How to Build a Simple App Using FLASK with AI
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Last week, and the week before we looked at building a fundamental and technical analysis python script. Here’s how to tie them together as a flask app.
For this tutorial presume foo.py is the python script I showed you last week that does technical analysis and bar.py is the python script I showed you two weeks ago to do fundamental analysis.
How to Use AI to Build a Flask App on Linux app
AI can help you write a Flask app quickly. On Linux, keep the project small, use a virtual environment, & split the app into two Python files: foo.py for the Flask app entry point and bar.py for supporting logic. csweb.wooster
A clean starter layout:
my_flask_app/
├── foo.py
├── bar.py
└── venv/
foo.py is where Flask starts, and bar.py holds helper functions, routes, or other reusable code. That separation makes the app easier to test, expand, and explain to an AI assistant when you want changes later. oneuptime
Set up Linux first
Create and activate a virtual environment before installing Flask. That keeps your project isolated from your system and avoids dependency conflicts.
python3 -m venv venv
source venv/bin/activate
pip install flask
Write bar.py
Use bar.py for reusable logic. For a simple demo, it can expose a function that returns a message.
# bar.py
def get_message():
return "Hello from bar.py"
Write foo.py
Now make foo.py the Flask entry point. (Foo.py is the fundamental analysis script from two eeks ago)
# foo.py
from flask import Flask
from bar import get_message
app = Flask(__name__)
@app.route("/")
def home():
return get_message()
if __name__ == "__main__":
app.run(debug=True)
Flask can run from a normal Python file. debug=True makes development easier by reloading on changes and showing errors. flask.palletsprojects
Run the app
From the project directory, activate the virtual environment and launch the app.
source venv/bin/activate
python foo.py
Then visit
http://127.0.0.1:5000/
in your browser. The page should display the string returned by bar.py. flask.palletsprojects
How to use AI
The real advantage is not just code generation, but iteration. You can ask your AI to write and explain each step in plain English:
“Create a minimal Flask app using
foo.pyandbar.py.”“Move the greeting string into
bar.py.”“Add a second route to
foo.py.”“Make this Linux-friendly with a virtual environment.”
“Explain how to run it from the terminal.”
Flask projects are easy to modularize. Separating code into multiple files is a common way to keep the app maintainable. digitalocean
Example prompt chain
Here is a simple prompt sequence you can copy for any foo and bar you might write yourself:
“Write
foo.pyandbar.pyfor a tiny Flask app on Linux.”“Make
bar.pyhandle the message logic.”“Add a
/healthroute.”“Show me the Linux commands to create and run the app.”
“Rewrite this as a beginner-friendly Substack post.”
Each prompt narrows the task, which usually produces cleaner code than asking for everything at once. This is goal oriented multi-shot prompting.
Conclusion
The best way to use an AI assistant for Flask is to treat it like an assistant: give it a structure, ask for one change at a time, and keep the code modular. With just foo.py, bar.py, and a Linux virtual environment, you can get from idea to working app very quickly. csweb.wooster
AI is best used as a writing and coding assistant. If you just try to blindly prompt anything you’ll probably wind up with garbage.
Free eBook this week, good luck on the bar exam!
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