3  Develop an app with streamlit

3.1 Context and Goal

ImportantGoal

Transition from a prototype .ipynb file to a production-ready .py file that builds an interactive Streamlit dashboard.

Data scientists often start in Jupyter Notebooks for exploration and analysis. However, real-world applications require deployable, interactive dashboards for sharing insights with others. The goal here is to:

• Refactor your notebook code into modular Python functions.
• Wrap those functions inside an interactive Streamlit interface.
• Understand how to iterate quickly with streamlit run app.py.

3.2 Start from a .ipynb file

• Download the following files from Canvas course.

  • Compute Financial Ratios Notebook.ipynb – a Jupyter Notebook
  • sp500_data.csv – Financial data.
  • sp500_tickers.csv – List of tickers.

• Create a folder named streamlit25 in the default directory using bash command.

• Drag these three files into this folder in your browser.

3.3 Make it a .py file with functions

The syntax of defining a python function.

• 1. Read the Compute Financial Ratios Notebook.ipynb
• 2. Add a z-score metric seen in Figure 3.1.

Figure 3.1: Z-score reference

• 3. Make a empty finratios.py script file inside the same folder using bash command.
• 4. Re-write the notebook code into reusable functions inside finratios.py
  • ‘fetch_data_local’
  • fetch_data_local_single_ticker
  • calculate_metrics
  • plot_trend

Good practices:

• Each function should do one clear thing (e.g., load data, compute metrics, or plot)
• When writing functions, only use variables that come from the input parameters. If you need to use something inside the function, then make them input parameters.

Warning⚠️ Be Careful with Function Inputs

Don’t rely on variables that aren’t passed into your function.
If your function can’t run using only its inputs, it’s not truly reusable.

✅ Do this:

def compute_margin(revenue, cost):
    return (revenue - cost) / revenue

🚫 Not this:

total_revenue = 1000
def compute_margin(cost):
    return (total_revenue - cost) / total_revenue  
    # works in notebook, breaks in module

• Decide on return values.
  
• Add a if __name__ == "__main__": for testing at the bottom of finratios.py, such as:

if __name__ == "__main__":
    # Simple test cases
    print(compute_pe_ratio(1000000, 50000, 30))

3.4 Learn a bit about Streamlit

Tip

You don’t know what Streamlit can do. Streamlit doesn’t know what you want.
But, we’ve gotta start somewhere.
So let’s learn a bit. Once you know what it can do,
you’ll finally know what you can make it do (with AI’s help).

TipHow learn to use a new Python package?

• What is Streamlit, and where is its API reference and official documentation?
• What can Streamlit do — what kinds of apps or problems is it best at solving?
• What small examples or experiments can I build to quickly discover and learn its core features?

• Start with the official docs and examples — skim the Streamlit docs homepage and gallery to get a mental map of what’s possible before diving into code.
• Learn by doing small experiments — build micro-apps (e.g., one with a button, one with a chart) to turn reading into muscle memory.
  • E.g., create app1.py, app2.py files each to test and run some micro streamlit apps.
• Read code, not just tutorials — explore community demos or open-source Streamlit apps to see how others structure layouts, manage state, and handle inputs.
• Iterate with feedback loops — run streamlit run app.py often and tweak one thing at a time; immediate visual feedback accelerates understanding.
• Reflect and generalize — after each mini-project, note what patterns repeat (e.g., sidebar widgets, caching, layout control) to build knowledge for future tools.

3.5 Build a Streamlit app

Here is an example. Try to build a dashboard displaying the financial metrics interactively.

An Streamlit Example

3.6 💡 Bonus: Add an AI Feature

Integrate the Google Gemini API to enhance your app with AI-powered capabilities.

• Explore the API
  Learn how to use the Google Gemini API and follow the setup guide to install the SDK.

• Get an API Key
  You can obtain a free API key using your Google account.

• Keep It Secure
  Never share or publish your API key on GitHub or any public platform — treat it like a password. Delete it after use.