Update Dockerfile

Dockerfile

@@ -1,36 +1,31 @@
-FROM python:3.10-slim
+FROM python:3.12-slim
 
-# Set the working directory in the container
 WORKDIR /app
 
-# Set environment variables
-# This forces python print() statements to be sent straight to the terminal without buffering
 ENV PYTHONUNBUFFERED=1
-# This tells Hugging Face libraries where to store models
 ENV HF_HOME=/app/.cache/huggingface
 ENV SENTENCE_TRANSFORMERS_HOME=/app/.cache/sentence_transformers
 
-# Copy only the requirements file first to leverage Docker cache
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    libgomp1 \
+    && rm -rf /var/lib/apt/lists/*
+
 COPY requirements.txt .
 
-# Install dependencies
-RUN pip install --no-cache-dir -r requirements.txt
 
-# Pre-download the embedding model during the build process for faster startup
+RUN pip install --no-cache-dir -r requirements.txt
 RUN python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')"
 
-# Copy the rest of your application code
 COPY . .
 
-# Create a non-root user and give it ownership of the /app directory
-RUN useradd -m user && chown -R user:user /app
+RUN useradd -m user
+
+RUN mkdir -p $HF_HOME $SENTENCE_TRANSFORMERS_HOME && \
+    chown -R user:user /app
 
-# Switch to the non-root user
 USER user
 
-# Expose the port the app runs on
 EXPOSE 7860
 
-# Define the command to run your app
-# The key is to disable CORS and XSRF protection, as Hugging Face handles this.
 CMD ["streamlit", "run", "up_app.py", "--server.port=7860", "--server.address=0.0.0.0", "--server.enableCORS=false", "--server.enableXsrfProtection=false"]