Activate model

app.py

@@ -1,21 +1,21 @@
 import gradio as gr
 from transformers import pipeline
 
-# token_classifier = pipeline(
-#   model="ZurichNLP/swissbert-ner",
-#   aggregation_strategy="simple",
-# )
+token_classifier = pipeline(
+  model="ZurichNLP/swissbert-ner",
+  aggregation_strategy="simple",
+)
 
-def run_ner_placeholder(text: str, language: str):
-    assert language in {"de_CH", "fr_CH", "it_CH", "rm_CH"}, f"Language {language} not supported"
-    # Placeholder
-    assert text == "Mein Name sei Gantenbein."
-    return [{'entity_group': 'PER',
-      'score': 0.50026333,
-      'word': 'Gantenbein',
-      'start': 13,
-      'end': 24}
-    ]
+# def run_ner_placeholder(text: str, language: str):
+#     assert language in {"de_CH", "fr_CH", "it_CH", "rm_CH"}, f"Language {language} not supported"
+#     # Placeholder
+#     assert text == "Mein Name sei Gantenbein."
+#     return [{'entity_group': 'PER',
+#       'score': 0.50026333,
+#       'word': 'Gantenbein',
+#       'start': 13,
+#       'end': 24}
+#     ]
 
 def run_ner(text: str, language: str):
   token_classifier.model.set_default_language(language)
@@ -23,7 +23,7 @@ def run_ner(text: str, language: str):
   return output
 
 def visualize_ner(text: str, language: str):
-  labels = run_ner_placeholder(text, language)
+  labels = run_ner(text, language)
   return {
     "entities": labels,
     "text": text,