Dockerfile

# ==============================================================================
# DOCKERFILE FOR PRODUCT RETURN PREDICTION API
# ==============================================================================

# 1. Use a lightweight official Python base image to keep the container fast and small
FROM python:3.9-slim

# 2. Set the working directory inside the container
WORKDIR /app

# 3. Copy requirements first (optimizes Docker cache for faster rebuilds)
COPY requirements.txt .

# 4. Install dependencies
# --no-cache-dir reduces image size by preventing pip from caching packages
RUN pip install --no-cache-dir --upgrade pip && \
    pip install --no-cache-dir -r requirements.txt

# 5. Copy all application code (api.py, function.py, and model folders) into the container
COPY . .

# 6. Adjust directory permissions
# Essential for cloud deployments (like Hugging Face Spaces) to allow read/write operations
RUN chmod -R 777 /app

# 7. Command to start the FastAPI server
# IMPORTANT: "api:app" points to the 'app' object inside 'api.py'
# Port 7860 is the default port required by Hugging Face Spaces
CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "7860"]