Dockerfile

# Use Python 3.10 slim image
FROM python:3.10-slim

# Set environment variables
ENV PYTHONDONTWRITEBYTECODE=1 \
    PYTHONUNBUFFERED=1 \
    HF_HOME=/app/.cache \
    TRANSFORMERS_VERBOSITY=error

# Set working directory and create necessary folders
WORKDIR /app
RUN mkdir -p /app/.cache /app/models/suno-bark /app/models/sentiment && \
    chmod -R 777 /app/.cache /app/models

# Install OS dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
    libsndfile1 ffmpeg ca-certificates && \
    rm -rf /var/lib/apt/lists/*

# Copy requirements and install Python dependencies
COPY requirements.txt .
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 .

# Expose port for Hugging Face Space
EXPOSE 7860

# Launch app using Uvicorn
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]