chore: cleanup

Author: rishisurana-labelbox

libs/labelbox/src/labelbox/data/annotation_types/__init__.py

@@ -32,7 +32,6 @@
 from .classification import ClassificationAnswer
 from .classification import Radio
 from .classification import Text
-from .classification import FrameLocation
 
 from .data import GenericDataRowData
 from .data import MaskData

libs/labelbox/src/labelbox/data/annotation_types/classification/__init__.py

@@ -1 +1 @@
-from .classification import Checklist, ClassificationAnswer, Radio, Text, FrameLocation
+from .classification import Checklist, ClassificationAnswer, Radio, Text

libs/labelbox/src/labelbox/data/annotation_types/classification/classification.py

@@ -7,12 +7,6 @@
 from ..feature import FeatureSchema
 
 
-class FrameLocation(BaseModel):
-    """Represents a temporal frame range with start and end times (in milliseconds)."""
-    start: int
-    end: int
-
-
 class ClassificationAnswer(FeatureSchema, ConfidenceMixin, CustomMetricsMixin):
     """
     - Represents a classification option.
@@ -23,16 +17,11 @@ class ClassificationAnswer(FeatureSchema, ConfidenceMixin, CustomMetricsMixin):
       Each answer can have a keyframe independent of the others.
         So unlike object annotations, classification annotations
           track keyframes at a classification answer level.
-
-    - For temporal classifications (audio/video), optional frames can specify
-      one or more time ranges for this answer. Must be within root annotation's frame ranges.
-      Defaults to root frame ranges if not specified.
     """
 
     extra: Dict[str, Any] = {}
     keyframe: Optional[bool] = None
     classifications: Optional[List["ClassificationAnnotation"]] = None
-    frames: Optional[List[FrameLocation]] = None
 
 
 class Radio(ConfidenceMixin, CustomMetricsMixin, BaseModel):
@@ -80,11 +69,9 @@ class ClassificationAnnotation(
         classifications (Optional[List[ClassificationAnnotation]]): Optional sub classification of the annotation
         feature_schema_id (Optional[Cuid])
         value (Union[Text, Checklist, Radio])
-        frames (Optional[List[FrameLocation]]): Frame ranges for temporal classifications (audio/video). Must be within root annotation's frame ranges. Defaults to root frames if not specified.
         extra (Dict[str, Any])
     """
 
     value: Union[Text, Checklist, Radio]
     message_id: Optional[str] = None
-    frames: Optional[List[FrameLocation]] = None
 

libs/labelbox/src/labelbox/data/annotation_types/label.py

@@ -69,8 +69,8 @@ def validate_data(cls, data):
     def object_annotations(self) -> List[ObjectAnnotation]:
         return self._get_annotations_by_type(ObjectAnnotation)
 
-    def classification_annotations(self) -> List[ClassificationAnnotation]:
-        return self._get_annotations_by_type(ClassificationAnnotation)
+    def classification_annotations(self) -> List[Union[ClassificationAnnotation, TemporalClassificationText, TemporalClassificationQuestion]]:
+        return self._get_annotations_by_type((ClassificationAnnotation, TemporalClassificationText, TemporalClassificationQuestion))
 
     def _get_annotations_by_type(self, annotation_type):
         return [

libs/labelbox/src/labelbox/data/annotation_types/temporal.py

@@ -5,17 +5,13 @@
 frame-level precision. All temporal classifications support nested hierarchies.
 """
 
-from typing import List, Optional, Tuple, Union
-from pydantic import Field
+from typing import Any, Dict, List, Optional, Tuple, Union
+from pydantic import BaseModel, Field
 
-from labelbox.data.annotation_types.annotation import ClassificationAnnotation
-from labelbox.data.annotation_types.classification.classification import (
-    ClassificationAnswer,
-    FrameLocation,
-)
+from ...annotated_types import Cuid
 
 
-class TemporalClassificationAnswer(ClassificationAnswer):
+class TemporalClassificationAnswer(BaseModel):
     """
     Temporal answer for Radio/Checklist questions with frame ranges.
 
@@ -27,8 +23,8 @@ class TemporalClassificationAnswer(ClassificationAnswer):
         frames (List[Tuple[int, int]]): List of (start_frame, end_frame) ranges in milliseconds
         classifications (Optional[List[Union[TemporalClassificationText, TemporalClassificationQuestion]]]):
             Nested classifications within this answer
-        feature_schema_id (Optional[str]): Feature schema identifier
-        extra (dict): Additional metadata
+        feature_schema_id (Optional[Cuid]): Feature schema identifier
+        extra (Dict[str, Any]): Additional metadata
 
     Example:
         >>> # Radio answer with nested classifications
@@ -49,16 +45,19 @@ class TemporalClassificationAnswer(ClassificationAnswer):
         >>> )
     """
 
+    name: str
     frames: List[Tuple[int, int]] = Field(
         default_factory=list,
         description="List of (start_frame, end_frame) tuples in milliseconds",
     )
     classifications: Optional[
         List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]]
     ] = None
+    feature_schema_id: Optional[Cuid] = None
+    extra: Dict[str, Any] = Field(default_factory=dict)
 
 
-class TemporalClassificationText(ClassificationAnnotation):
+class TemporalClassificationText(BaseModel):
     """
     Temporal text classification with multiple text values at different frame ranges.
 
@@ -67,17 +66,17 @@ class TemporalClassificationText(ClassificationAnnotation):
 
     Args:
         name (str): Name of the text classification
-        values (List[Tuple[int, int, str]]): List of (start_frame, end_frame, text_value) tuples
+        value (List[Tuple[int, int, str]]): List of (start_frame, end_frame, text_value) tuples
         classifications (Optional[List[Union[TemporalClassificationText, TemporalClassificationQuestion]]]):
             Nested classifications
-        feature_schema_id (Optional[str]): Feature schema identifier
-        extra (dict): Additional metadata
+        feature_schema_id (Optional[Cuid]): Feature schema identifier
+        extra (Dict[str, Any]): Additional metadata
 
     Example:
         >>> # Simple text with multiple temporal values
         >>> transcription = TemporalClassificationText(
         >>>     name="transcription",
-        >>>     values=[
+        >>>     value=[
         >>>         (1600, 2000, "Hello, how can I help you?"),
         >>>         (2500, 3000, "Thank you for calling!"),
         >>>     ]
@@ -86,31 +85,33 @@ class TemporalClassificationText(ClassificationAnnotation):
         >>> # Text with nested classifications
         >>> transcription_with_notes = TemporalClassificationText(
         >>>     name="transcription",
-        >>>     values=[
+        >>>     value=[
         >>>         (1600, 2000, "Hello, how can I help you?"),
         >>>     ],
         >>>     classifications=[
         >>>         TemporalClassificationText(
         >>>             name="speaker_notes",
-        >>>             values=[
+        >>>             value=[
         >>>                 (1600, 2000, "Polite greeting"),
         >>>             ]
         >>>         )
         >>>     ]
         >>> )
     """
 
-    # Override parent's value field
+    name: str
     value: List[Tuple[int, int, str]] = Field(
         default_factory=list,
         description="List of (start_frame, end_frame, text_value) tuples",
     )
     classifications: Optional[
         List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]]
     ] = None
+    feature_schema_id: Optional[Cuid] = None
+    extra: Dict[str, Any] = Field(default_factory=dict)
 
 
-class TemporalClassificationQuestion(ClassificationAnnotation):
+class TemporalClassificationQuestion(BaseModel):
     """
     Temporal Radio/Checklist question with multiple answer options.
 
@@ -119,20 +120,20 @@ class TemporalClassificationQuestion(ClassificationAnnotation):
 
     Args:
         name (str): Name of the question/classification
-        answers (List[TemporalClassificationAnswer]): List of answer options with frame ranges
-        feature_schema_id (Optional[str]): Feature schema identifier
-        extra (dict): Additional metadata
+        value (List[TemporalClassificationAnswer]): List of answer options with frame ranges
+        feature_schema_id (Optional[Cuid]): Feature schema identifier
+        extra (Dict[str, Any]): Additional metadata
 
     Note:
-        - Radio: Single answer in the answers list
-        - Checklist: Multiple answers in the answers list
+        - Radio: Single answer in the value list
+        - Checklist: Multiple answers in the value list
         The serializer automatically handles the distinction based on the number of answers.
 
     Example:
         >>> # Radio question (single answer)
         >>> speaker = TemporalClassificationQuestion(
         >>>     name="speaker",
-        >>>     answers=[
+        >>>     value=[
         >>>         TemporalClassificationAnswer(
         >>>             name="user",
         >>>             frames=[(200, 1600)]
@@ -143,7 +144,7 @@ class TemporalClassificationQuestion(ClassificationAnnotation):
         >>> # Checklist question (multiple answers)
         >>> audio_quality = TemporalClassificationQuestion(
         >>>     name="audio_quality",
-        >>>     answers=[
+        >>>     value=[
         >>>         TemporalClassificationAnswer(
         >>>             name="background_noise",
         >>>             frames=[(0, 1500), (2000, 3000)]
@@ -158,14 +159,14 @@ class TemporalClassificationQuestion(ClassificationAnnotation):
         >>> # Nested structure: Radio > Radio > Radio
         >>> speaker_with_tone = TemporalClassificationQuestion(
         >>>     name="speaker",
-        >>>     answers=[
+        >>>     value=[
         >>>         TemporalClassificationAnswer(
         >>>             name="user",
         >>>             frames=[(200, 1600)],
         >>>             classifications=[
         >>>                 TemporalClassificationQuestion(
         >>>                     name="tone",
-        >>>                     answers=[
+        >>>                     value=[
         >>>                         TemporalClassificationAnswer(
         >>>                             name="professional",
         >>>                             frames=[(1000, 1600)]
@@ -178,14 +179,16 @@ class TemporalClassificationQuestion(ClassificationAnnotation):
         >>> )
     """
 
-    # Override parent's value field
+    name: str
     value: List[TemporalClassificationAnswer] = Field(
         default_factory=list,
         description="List of temporal answer options",
     )
     classifications: Optional[
         List[Union["TemporalClassificationText", "TemporalClassificationQuestion"]]
     ] = None
+    feature_schema_id: Optional[Cuid] = None
+    extra: Dict[str, Any] = Field(default_factory=dict)
 
 
 # Update forward references for recursive types