Dockerfile

# Use Python 3.9 as the base image
FROM python:3.9

# Set working directory in the container
WORKDIR /app

# Create a non-root user and set permissions
RUN useradd -m myuser && chown -R myuser:myuser /app
USER myuser

# Set Hugging Face cache directory
ENV HF_HOME=/app/.cache/huggingface

# Update PATH to include user’s local bin (where pip installs uvicorn)
ENV PATH="/home/myuser/.local/bin:${PATH}"

# Copy requirements.txt and install dependencies
COPY --chown=myuser:myuser requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Explicitly install uvicorn to ensure it’s available
RUN pip install --no-cache-dir uvicorn

# Pre-download the model to avoid runtime issues
RUN python -c "from transformers import pipeline; pipeline('text-classification', model='roberta-base-openai-detector')"

# Copy the application code
COPY --chown=myuser:myuser . .

# Run the FastAPI app with Uvicorn
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]