Auto-generate Streamlit UI elements from Pydantic models.
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Streamlit-pydantic makes it easy to auto-generate UI elements from Pydantic models or dataclasses. Just define your data model and turn it into a full-fledged UI form. It supports data validation, nested models, and field limitations. Streamlit-pydantic can be easily integrated into any Streamlit app.
Try out and explore various examples in our playground here.
- 🪄 Auto-generated UI elements from Pydantic models & Dataclasses.
- 📇 Out-of-the-box data validation.
- 📑 Supports nested Pydantic models.
- 📏 Supports field limits and customizations.
- 🎈 Easy to integrate into any Streamlit app.
pip install streamlit-pydantic-
Create a script (
my_script.py) with a Pydantic model and render it viapydantic_form:import streamlit as st import streamlit_pydantic as sp from pydantic import BaseModel class ExampleModel(BaseModel): some_text: str some_number: int some_boolean: bool data = sp.pydantic_form(key="my_sample_form", model=ExampleModel) if data: st.json(data.model_dump())
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Run the Streamlit server on the Python script:
streamlit run my_script.py -
You can find additional examples in the examples section below.
👉 Try out and explore these examples in our playground here
The following collection of examples demonstrates how Streamlit Pydantic can be applied in more advanced scenarios. You can find additional - even more advanced - examples in the examples folder or on the playground.
import streamlit as st import streamlit_pydantic as sp from pydantic import BaseModel class ExampleModel(BaseModel): some_text: str some_number: int some_boolean: bool data = sp.pydantic_form(key="my_sample_form", model=ExampleModel) if data: st.json(data.model_dump())import streamlit as st import streamlit_pydantic as sp from pydantic import BaseModel, Field, HttpUrl from pydantic_extra_types.color import Color class ExampleModel(BaseModel): url: HttpUrl color: Color = Field("blue", format="text") email: str = Field(..., max_length=100, regex=r"^\S+@\S+$") data = sp.pydantic_form(key="my_form", model=ExampleModel) if data: st.json(data.model_dump_json())import dataclasses import json import streamlit as st from pydantic.json import pydantic_encoder import streamlit_pydantic as sp @dataclasses.dataclass class ExampleModel: some_number: int some_boolean: bool some_text: str = "default input" data = sp.pydantic_form(key="my_dataclass_form", model=ExampleModel) if data: st.json(dataclasses.asdict(data))from enum import Enum from typing import Set import streamlit as st from pydantic import BaseModel, Field import streamlit_pydantic as sp class OtherData(BaseModel): text: str integer: int class SelectionValue(str, Enum): FOO = "foo" BAR = "bar" class ExampleModel(BaseModel): long_text: str = Field( ..., format="multi-line", description="Unlimited text property" ) integer_in_range: int = Field( 20, ge=10, le=30, multiple_of=2, description="Number property with a limited range.", ) single_selection: SelectionValue = Field( ..., description="Only select a single item from a set." ) multi_selection: Set[SelectionValue] = Field( ..., description="Allows multiple items from a set." ) read_only_text: str = Field( "Lorem ipsum dolor sit amet", description="This is a ready only text.", readOnly=True, ) single_object: OtherData = Field( ..., description="Another object embedded into this model.", ) data = sp.pydantic_form(key="my_form", model=ExampleModel) if data: st.json(data.model_dump_json())from pydantic import BaseModel import streamlit_pydantic as sp class ExampleModel(BaseModel): some_text: str some_number: int = 10 # Optional some_boolean: bool = True # Option input_data = sp.pydantic_input( "model_input", model=ExampleModel, group_optional_fields="sidebar" )import datetime from pydantic import BaseModel, Field import streamlit_pydantic as sp class ExampleModel(BaseModel): text: str = Field(..., description="A text property") integer: int = Field(..., description="An integer property.") date: datetime.date = Field(..., description="A date.") instance = ExampleModel(text="Some text", integer=40, date=datetime.date.today()) sp.pydantic_output(instance)import streamlit as st from pydantic import BaseModel import streamlit_pydantic as sp class ExampleModel(BaseModel): some_text: str some_number: int = 10 some_boolean: bool = True with st.form(key="pydantic_form"): data = sp.pydantic_input(key="my_custom_form_model", model=ExampleModel) submit_button = st.form_submit_button(label="Submit") obj = ExampleModel(data) if data: st.json(obj.model_dump())| Type | Channel |
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The API documentation can be found here. To generate UI elements, you can use the high-level pydantic_form method. Or the more flexible lower-level pydantic_input and pydantic_output methods. See the examples section on how to use those methods.
- Pull requests are encouraged and always welcome. Read our contribution guidelines and check out help-wanted issues.
- Submit Github issues for any feature request and enhancement, bugs, or documentation problems.
- By participating in this project, you agree to abide by its Code of Conduct.
- The development section below contains information on how to build and test the project after you have implemented some changes.
This repo uses Rye for development. To get started, install Rye and sync the project:
rye syncRun the playground app:
rye run playgroundRun linting and type checks:
rye run checksTip
The linting and formatting is using ruff and type-checking is done with mypy. You can use the ruff and mypy extensions of your IDE to automatically run these checks during development.
Format the code:
rye run formatRun tests:
rye testLicensed MIT.