Switch to public Sinhala sentiment analysis model (sinbert-small)

Dockerfile

@@ -23,8 +23,7 @@ COPY --chown=user . $HOME/app
 
 # Pre-download the Hugging Face model during the build stage
 # This saves time and bandwidth during container startup
-RUN --mount=type=secret,id=HF_TOKEN,mode=0444,required=true \
-    python -c "import os; from transformers import pipeline; token = open('/run/secrets/HF_TOKEN').read().strip(); pipeline('sentiment-analysis', model='keshan/sinhala-sentiment-analysis', token=token)"
+RUN python -c "from transformers import pipeline; pipeline('sentiment-analysis', model='sinhala-nlp/sinhala-sentiment-analysis-sinbert-small')"
 
 # Make port 7860 available to the world outside this container
 EXPOSE 7860

app/model.py

@@ -5,13 +5,8 @@ import os
 
 logger = logging.getLogger(__name__)
 
-# Login with token if available
-hf_token = os.environ.get("HF_TOKEN")
-if hf_token:
-    login(token=hf_token)
-
 # Using a robust Sinhala sentiment analysis model from Hugging Face
-MODEL_NAME = "keshan/sinhala-sentiment-analysis"
+MODEL_NAME = "sinhala-nlp/sinhala-sentiment-analysis-sinbert-small"
 sentiment_pipeline = None
 
 def load_model():