Add a feature to upload intent detection and slot filling data

backend/api/tests/data/intent/example.jsonl

@@ -0,0 +1,5 @@
+{"text": "exampleA", "entities": [[0, 1, "LOC"]], "cats": ["positive"]}
+{"text": "exampleB", "cats": ["positive"]}
+{"text": "exampleC", "entities": [[0, 1, "LOC"]]}
+{"text": "exampleD"}
+{"entities": [[0, 1, "LOC"]], "cats": ["positive"]}

backend/api/tests/test_tasks.py

@@ -2,8 +2,10 @@ import pathlib
 
 from django.test import TestCase
 
-from ..models import (DOCUMENT_CLASSIFICATION, SEQ2SEQ, SEQUENCE_LABELING,
-                      Category, DocType, Example, Span, SpanType)
+from ..models import (DOCUMENT_CLASSIFICATION,
+                      INTENT_DETECTION_AND_SLOT_FILLING, SEQ2SEQ,
+                      SEQUENCE_LABELING, Category, DocType, Example, Span,
+                      SpanType)
 from ..tasks import ingest_data
 from .api.utils import prepare_project
 
@@ -236,3 +238,28 @@ class TestIngestSeq2seqData(TestIngestData):
         ]
         self.ingest_data(filename, file_format)
         self.assert_examples(dataset)
+
+
+class TextIngestIntentDetectionAndSlotFillingData(TestIngestData):
+    task = INTENT_DETECTION_AND_SLOT_FILLING
+
+    def assert_examples(self, dataset):
+        self.assertEqual(Example.objects.count(), len(dataset))
+        for text, expected_labels in dataset:
+            example = Example.objects.get(text=text)
+            cats = set(cat.label.text for cat in example.categories.all())
+            entities = [(span.start_offset, span.end_offset, span.label.text) for span in example.spans.all()]
+            self.assertEqual(cats, set(expected_labels['cats']))
+            self.assertEqual(entities, expected_labels['entities'])
+
+    def test_entities_and_cats(self):
+        filename = 'intent/example.jsonl'
+        file_format = 'JSONL'
+        dataset = [
+            ('exampleA', {'cats': ['positive'], 'entities': [(0, 1, 'LOC')]}),
+            ('exampleB', {'cats': ['positive'], 'entities': []}),
+            ('exampleC', {'cats': [], 'entities': [(0, 1, 'LOC')]}),
+            ('exampleD', {'cats': [], 'entities': []}),
+        ]
+        self.ingest_data(filename, file_format)
+        self.assert_examples(dataset)

backend/api/views/statistics.py

@@ -7,8 +7,8 @@ from rest_framework.permissions import IsAuthenticated
 from rest_framework.response import Response
 from rest_framework.views import APIView
 
-from ..models import (Annotation, Category, ExampleState, Project, RoleMapping,
-                      Span)
+from ..models import (Annotation, Category, Example, ExampleState, Project,
+                      RoleMapping, Span)
 from ..permissions import IsInProjectReadOnlyOrAdmin
 
 
@@ -29,7 +29,7 @@ class StatisticsAPI(APIView):
             response['user_label'] = user_count
 
         if not include or 'total' in include or 'remaining' in include or 'user' in include:
-            progress = self.progress(project=p)
+            progress = self.progress()
             response.update(progress)
 
         if not include or 'confirmed_count' in include:
@@ -41,8 +41,8 @@ class StatisticsAPI(APIView):
 
         return Response(response)
 
-    def progress(self, project):
-        examples = project.examples.values('id')
+    def progress(self):
+        examples = Example.objects.filter(project=self.kwargs['project_id']).values('id')
         total = examples.count()
         done = ExampleState.objects.count_done(examples)
         done_by_user = ExampleState.objects.count_user(examples)
@@ -50,8 +50,6 @@ class StatisticsAPI(APIView):
         return {'total': total, 'remaining': remaining, 'user': done_by_user}
 
     def label_per_data(self, project):
-        # annotation_class = project.get_annotation_class()
-        # return annotation_class.objects.get_label_per_data(project=project)
         return {}, {}
 
     def confirmed_count(self, project):
@@ -70,16 +68,21 @@ class StatisticsAPI(APIView):
         return confirmed_count
 
 
+class ProgressAPI(APIView):
+
+    def get(self, request, *args, **kwargs):
+        examples = Example.objects.filter(project=self.kwargs['project_id']).values('id')
+        total = examples.count()
+        done = ExampleState.objects.count_done(examples)
+        return {'total': total, 'remaining': total - done}
+
+
 class LabelFrequency(abc.ABC, APIView):
     permission_classes = [IsAuthenticated & IsInProjectReadOnlyOrAdmin]
     model = Annotation
 
     def get(self, request, *args, **kwargs):
-        return self.calc_label_frequency()
-
-    def calc_label_frequency(self):
-        project = get_object_or_404(Project, pk=self.kwargs['project_id'])
-        examples = project.examples.values('id')
+        examples = Example.objects.filter(project=self.kwargs['project_id']).values('id')
         return self.model.objects.calc_label_frequency(examples)
 
 

backend/api/views/upload/catalog.py

@@ -4,7 +4,8 @@ from typing import Dict, List, Type
 from pydantic import BaseModel
 from typing_extensions import Literal
 
-from ...models import (DOCUMENT_CLASSIFICATION, IMAGE_CLASSIFICATION, SEQ2SEQ,
+from ...models import (DOCUMENT_CLASSIFICATION, IMAGE_CLASSIFICATION,
+                       INTENT_DETECTION_AND_SLOT_FILLING, SEQ2SEQ,
                        SEQUENCE_LABELING, SPEECH2TEXT)
 from . import examples
 
@@ -244,6 +245,11 @@ Options.register(SEQ2SEQ, JSON, OptionColumn, examples.Text_JSON)
 Options.register(SEQ2SEQ, JSONL, OptionColumn, examples.Text_JSONL)
 Options.register(SEQ2SEQ, Excel, OptionColumn, examples.Text_CSV)
 
+# Intent detection and slof filling
+Options.register(INTENT_DETECTION_AND_SLOT_FILLING, TextFile, OptionEncoding, examples.Generic_TextFile)
+Options.register(INTENT_DETECTION_AND_SLOT_FILLING, TextLine, OptionEncoding, examples.Generic_TextLine)
+Options.register(INTENT_DETECTION_AND_SLOT_FILLING, JSONL, OptionNone, examples.IDSF_JSONL)
+
 # Image classification
 Options.register(IMAGE_CLASSIFICATION, ImageFile, OptionNone, examples.Generic_ImageFile)
 

backend/api/views/upload/examples.py

@@ -92,3 +92,8 @@ lamb  O
 Peter B-PER
 Blackburn I-PER
 """
+
+IDSF_JSONL = """
+{"text": "Find a flight from Memphis to Tacoma", "entities": [[0, 26, "City"], [30, 36, "City"]], "cats": ["flight"]}
+{"text": "I want to know what airports are in Los Angeles", "entities": [[36, 47, "City"]], "cats": ["airport"]}
+"""

backend/api/views/upload/factories.py

@@ -1,10 +1,16 @@
-from ...models import (DOCUMENT_CLASSIFICATION, IMAGE_CLASSIFICATION, SEQ2SEQ,
+from ...models import (DOCUMENT_CLASSIFICATION, IMAGE_CLASSIFICATION,
+                       INTENT_DETECTION_AND_SLOT_FILLING, SEQ2SEQ,
                        SEQUENCE_LABELING, SPEECH2TEXT)
 from . import builders, catalog, cleaners, data, label, parsers, readers
 
 
 def get_data_class(project_type: str):
-    text_projects = [DOCUMENT_CLASSIFICATION, SEQUENCE_LABELING, SEQ2SEQ]
+    text_projects = [
+        DOCUMENT_CLASSIFICATION,
+        SEQUENCE_LABELING,
+        SEQ2SEQ,
+        INTENT_DETECTION_AND_SLOT_FILLING
+    ]
     if project_type in text_projects:
         return data.TextData
     else:
@@ -59,14 +65,28 @@ def create_bulder(project, **kwargs):
         name=kwargs.get('column_data') or readers.DEFAULT_TEXT_COLUMN,
         value_class=get_data_class(project.project_type)
     )
-    # Todo: If project is EntityClassification,
+    # If project is intent detection and slot filling,
     # column names are fixed: entities, cats
-    label_column = builders.LabelColumn(
-        name=kwargs.get('column_label') or readers.DEFAULT_LABEL_COLUMN,
-        value_class=get_label_class(project.project_type)
-    )
+    if project.project_type == INTENT_DETECTION_AND_SLOT_FILLING:
+        label_columns = [
+            builders.LabelColumn(
+                name='cats',
+                value_class=label.CategoryLabel
+            ),
+            builders.LabelColumn(
+                name='entities',
+                value_class=label.SpanLabel
+            )
+        ]
+    else:
+        label_columns = [
+            builders.LabelColumn(
+                name=kwargs.get('column_label') or readers.DEFAULT_LABEL_COLUMN,
+                value_class=get_label_class(project.project_type)
+            )
+        ]
     builder = builders.ColumnBuilder(
         data_column=data_column,
-        label_columns=[label_column]
+        label_columns=label_columns
     )
     return builder