Fall 2025 CS 3200 Project Template

This is a template repo for Dr. Fontenot's Fall 2025 CS 3200 Course Project.

It includes most of the infrastructure setup (containers), sample databases, and example UI pages. Explore it fully and ask questions!

Prerequisites

• A GitHub Account
• A terminal-based git client or GUI Git client such as GitHub Desktop or the Git plugin for VSCode.
• A distribution of Python running on your laptop. The distribution supported by the course is Anaconda or Miniconda.
  • Create a new Python 3.11 environment in conda named db-proj by running:

    conda create -n db-proj python=3.11

  • Install the Python dependencies listed in api/requirements.txt and app/src/requirements.txt into your local Python environment. You can do this by running pip install -r requirements.txt in each respective directory.

    cd api
    pip install -r requirements.txt
    cd ../app
    pip install -r requirements.txt

    Note that the .. means go to the parent folder of the folder you're currently in (which is api/ after the first command)
• VSCode with the Python Plugin installed
  • You may use some other Python/code editor. However, Course staff will only support VS Code.

Structure of the Repo

• This repository is organized into five main directories:

  • ./app - the Streamlit app
  • ./api - the Flask REST API
  • ./database-files - SQL scripts to initialize the MySQL database
  • ./datasets - folder for storing datasets

• The repo also contains a docker-compose.yaml file that is used to set up the Docker containers for the front end app, the REST API, and MySQL database.

Suggestion for Learning the Project Code Base

If you are not familiar with web app development, this code base might be confusing. But don't worry, we'll get through it together. Here are some suggestions for learning the code base:

1. Start by exploring the ./app directory. This is where the Streamlit app is located. The Streamlit app is a Python-based web app that is used to interact with the user. It's a great way to build a simple web app without having to learn a lot of web development.
2. Next, explore the ./api directory. This is where the Flask REST API is located. The REST API is used to interact with the database and perform other server-side tasks. You might also consider this the "application logic" or "business logic" layer of your app.
3. Finally, explore the ./database-files directory. This is where the SQL scripts are located that will be used to initialize the MySQL database.
4. Bonus: If you want to have a totally separate copy of the Template Repo on your laptop that you can use to explore and try things without messing up your team repo, see Setting Up a Personal Testing Repo (Optional) section below.

Setting Up the Repos

Setting Up a Personal Testing Repo (Optional)

Setting Up A Personal Sandbox Repo (This is Optional)

Before you start: You need to have a GitHub account and a terminal-based git client or GUI Git client such as GitHub Desktop or the Git plugin for VSCode.

1. Clone this repo to your local machine.
   1. You can do this by clicking the green "Code" button on the top right of the repo page and copying the URL. Then, in your terminal, run git clone <URL>.
   2. Or, you can use the GitHub Desktop app to clone the repo. See this page of the GitHub Desktop Docs for more info.
2. Open the repository folder in VSCode.
3. Set up the .env file in the api folder based on the .env.template file.
   1. Make a copy of the .env.template file and name it .env.
   2. Open the new .env file.
   3. On the last line, delete the <...> placeholder text, and put a password. Don't reuse any passwords you use for any other services (email, etc.)
4. For running the testing containers (for your personal repo), you will tell docker compose to use a different configuration file than the typical one. The one you will use for testing is sandbox.yaml.
   1. docker compose -f sandbox.yaml up -d to start all the containers in the background
   2. docker compose -f sandbox.yaml down to shutdown and delete the containers
   3. docker compose -f sandbox.yaml up db -d only start the database container (replace db with api or app for the other two services as needed)
   4. docker compose -f sandbox.yaml stop to "turn off" the containers but not delete them.

Setting Up Your Team's Repo

Before you start: As a team, one person needs to assume the role of Team Project Repo Owner.

1. The Team Project Repo Owner needs to fork this template repo into their own GitHub account and give the repo a name consistent with your project's name. If you're worried that the repo is public, don't. Every team is doing a different project.
2. In the newly forked team repo, the Team Project Repo Owner should go to the Settings tab, choose Collaborators and Teams on the left-side panel. Add each of your team members to the repository with Write access.

Remaining Team Members

1. Each of the other team members will receive an invitation to join.
2. Once you have accepted the invitation, you should clone the Team's Project Repo to your local machine.
3. Set up the .env file in the api folder based on the .env.template file.
4. For running the testing containers (for your team's repo):
   1. docker compose up -d to start all the containers in the background
   2. docker compose down to shutdown and delete the containers
   3. docker compose up db -d only start the database container (replace db with api or app for the other two services as needed)
   4. docker compose stop to "turn off" the containers but not delete them.

Note: You can also use the Docker Desktop GUI to start and stop the containers after the first initial run.

Important Tips

1. In general, any changes you make to the api code base (REST API) or the Streamlit app code should be hot reloaded when the files are saved. This means that the changes should be immediately available.
   1. Don't forget to hit click the Always Rerun button in the browser tab of the Streamlit app for it to reload with changes.
   2. Sometimes, a bug in the code will shut the containers down. If this is the case, try and fix the bug in the code. Then you can restart the web-app container in Docker Desktop or restart all the containers with docker compose restart (no -d flag).
2. The MySQL Container is different.
   1. When the MySQL container is created the first time, it will execute any .sql files in the ./database-files folder. Important: it will execute them in alphabetical order.
   2. The MySQL Container's log files are your friend! Remember, you can access them in Docker Desktop by going to the MySQL Container, and clicking on the Logs tab. If there are errors in your .sql files as it is trying to run them, there will be a message in the logs. You can search 🔍 for Error to find them more quickly.
   3. If you need to update anything in any of your SQL files, you MUST recreate the MySQL container (rather than just stopping and restarting it). You can recreate the MySQL container by using the following command: docker compose down db -v && docker compose up db -d.
      1. docker compose down db -v stops and deletes the MySQL container and the volume attached to it.
      2. docker compose up db -d will create a new db container and re-run the files in the database-files folder.

Handling User Role Access and Control

In most applications, when a user logs in, they assume a particular role in the app. For instance, when one logs in to a stock price prediction app, they may be a single investor, a portfolio manager, or a corporate executive (of a publicly traded company). Each of those roles will likely present some similar features as well as some different features when compared to the other roles. So, how do you accomplish this in Streamlit? This is sometimes called Role-based Access Control, or RBAC for short.

The code in this project demonstrates how to implement a simple RBAC system in Streamlit but without actually using user authentication (usernames and passwords). The Streamlit pages from the original template repo are split up among 3 roles - Political Strategist, USAID Worker, and a System Administrator role (this is used for any sort of system tasks such as re-training ML model, etc.). It also demonstrates how to deploy an ML model.

Wrapping your head around this will take a little time and exploration of this code base. Some highlights are below.

Getting Started with the RBAC

1. We need to turn off the standard panel of links on the left side of the Streamlit app. This is done through the app/src/.streamlit/config.toml file. So check that out. We are turning it off so we can control directly what links are shown.
2. Then I created a new python module in app/src/modules/nav.py. When you look at the file, you will se that there are functions for basically each page of the application. The st.sidebar.page_link(...) adds a single link to the sidebar. We have a separate function for each page so that we can organize the links/pages by role.
3. Next, check out the app/src/Home.py file. Notice that there are 3 buttons added to the page and when one is clicked, it redirects via st.switch_page(...) to that Roles Home page in app/src/pages. But before the redirect, I set a few different variables in the Streamlit session_state object to track role, first name of the user, and that the user is now authenticated.
4. Notice near the top of app/src/Home.py and all other pages, there is a call to SideBarLinks(...) from the app/src/nav.py module. This is the function that will use the role set in session_state to determine what links to show the user in the sidebar.
5. The pages are organized by Role. Pages that start with a 0 are related to the Political Strategist role. Pages that start with a 1 are related to the USAID worker role. And, pages that start with a 2 are related to The System Administrator role.

(Completely Optional) Incorporating ML Models into your Project

Note: This project only contains the infrastructure for a hypothetical ML model.

1. Collect and preprocess necessary datasets for your ML models.
2. Build, train, and test your ML model in a Jupyter Notebook.
   • You can store your datasets in the datasets folder. You can also store your Jupyter Notebook in the ml-src folder.
3. Once your team is happy with the model's performance, convert your Jupyter Notebook code for the ML model to a pure Python script.
   • You can include the training and testing functionality as well as the prediction functionality.
   • Develop and test this pure Python script first in the ml-src folder.
   • You may or may not need to include data cleaning, though.
4. Review the api/backend/ml_models module. In this folder,
   • We've put a sample (read fake) ML model in the model01.py file. The predict function will be called by the Flask REST API to perform 'real-time' prediction based on model parameter values that are stored in the database. Important: you would never want to hard code the model parameter weights directly in the prediction function.
5. The prediction route for the REST API is in api/backend/customers/customer_routes.py. Basically, it accepts two URL parameters and passes them to the prediction function in the ml_models module. The prediction route/function packages up the value(s) it receives from the model's predict function and send its back to Streamlit as JSON.
6. Back in streamlit, check out app/src/pages/11_Prediction.py. Here, I create two numeric input fields. When the button is pressed, it makes a request to the REST API URL /c/prediction/.../... function and passes the values from the two inputs as URL parameters. It gets back the results from the route and displays them. Nothing fancy here.