Update Dockerfile

Dockerfile

@@ -1,37 +1,39 @@
-# Use a slim Python 3.11 image to keep the base footprint small
 FROM python:3.11-slim
 
-# Prevent Python from writing pyc files and keep stdout/stderr unbuffered
 ENV PYTHONDONTWRITEBYTECODE=1
 ENV PYTHONUNBUFFERED=1
 
-# Install system dependencies required by Whisper
+# 1. Install system dependencies
 RUN apt-get update && apt-get install -y \
     ffmpeg \
     git \
     && rm -rf /var/lib/apt/lists/*
 
-# Set the working directory
-WORKDIR /app
+# 2. Create the non-root user required by Hugging Face Spaces (UID 1000)
+RUN useradd -m -u 1000 user
+USER user
 
-# Copy dependency list first to leverage Docker layer caching
-COPY requirements.txt .
+# Set environment variables for the new user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
 
-# Optimize PyTorch installation: 
-# If you are deploying to a standard CPU environment, pulling the CPU-only wheels 
-# prevents downloading CUDA binaries, saving ~2GB of image space.
-RUN pip install --no-cache-dir torch torchaudio --index-url https://download.pytorch.org/whl/cpu
+WORKDIR $HOME/app
 
-# Install the rest of the Python dependencies
-RUN pip install --no-cache-dir -r requirements.txt
+# 3. Copy requirements and transfer ownership to the new user
+COPY --chown=user requirements.txt .
 
-# Pre-cache the Whisper 'medium' model during the image build phase
-RUN python -c "import whisper; whisper.load_model('medium')"
+# 4. Install Python dependencies as the user
+RUN pip install --user --no-cache-dir torch torchaudio --index-url https://download.pytorch.org/whl/cpu
+RUN pip install --user --no-cache-dir -r requirements.txt
 
-# Copy the application code and necessary binary files (like q_table.pkl)
-COPY . .
+# 5. Pre-cache Whisper WITHOUT loading it into PyTorch memory to avoid OOM limits.
+# Using whisper._download handles the checksum logic and places it in the user's cache directory.
+RUN python -c "import whisper, os; whisper._download(whisper._MODELS['medium'], os.path.expanduser('~/.cache/whisper'), False)"
 
-# Expose the port defined in your backend.py
+# 6. Copy the rest of the application code
+COPY --chown=user . .
+
+# Expose the correct Hugging Face port
 EXPOSE 7860
 
 # Start the Flask application