hide dropdown menus when the labels match

app_text_classification.py

@@ -4,6 +4,7 @@ import os
 import time
 import subprocess
 import logging
+import threading
 
 import json
 
@@ -169,14 +170,12 @@ def get_demo():
                 all_mappings["features"][feat] = ds_features[i]
         write_column_mapping(all_mappings)
 
-    def list_labels_and_features_from_dataset(dataset_id, dataset_config, dataset_split, model_id2label, model_features):
-        ds_labels, ds_features = get_labels_and_features_from_dataset(dataset_id, dataset_config, dataset_split)
-        if ds_labels is None or ds_features is None:
-            return [gr.Dropdown(visible=False) for _ in range(MAX_LABELS + MAX_FEATURES)]
+    def list_labels_and_features_from_dataset(ds_labels, ds_features, model_id2label):
         model_labels = list(model_id2label.values())
         lables = [gr.Dropdown(label=f"{label}", choices=model_labels, value=model_id2label[i], interactive=True, visible=True) for i, label in enumerate(ds_labels[:MAX_LABELS])]
         lables += [gr.Dropdown(visible=False) for _ in range(MAX_LABELS - len(lables))]
-        features = [gr.Dropdown(label=f"{feature}", choices=ds_features, value=ds_features[0], interactive=True, visible=True) for feature in model_features]
+        # TODO: Substitute 'text' with more features for zero-shot
+        features = [gr.Dropdown(label=f"{feature}", choices=ds_features, value=ds_features[0], interactive=True, visible=True) for feature in ['text']]
         features += [gr.Dropdown(visible=False) for _ in range(MAX_FEATURES - len(features))]
         return lables + features
     
@@ -196,24 +195,43 @@ def get_demo():
                 gr.update(visible=False),
                 *[gr.update(visible=False) for _ in range(MAX_LABELS + MAX_FEATURES)]
             )
-        model_id2label = ppl.model.config.id2label
-        model_features = ['text']
-        column_mappings = list_labels_and_features_from_dataset(
-            dataset_id, 
-            dataset_config, 
-            dataset_split, 
-            model_id2label, 
-            model_features
-        )
-
+        
+        dropdown_placement = [gr.Dropdown(visible=False) for _ in range(MAX_LABELS + MAX_FEATURES)]
+            
         if ppl is None:
             gr.Warning("Model not found")
             return (
                 gr.update(visible=False),
                 gr.update(visible=False),
                 gr.update(visible=False, open=False),
+                *dropdown_placement
+            )
+        model_id2label = ppl.model.config.id2label
+        ds_labels, ds_features = get_labels_and_features_from_dataset(dataset_id, dataset_config, dataset_split)
+        
+        if ds_labels is None or ds_features is None:
+            gr.Warning(CONFIRM_MAPPING_DETAILS_FAIL_RAW)
+            return (
+                gr.update(visible=False),
+                gr.update(visible=False),
+                gr.update(visible=False, open=False),
+                *dropdown_placement
+            )
+        
+        column_mappings = list_labels_and_features_from_dataset(
+            ds_labels,
+            ds_features,
+            model_id2label,
+        )
+        if model_id2label.items() != ds_labels.items():
+            gr.Warning(CONFIRM_MAPPING_DETAILS_FAIL_RAW)
+            return (
+                gr.update(visible=False),
+                gr.update(visible=False),
+                gr.update(visible=True, open=True),
                 *column_mappings
             )
+
         prediction_input, prediction_output = get_example_prediction(ppl, dataset_id, dataset_config, dataset_split)
         return (
             gr.update(value=prediction_input, visible=True),