Update Dockerfile

Dockerfile

@@ -1,30 +1,28 @@
-# STEP 1: Change this line to a CUDA 11.7.1 base image
-FROM nvidia/cuda:11.7.1-devel-ubuntu22.04
+# Start from a standard Python 3.10 image
+FROM python:3.10-slim-buster
 
 # Set up the environment
 ENV DEBIAN_FRONTEND=noninteractive
 ENV PYTHONUNBUFFERED=1
 
-# Install Python and pip
-RUN apt-get update && apt-get install -y \
-    python3.10 python3-pip \
-    && rm -rf /var/lib/apt/lists/*
+# Install system dependencies needed for OpenCV
+RUN apt-get update && apt-get install -y libgl1-mesa-glx && rm -rf /var/lib/apt/lists/*
 
-# Set the working directory inside the container
+# Set the working directory
 WORKDIR /app
 
-# Set a writable cache directory for Hugging Face models
+# Set a writable cache directory
 ENV HF_HOME=/app/cache
 
-# Create the cache directory and give it open permissions
+# Create the cache directory
 RUN mkdir -p /app/cache && chmod 777 /app/cache
 
-# STEP 2: Change this line to install PyTorch for CUDA 11.7
-RUN pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
+# Install PyTorch for CUDA 11.3. It will link to the host's CUDA libraries at runtime.
+RUN pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
 
 # Copy and install the rest of the Python dependencies
 COPY requirements.txt .
-RUN pip3 install --no-cache-dir -r requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
 
 # Copy your main application code
 COPY main.py .
@@ -32,5 +30,5 @@ COPY main.py .
 # Expose the port your API will run on
 EXPOSE 8000
 
-# The command to start the Uvicorn server when the container runs
+# The command to start the Uvicorn server
 CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]