Taipy — Build Data & AI Web Apps in Python

What it is

Taipy is a full-stack Python framework that combines a GUI builder with workflow orchestration for data dashboards and AI pipelines. You write dashboards and data processing pipelines in Python without switching to JavaScript or separate orchestration tools.

The framework targets data scientists, ML engineers, and Python developers who want to build interactive web applications around their data and AI models without learning frontend frameworks.

How it saves time or tokens

Taipy eliminates the need for separate tools for dashboards (Streamlit/Dash) and pipelines (Airflow/Prefect). One framework handles both the UI and the backend orchestration. You define dashboards as Python objects and pipelines as task graphs, all in the same codebase.

How to use

1. Install Taipy: pip install taipy.
2. Define your data processing tasks as Python functions.
3. Create a GUI with Taipy's markdown-based layout syntax.

Example

import taipy as tp
from taipy.gui import Gui

# Data
data = {'Month': ['Jan', 'Feb', 'Mar'], 'Sales': [100, 150, 200]}
selected_month = 'Jan'

# Dashboard layout (markdown syntax)
page = '''
# Sales Dashboard

<|{selected_month}|selector|lov={data['Month']}|>

<|{data}|chart|x=Month|y=Sales|type=bar|>

Selected: <|{selected_month}|>
'''

Gui(page=page).run()

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Common pitfalls

• Taipy's markdown-based syntax has its own learning curve. The <|...|> syntax for components is not standard markdown and requires reading the component reference.
• For production deployment, use Taipy's built-in multi-user mode. The default single-user mode shares state across all browser sessions.
• Pipeline orchestration in Taipy is simpler than Airflow. If you need complex DAG scheduling, cron triggers, or distributed workers, Airflow may be a better fit.