Taipy: Full-Stack Framework for Building Production-Ready AI Web Applications
Building production-ready AI applications often requires a combination of various tools: a frontend framework for UI, a backend for business logic, and pipeline orchestration for data processing. Taipy offers an all-in-one solution that enables data scientists and ML engineers to build complete AI web applications using only Python.
In this tutorial, we will explore Taipy in depth, from installation, GUI building, pipeline orchestration, to deploying a production-ready ML dashboard application.
What Is Taipy?
Taipy is an open-source Python framework designed specifically for building data and AI applications. Unlike Streamlit or Gradio, which focus on prototyping, Taipy is designed for production-grade applications with comprehensive features:
- Taipy GUI: Build interactive web interfaces with visual elements
- Taipy Core: Manage data pipelines, scenarios, and task scheduling
- Multi-page support: Multi-page applications with routing
- State management: Robust state management for complex applications
- Scalability: Support for concurrent users and heavy workloads
Installation and Setup
Installing Taipy
pip install taipy
For installation with additional dependencies:
# With PostgreSQL support
pip install taipy[postgres]
With all extras
pip install taipy[all]
Verify installation
python -c "import taipy; print(taipy.version)"
Project Structure
taipy-ml-dashboard/
├── main.py
├── config.py
├── pages/
│ ├── home.py
│ ├── dashboard.py
│ ├── predictions.py
│ └── settings.py
├── algorithms/
│ ├── preprocessing.py
│ ├── training.py
│ └── inference.py
├── data/
│ └── dataset.csv
└── requirements.txt
Taipy GUI: Building Web Interfaces
Basic Visual Elements
Taipy uses a simple Markdown-like syntax to define UI components:
from taipy.gui import Gui
State variables
name = "Data Scientist"
message = "Welcome to Taipy!"
Page definition with Markdown syntax
page = """
AI Dashboard Application
Hello, <|{name}|>!
<|{message}|text|>
<|Click Me|button|onaction=onbuttonclick|>
"""
def onbuttonclick(state):
state.message = f"Hello {state.name}! Dashboard is ready to use."
if name == "main":
Gui(page=page).run(title="AI Dashboard", port=5000)
Input and Form Elements
from taipy.gui import Gui
State variables
username = ""
age = 25
role = "Data Scientist"
roles = ["Data Scientist", "ML Engineer", "Data Analyst", "AI Researcher"]
isactive = True
page = """
AI Team Registration Form
<|{username}|input|label=Full Name|>
<|{age}|slider|min=18|max=65|label=Age|>
<|{role}|selector|lov={roles}|dropdown|label=Select Role|>
<|{isactive}|toggle|label=Active Status|>
<|Submit|button|onaction=onsubmit|>
Input Summary:
- Name: <|{username}|text|>
- Age: <|{age}|text|>
- Role: <|{role}|text|>
- Active: <|{isactive}|text|>
"""
def onsubmit(state):
print(f"Registered: {state.username}, {state.age}, {state.role}")
Gui(page=page).run()
State Management
Taipy has a powerful state management system. Each user session has an independent state:
from taipy.gui import Gui, State
counter = 0
history = []
def onincrement(state: State):
state.counter += 1
state.history = state.history + [f"Counter: {state.counter}"]
def onreset(state: State):
state.counter = 0
state.history = []
def oninit(state: State):
"""Called when a new user opens the application"""
state.counter = 0
state.history = []
page = """
State Management Demo
Counter: <|{counter}|text|>
<|Increment|button|onaction=onincrement|>
<|Reset|button|onaction=onreset|>