         examples=["$steps.line_counter.count_in"],
     )
     next_steps: List[StepSelector] = Field(
-        description="Reference to steps which shall be executed when value changes.",
+        description="Steps to execute when the value changes.",
         examples=["$steps.write_to_csv", "$steps.write_to_opc"],
     )
 

inference/core/workflows/core_steps/flow_control/rate_limiter/v1.py

@@ -79,11 +79,11 @@ class RateLimiterManifest(WorkflowBlockManifest):
         ge=0.0,
     )
     depends_on: Selector() = Field(
-        description="Reference to any output of the the step which immediately preceeds this branch.",
+        description="Step immediately preceding this block.",
         examples=["$steps.model"],
     )
     next_steps: List[StepSelector] = Field(
-        description="Reference to steps which shall be executed if rate limit allows.",
+        description="Steps to execute if allowed by the rate limit.",
         examples=[["$steps.upload"]],
     )
     video_reference_image: Optional[WorkflowImageSelector] = Field(

inference/core/workflows/core_steps/formatters/expression/v1.py

@@ -134,7 +134,8 @@ class BlockManifest(WorkflowBlockManifest):
         default_factory=lambda: {},
     )
     switch: CasesDefinition = Field(
-        description="Definition of switch-case statement",
+        title="Logic Definition",
+        description="Define the logic to be executed.",
         examples=[SWITCH_STATEMENT_EXAMPLE],
     )
 

inference/core/workflows/core_steps/formatters/property_definition/v1.py

@@ -61,11 +61,11 @@ class BlockManifest(WorkflowBlockManifest):
         "PropertyExtraction",
     ]
     data: Selector() = Field(
-        description="Reference data to extract property from",
+        description="Data to extract property from.",
         examples=["$steps.my_step.predictions"],
     )
     operations: List[AllOperationsType] = Field(
-        description="List of operations to perform on data to generate output",
+        description="List of operations to perform on the data.",
         examples=[
             [{"type": "DetectionsPropertyExtract", "property_name": "class_name"}]
         ],

inference/core/workflows/core_steps/fusion/detections_stitch/v1.py

@@ -64,7 +64,7 @@ class BlockManifest(WorkflowBlockManifest):
     )
     type: Literal["roboflow_core/detections_stitch@v1"]
     reference_image: Selector(kind=[IMAGE_KIND]) = Field(
-        description="Image that was origin to take crops that yielded predictions.",
+        description="Original image that was cropped to produce the predictions.",
         examples=["$inputs.image"],
     )
     predictions: Selector(
@@ -73,7 +73,7 @@ class BlockManifest(WorkflowBlockManifest):
             INSTANCE_SEGMENTATION_PREDICTION_KIND,
         ]
     ) = Field(
-        description="The output of a detection model describing the bounding boxes to be merged.",
+        description="Model predictions to be merged into the original image.",
         examples=["$steps.my_object_detection_model.predictions"],
     )
     overlap_filtering_strategy: Union[
@@ -82,16 +82,16 @@ class BlockManifest(WorkflowBlockManifest):
     ] = Field(
         default="nms",
         description="Which strategy to employ when filtering overlapping boxes. "
-        "None does nothing, NMS discards surplus detections, NMM merges them.",
+        "None does nothing, NMS discards lower-confidence detections, NMM combines them.",
         examples=["nms", "$inputs.overlap_filtering_strategy"],
     )
     iou_threshold: Union[
         FloatZeroToOne,
         Selector(kind=[FLOAT_ZERO_TO_ONE_KIND]),
     ] = Field(
         default=0.3,
-        description="Parameter of overlap filtering strategy. If box intersection over union is above this "
-        " ratio, discard or merge the lower confidence box.",
+        description="Minimum overlap threshold between boxes. If intersection over union (IoU) is above this "
+        "ratio, discard or merge the lower confidence box.",
         examples=[0.4, "$inputs.iou_threshold"],
     )
 

inference/core/workflows/core_steps/models/foundation/anthropic_claude/v1.py

@@ -127,6 +127,7 @@ class BlockManifest(WorkflowBlockManifest):
             "relevant_for": {
                 "task_type": {"values": TASKS_REQUIRING_PROMPT, "required": True},
             },
+            "multiline": True,
         },
     )
     output_structure: Optional[Dict[str, str]] = Field(

inference/core/workflows/core_steps/models/foundation/clip/v1.py

@@ -54,7 +54,7 @@ class BlockManifest(WorkflowBlockManifest):
             "ui_manifest": {
                 "section": "model",
                 "icon": "far fa-paperclip",
-                "blockPriority": 2,
+                "blockPriority": 9.9,
             },
         }
     )

inference/core/workflows/core_steps/models/foundation/cog_vlm/v1.py

@@ -73,6 +73,9 @@ class BlockManifest(WorkflowBlockManifest):
     prompt: Union[Selector(kind=[STRING_KIND]), str] = Field(
         description="Text prompt to the CogVLM model",
         examples=["my prompt", "$inputs.prompt"],
+        json_schema_extra={
+            "multiline": True,
+        },
     )
     json_output_format: Optional[Dict[str, str]] = Field(
         default=None,

inference/core/workflows/core_steps/models/foundation/florence2/v1.py

@@ -182,6 +182,7 @@ class BaseManifest(WorkflowBlockManifest):
             "relevant_for": {
                 "task_type": {"values": TASKS_REQUIRING_PROMPT, "required": True},
             },
+            "multiline": True,
         },
     )
     classes: Optional[Union[Selector(kind=[LIST_OF_VALUES_KIND]), List[str]]] = Field(

inference/core/workflows/core_steps/models/foundation/google_gemini/v1.py

@@ -137,6 +137,7 @@ class BlockManifest(WorkflowBlockManifest):
             "relevant_for": {
                 "task_type": {"values": TASKS_REQUIRING_PROMPT, "required": True},
             },
+            "multiline": True,
         },
     )
     output_structure: Optional[Dict[str, str]] = Field(

inference/core/workflows/core_steps/models/foundation/llama_vision/v1.py

@@ -179,6 +179,7 @@ class BlockManifest(WorkflowBlockManifest):
             "relevant_for": {
                 "task_type": {"values": TASKS_REQUIRING_PROMPT, "required": True},
             },
+            "multiline": True,
         },
     )
     output_structure: Optional[Dict[str, str]] = Field(

inference/core/workflows/core_steps/models/foundation/lmm/v1.py

@@ -102,6 +102,9 @@ class BlockManifest(WorkflowBlockManifest):
     prompt: Union[Selector(kind=[STRING_KIND]), str] = Field(
         description="Holds unconstrained text prompt to LMM mode",
         examples=["my prompt", "$inputs.prompt"],
+        json_schema_extra={
+            "multiline": True,
+        },
     )
     lmm_type: Union[Selector(kind=[STRING_KIND]), Literal["gpt_4v", "cog_vlm"]] = Field(
         description="Type of LMM to be used", examples=["gpt_4v", "$inputs.lmm_type"]

inference/core/workflows/core_steps/models/foundation/openai/v1.py

@@ -81,6 +81,9 @@ class BlockManifest(WorkflowBlockManifest):
     prompt: Union[Selector(kind=[STRING_KIND]), str] = Field(
         description="Text prompt to the OpenAI model",
         examples=["my prompt", "$inputs.prompt"],
+        json_schema_extra={
+            "multiline": True,
+        },
     )
     openai_api_key: Union[Selector(kind=[STRING_KIND, SECRET_KIND]), Optional[str]] = (
         Field(

inference/core/workflows/core_steps/models/foundation/openai/v2.py

@@ -126,6 +126,7 @@ class BlockManifest(WorkflowBlockManifest):
             "relevant_for": {
                 "task_type": {"values": TASKS_REQUIRING_PROMPT, "required": True},
             },
+            "multiline": True,
         },
     )
     output_structure: Optional[Dict[str, str]] = Field(

inference/core/workflows/core_steps/models/foundation/stability_ai/image_gen/v1.py

@@ -80,6 +80,9 @@ class BlockManifest(WorkflowBlockManifest):
     ] = Field(
         description="Prompt to generate new images from text (what you wish to see)",
         examples=["my prompt", "$inputs.prompt"],
+        json_schema_extra={
+            "multiline": True,
+        },
     )
     negative_prompt: Optional[
         Union[