Create Dockerfile

Dockerfile

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+# syntax=docker/dockerfile:1
+
+# Base image using the specified Python version
+ARG PYTHON_VERSION=3.12.3
+FROM python:${PYTHON_VERSION}-slim as base
+
+# Set environment variables to prevent Python from writing .pyc files
+# and to keep Python from buffering stdout and stderr
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1
+
+# Set working directory inside the container
+WORKDIR /app
+
+# Install TensorFlow and Keras (no need for tf-keras as a separate package)
+# Using a single RUN command to reduce layers
+RUN pip install tensorflow==2.16.1 keras
+
+# Create a non-privileged user to run the application
+# Follow best practices from Docker's user handling
+ARG UID=10001
+RUN adduser --disabled-password --gecos "" --home "/nonexistent" \
+    --shell "/sbin/nologin" --no-create-home --uid "${UID}" appuser
+
+# Prepare environment for Transformers and NLTK
+# Creating cache directories with appropriate permissions
+ENV TRANSFORMERS_CACHE=/tmp/.cache/huggingface \
+    NLTK_DATA=/tmp/nltk_data
+RUN mkdir -p $TRANSFORMERS_CACHE $NLTK_DATA && chmod -R 777 $TRANSFORMERS_CACHE $NLTK_DATA
+
+# Copy the Python requirements file and install dependencies
+# Using a single COPY to reduce layers and ensure requirements.txt is present
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+# Copy the rest of the application source code into the container
+COPY . .
+
+# Switch to the non-privileged user for running the application
+USER appuser
+
+# Expose the port that the application will listen on
+EXPOSE 7860
+
+# Command to run the application using uvicorn
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]