# # YOLOv5 🚀 by Ultralytics, GPL-3.0 license

# # Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
# FROM nvcr.io/nvidia/pytorch:21.05-py3

# # Install linux packages
# RUN apt update && apt install -y zip htop screen libgl1-mesa-glx

# # Install python dependencies
# COPY requirements.txt .
# RUN python -m pip install --upgrade pip
# RUN pip uninstall -y nvidia-tensorboard nvidia-tensorboard-plugin-dlprof
# RUN pip install --no-cache -r requirements.txt coremltools onnx gsutil notebook
# RUN pip install --no-cache -U torch torchvision numpy
# # RUN pip install --no-cache torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html

# # Create working directory
# RUN mkdir -p /usr/src/app
# WORKDIR /usr/src/app

# # Copy contents
# COPY . /usr/src/app

# # Set environment variables
# ENV HOME=/usr/src/app


# Usage Examples -------------------------------------------------------------------------------------------------------

# Build and Push
# t=ultralytics/yolov5:latest && sudo docker build -t $t . && sudo docker push $t

# Pull and Run
# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t

# Pull and Run with local directory access
# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t

# Kill all
# sudo docker kill $(sudo docker ps -q)

# Kill all image-based
# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest)

# Bash into running container
# sudo docker exec -it 5a9b5863d93d bash

# Bash into stopped container
# id=$(sudo docker ps -qa) && sudo docker start $id && sudo docker exec -it $id bash

# Clean up
# docker system prune -a --volumes
FROM python:3.9
EXPOSE 8501
WORKDIR /app
COPY requirements.txt ./requirements.txt
RUN pip3 install -r requirements.txt
COPY . .
# CMD streamlit run app.py
CMD streamlit run --server.port $PORT app.py