Update Dockerfile

Dockerfile

@@ -4,13 +4,19 @@ FROM python:3.10-slim
 # Set environment variables
 ENV PYTHONDONTWRITEBYTECODE=1 \
     PYTHONUNBUFFERED=1 \
-    HF_HOME=/app/.cache \
+    HF_HOME=/home/user/.cache/huggingface \
+    TRANSFORMERS_CACHE=/home/user/.cache/huggingface/transformers \
     TRANSFORMERS_VERBOSITY=error
 
-# Set working directory and create necessary folders
+# Create a non-root user
+RUN useradd -m -u 1000 user
+
+# Set working directory
 WORKDIR /app
-RUN mkdir -p /app/.cache /app/models/suno-bark /app/models/sentiment && \
-    chmod -R 777 /app/.cache /app/models
+
+# Create necessary folders with proper permissions
+RUN mkdir -p /home/user/.cache/huggingface /app/models/suno-bark /app/models/sentiment && \
+    chmod -R 777 /home/user/.cache /app/models
 
 # Install OS dependencies
 RUN apt-get update && apt-get install -y --no-install-recommends \
@@ -25,40 +31,35 @@ RUN pip install --no-cache-dir --root-user-action=ignore -U pip && \
         "protobuf" && \
     pip install --no-cache-dir --root-user-action=ignore -r requirements.txt
 
-RUN apt-get update && apt-get install -y libsndfile1
+# Ensure soundfile is installed
 RUN pip install soundfile
 
+# Set user to non-root for Hugging Face compatibility
+USER user
+
 # Download Bark TTS model
 RUN python3 - <<EOF
-try:
-    from transformers import AutoTokenizer, AutoProcessor, BarkModel
-    model_name = "suno/bark-small"
-    print(f"Downloading {model_name}...")
-    AutoTokenizer.from_pretrained(model_name).save_pretrained("/app/models/suno-bark")
-    AutoProcessor.from_pretrained(model_name).save_pretrained("/app/models/suno-bark")
-    BarkModel.from_pretrained(model_name).save_pretrained("/app/models/suno-bark")
-except Exception as e:
-    print("ERROR:", e)
-    exit(1)
+from transformers import AutoTokenizer, AutoProcessor, BarkModel
+model_name = "suno/bark-small"
+print(f"Downloading {model_name}...")
+AutoTokenizer.from_pretrained(model_name).save_pretrained("/app/models/suno-bark")
+AutoProcessor.from_pretrained(model_name).save_pretrained("/app/models/suno-bark")
+BarkModel.from_pretrained(model_name).save_pretrained("/app/models/suno-bark")
 EOF
 
 # Download sentiment analysis model
 RUN python3 - <<EOF
-try:
-    from transformers import AutoModelForSequenceClassification, AutoTokenizer
-    model_name = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
-    print(f"Downloading {model_name}...")
-    AutoTokenizer.from_pretrained(model_name).save_pretrained("/app/models/sentiment")
-    AutoModelForSequenceClassification.from_pretrained(model_name).save_pretrained("/app/models/sentiment")
-except Exception as e:
-    print("ERROR:", e)
-    exit(1)
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+model_name = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
+print(f"Downloading {model_name}...")
+AutoTokenizer.from_pretrained(model_name).save_pretrained("/app/models/sentiment")
+AutoModelForSequenceClassification.from_pretrained(model_name).save_pretrained("/app/models/sentiment")
 EOF
 
 # Copy application source code
-COPY app.py .
+COPY --chown=user:user app.py .
 
-# Expose port for the application
+# Expose port
 EXPOSE 7860
 
 # Launch app using Uvicorn