Update app.py

app.py

@@ -8,36 +8,10 @@ nltk.download('punkt')
 
 # Define available models
 models = {
-    "PFSA-ID-MEDWO-IndoBERT-LEM": "damand2061/pfsa-id-medwo-indobert-lem",
+    "PFSA-ID-IndoBERT-LEM": "damand2061/pfsa-id-indobert-lem",
     "PFSA-ID-MED-IndoBERT-LEM": "damand2061/pfsa-id-med-indobert-lem"
 }
 
-# Define label descriptions for each model
-label_descriptions = {
-    "PFSA-ID-MEDWO-IndoBERT-LEM": [
-        ["STATEMENT", "Pernyataan"],
-        ["CUECOREF", "Isyarat Pronomina"],
-        ["CUE", "Isyarat"],
-        ["AFFILIATION", "Afiliasi"],
-        ["ROLE", "Jabatan"],
-        ["PERSONCOREF", "Pronomina Orang"],
-        ["PERSON", "Orang"],
-    ],
-    "PFSA-ID-MED-IndoBERT-LEM": [
-        ["EVENT", "Acara"],
-        ["LOCATION", "Lokasi"],
-        ["DATETIME", "Waktu"],
-        ["ISSUE", "Isu"],
-        ["STATEMENT", "Pernyataan"],
-        ["CUECOREF", "Isyarat Pronomina"],
-        ["CUE", "Isyarat"],
-        ["AFFILIATION", "Afiliasi"],
-        ["ROLE", "Jabatan"],
-        ["PERSONCOREF", "Pronomina Orang"],
-        ["PERSON", "Orang"],
-    ]
-}
-
 def load_model(model_name):
     ner_pipeline = pipeline("ner", model=models[model_name])
     ner_pipeline.model.config.id2label = {k: v.replace("L-", "I-").replace("U-", "B-") for k, v in ner_pipeline.model.config.id2label.items()}
@@ -144,10 +118,20 @@ with gr.Blocks() as demo:
             gr.Examples(example_link, inputs=link_input)
 
     gr.Markdown("## Penjelasan Label")
-    label_description = gr.DataFrame(label_descriptions[list(models.keys())[0]], headers=["Label", "Keterangan"])
-
-    model_select_text.change(fn=update_label_descriptions, inputs=model_select_text, outputs=label_description)
-    model_select_link.change(fn=update_label_descriptions, inputs=model_select_link, outputs=label_description)
+    label_descriptions = [
+        ["EVENT", "Acara"],
+        ["LOCATION", "Lokasi"],
+        ["DATETIME", "Waktu"],
+        ["ISSUE", "Isu"],
+        ["STATEMENT", "Pernyataan"],
+        ["CUECOREF", "Isyarat Pronomina"],
+        ["CUE", "Isyarat"],
+        ["AFFILIATION", "Afiliasi"],
+        ["ROLE", "Jabatan"],
+        ["PERSONCOREF", "Pronomina Orang"],
+        ["PERSON", "Orang"],
+    ]
+    gr.DataFrame(label_descriptions, headers=["Label", "Keterangan"])
 
 if __name__ == "__main__":
     demo.launch()