ok

app.py

@@ -15,7 +15,7 @@ demo = gr.Interface(
     ],
     outputs=gr.Textbox(label="Extracted Aspect-Sentiment-Opinion Triplets"),
     title="Arabic ABSA (Aspect-Based Sentiment Analysis)",
-    description="Choose a model (mT5, mBART, GPT) to extract aspects, opinions, and sentiment using LoRA adapters"
+    description="Choose a model (Araberta, mT5, mBART, GPT) to extract aspects, opinions, and sentiment using LoRA adapters"
 )
 
 if __name__ == "__main__":

inference.py

@@ -6,7 +6,7 @@ from transformers import AutoTokenizer, AutoModelForTokenClassification
 
 # Define supported models and their adapter IDs
 MODEL_OPTIONS = {
-    "araberta": {
+    "Araberta": {
         "base": "asmashayea/absa-araberta",
         "adapter": "asmashayea/absa-araberta"
     },
@@ -56,7 +56,7 @@ def predict_absa(text, model_choice):
     elif model_choice == 'mBART':
         decoded = infer_mBart_prompt(text, tokenizer, model)
 
-    elif model_choice == 'bert':
+    elif model_choice == 'Araberta':
 
         model = AutoModelForTokenClassification.from_pretrained("asmashayea/absa-araberta")
         tokenizer = AutoTokenizer.from_pretrained("asmashayea/absa-araberta")