Cloud-For-AI-Assignment

Data set

https://www.kaggle.com/datasets/samuelcortinhas/sports-balls-multiclass-image-classification/data

Application

Local

Create a new python environment (tested with python 3.10) and install dependencies using

pip install -r requirements-local.txt

Frontend

The frontend is built using Streamlit. In order to run it, execute following commands:

cd <project-root>/app
streamlit run home.py

Note: change base_url = "http://localhost:8000" in main.py.

Backend

The backend is built using Fastapi. In order to run it, execute following commands:

cd <project-root>/api
uvicorn main:app --host api --port 8000

Docker

Frontend

The frontend is built using Streamlit. In order to run it, execute following commands:

cd <project-root>/app
docker build -t <tag-name> .
docker run -p 8501:8501 <tag-name>

Navigate in your browser to http://localhost:8501.

Backend

The backend is built using Fastapi. In order to run it, execute following commands:

cd <project-root>/api
docker build -t <tag-name> .
docker run -p 8000:8000 <tag-name>

Docker-compose

Run both front- and backend simultaneously with docker compose:

docker-compose build
docker-compose up