Data App Frameworks
Gradio, Streamlit, and Evidence are three popular tools for building interactive data applications. While all three allow users to create web-based UIs, they differ in their primary focus:
- Gradio is optimized for machine learning models and AI demos.
- Streamlit is designed for Python-based data apps and dashboarding.
- Evidence is designed for SQL analysts and business reporting.
This guide compares them in ease of use, features, performance, and best use cases to help you decide which tool fits your needs.
Feature Comparison
| Feature | Gradio | Streamlit | Evidence |
|---|---|---|---|
| Dashboarding | Limited | Built for dashboards | Optimized for reporting |
| UI Components | Basic | Rich | Rich |
| Data Source | Python Functions | Python DataFrames | SQL Databases |
| Source Code | Python | Python | SQL + Markdown |
| Deployment | Python Runtime | Python Runtime | Static Site |
| Performance | Fast | Fast | Instant |
| Customizability | Limited | More flexible | Fully White-Labelable |
| Machine Learning Integration | Optimized for ML models | Requires additional setup | Not ML-focused |
When to Use Gradio
- Building AI/ML Demos: If you’re showcasing an ML model and want users to interact with it via input fields, sliders, or images, Gradio provides a simple API.
- Quick Prototyping for ML: Gradio makes it easy to wrap a function and deploy a UI in just a few lines of code.
Example: Creating a Simple ML Model Demo in Gradio
import gradio as gr
def sentiment_analysis(text):
return "Positive" if "good" in text else "Negative"
gr.Interface(fn=sentiment_analysis, inputs="text", outputs="text").launch()When to Use Streamlit
- Building Data Apps & Dashboards: If you need interactive charts, tables, or reports, Streamlit is the better choice.
- Custom UI Components: Unlike Gradio, Streamlit offers custom layouts and widgets for data visualization.
Example: Creating an Interactive Dashboard in Streamlit
import streamlit as st
import pandas as pd
data = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
st.dataframe(data)When to Use Evidence
- SQL-Driven Reporting & Analytics: If your data is stored in a database and your team is most comfortable with SQL, Evidence is the best option.
- Automated & Interactive Dashboards: Evidence allows users to write SQL queries that update dynamically when users interact with the UI, eliminating the need for Python scripting.
Example: Creating an Interactive Report in Evidence
```sql my_query
SELECT category, SUM(sales) FROM orders GROUP BY category
```
<DataTable data={my_query} />This ensures that reports remain query-driven and dynamically updated, allowing users to explore real-time data without needing Python scripts.
Performance Considerations
- Gradio loads quickly but is not optimized for large datasets.
- Streamlit is more suitable for handling structured data and dashboards.
- Evidence is optimized for query-based analytics with real-time updates.
Gradio vs. Streamlit vs. Evidence: Which One to Choose?
| Use Case | Best Option |
|---|---|
| Showcasing AI Models | Gradio |
| Creating Data Dashboards | Streamlit |
| SQL-Based Reporting | Evidence |
| Interactive ML Prototyping | Gradio |
| Business Intelligence & Reporting | Evidence |
Conclusion
- Use Gradio if you’re developing AI/ML demos and interactive models.
- Use Streamlit if you need Python-based dashboards and analytics apps.
- Use Evidence if your workflow is SQL-based and requires structured reporting.
Check out the Evidence documentation for more details on SQL-driven reporting.
