Update Dockerfile

Dockerfile

@@ -1,4 +1,4 @@
-# Use Python 3.10 with slim image
+# Use Python 3.10 slim image
 FROM python:3.10-slim
 
 # Set environment variables
@@ -7,52 +7,52 @@ ENV PYTHONDONTWRITEBYTECODE=1 \
     HF_HOME=/app/.cache \
     TRANSFORMERS_VERBOSITY=error
 
-# Create directories with proper permissions
+# Set working directory and create necessary folders
 WORKDIR /app
-RUN mkdir -p /app/.cache /app/models && \
+RUN mkdir -p /app/.cache /app/models/suno-bark /app/models/sentiment && \
     chmod -R 777 /app/.cache /app/models
 
-# Install system dependencies (INCLUDING SSL CERTS)
+# Install OS dependencies
 RUN apt-get update && apt-get install -y --no-install-recommends \
     libsndfile1 ffmpeg ca-certificates && \
     rm -rf /var/lib/apt/lists/*
 
-# Install Python dependencies
+# Copy requirements and install Python dependencies
 COPY requirements.txt .
-RUN pip install --no-cache-dir -U pip && \
-    pip install --no-cache-dir -r requirements.txt
-
-# Pre-download TTS model (WITH ERROR HANDLING)
-RUN python -c "try: \
-    from transformers import AutoTokenizer, AutoProcessor, BarkModel; \
-    model_name = 'suno/bark-small'; \
-    print(f'Downloading {model_name}...'); \
-    tokenizer = AutoTokenizer.from_pretrained(model_name); \
-    processor = AutoProcessor.from_pretrained(model_name); \
-    model = BarkModel.from_pretrained(model_name); \
-    tokenizer.save_pretrained('/app/models/suno-bark'); \
-    processor.save_pretrained('/app/models/suno-bark'); \
-    model.save_pretrained('/app/models/suno-bark'); \
-    except Exception as e: \
-    print(f'ERROR: {str(e)}'); \
-    exit(1)"
-
-# Pre-download sentiment model (WITH ERROR HANDLING)
-RUN python -c "try: \
-    from transformers import AutoModelForSequenceClassification, AutoTokenizer; \
-    model_name = 'cardiffnlp/twitter-xlm-roberta-base-sentiment'; \
-    print(f'Downloading {model_name}...'); \
-    tokenizer = AutoTokenizer.from_pretrained(model_name); \
-    model = AutoModelForSequenceClassification.from_pretrained(model_name); \
-    tokenizer.save_pretrained('/app/models/sentiment'); \
-    model.save_pretrained('/app/models/sentiment'); \
-    except Exception as e: \
-    print(f'ERROR: {str(e)}'); \
-    exit(1)"
-
-# Copy application code
-COPY app.py .
-
-# Expose port and run
+RUN pip install --no-cache-dir --root-user-action=ignore -r requirements.txt
+
+# 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)
+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)
+EOF
+
+# Copy application source code
+COPY app ./app
+
+# Expose port for Hugging Face Space
 EXPOSE 7860
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+
+# Launch app using Uvicorn
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]