Creating Analysis Applications with AI

In this guide, you’ll use AI-powered Copilot Chat to build interactive Python applications with user interfaces for exploring and analyzing ASAM ODS data. This extends the notebook-based workflows into standalone tools.

Overview

This playground demonstrates:

• Designing application architecture with AI

• Generating complete Python applications with UI frameworks

• Creating interactive dashboards and data exploration tools

• Deploying applications for team use

Time to complete: 30-45 minutes

Recommended Frameworks

For interactive applications, consider these frameworks:

• Streamlit — Simple, rapid UI development with Python (recommended for beginners)

• Dash — More advanced dashboards and web applications

• PyQt / Tkinter — Desktop applications with rich UI

This guide focuses on Streamlit for its ease of use.

Step 1: Define Your Application

Determine what your application should do. Examples:

• Data Explorer — Browse measurements, filter by criteria, view details

• Dashboard — Real-time monitoring of measurement values

• Analysis Tool — Run analysis workflows with user-configurable parameters

• Report Generator — Create PDF reports from selected data

Step 2: Ask Copilot to Design the Application

In Copilot Chat, describe your application:

I want to build a Streamlit application that lets users:
1. Connect to an ASAM ODS server
2. Browse measurements from a selected test
3. View temperature data with a line chart
4. Compare measurements side-by-side

Copilot will:

1. Ask clarifying questions about UI layout and features

2. Suggest library choices (Streamlit, Dash, etc.)

3. Outline application structure

4. Generate complete code

Step 3: Generate and run Application Code

Copilot will create a Streamlit application structure. Launch the application:

streamlit run app.py

Streamlit will:

• Start a local development server (usually http://localhost:8501)

• Open your application in the default browser

• Auto-reload on code changes

Step 4: Test and Refine

Interact with your application:

1. Test all features — Connect, browse data, create charts

2. Identify issues — Note any bugs or missing functionality

3. Ask Copilot for improvements — Request UI enhancements or new features

Example refinement requests:

Add a date range picker to filter measurements

Create a comparison view to display two measurements side-by-side

Add statistics (mean, max, min, std dev) below the chart

Add login functionality to control data access

Tips & Best Practices

1. Start small — Create a minimal working app, then add features

2. Use session state — Streamlit sessions persist UI state

3. Cache expensive operations — Use @st.cache_data to avoid recomputing

4. Validate inputs — Check user inputs before querying ASAM ODS server

5. Handle errors gracefully — Show user-friendly error messages

6. Document code — Add comments and docstrings

7. Test thoroughly — Verify all user interactions

Next Steps

• Learn more Streamlit — Visit Streamlit documentation

• Advanced ODS queries — Review odsbox-jaquel-mcp documentation

• Share your app — Deploy and share with your team

• Extend functionality — Add databases, APIs, or other integrations

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Congratulations! You’ve completed the AI playground series. You now have skills to:

• Explore ASAM ODS data interactively

• Generate analysis notebooks with AI

• Build standalone applications

Next steps: Explore the odsbox-jaquel-mcp GitHub for advanced techniques and the MCP Playground for more examples.