Streamlit Pydantic

Auto-generate Streamlit UI elements from Pydantic models.

Getting Started • Documentation • Support • Report a Bug • Contribution • Changelog

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.

^{Beta Version: Only suggested for experimental usage.}

Try out and explore various examples in our playground here.

Highlights

🪄 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.

Getting Started

Installation

Requirements: Python 3.6+.

pip install streamlit-pydantic

Usage

Create a script (my_script.py) with a Pydantic model and render it via pydantic_form:

import streamlit as st
from pydantic import BaseModel
import streamlit_pydantic as sp

class ExampleModel(BaseModel):
    some_text: str
    some_number: int
    some_boolean: bool

data = sp.pydantic_form(key="my_form", model=ExampleModel)
if data:
    st.json(data.json())

Run the streamlit server on the python script: streamlit run my_script.py
You can find additional examples in the examples section below.

Examples

👉 Try out and explore these examples in our playground here

The following collection of examples demonstrate how Streamlit Pydantic can be applied in more advanced scenarios. You can find additional - even more advanced - examples in the examples folder or in the playground.

Simple Form

import streamlit as st
from pydantic import BaseModel

import streamlit_pydantic as sp


class ExampleModel(BaseModel):
    some_text: str
    some_number: int
    some_boolean: bool

data = sp.pydantic_form(key="my_form", model=ExampleModel)
if data:
    st.json(data.json())

Date Validation

import streamlit as st
from pydantic import BaseModel, Field, HttpUrl
from pydantic.color import Color

import streamlit_pydantic as sp


class ExampleModel(BaseModel):
    url: HttpUrl
    color: Color
    email: str = Field(..., max_length=100, regex=r"^\S+@\S+$")


data = sp.pydantic_form(key="my_form", model=ExampleModel)
if data:
    st.json(data.json())

Dataclasses Support

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_form", model=ExampleModel)
if data:
    st.json(json.dumps(data, default=pydantic_encoder))

Complex Nested Model

from enum import Enum
from typing import Set

import streamlit as st
from pydantic import BaseModel, Field, ValidationError, parse_obj_as

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(..., description="Unlimited text property")
    integer_in_range: int = Field(
        20,
        ge=10,
        lt=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."
    )
    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.json())

Render Input

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", ExampleModel, use_sidebar=True)

Render Output

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)

Custom Form

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"):
    sp.pydantic_input(key="my_input_model", model=ExampleModel)
    submit_button = st.form_submit_button(label="Submit")

Support & Feedback

Type	Channel
🚨 Bug Reports
🎁 Feature Requests
👩‍💻 Usage Questions
📢 Announcements

Documentation

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.

Contribution

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.

Development

To build the project and run the style/linter checks, execute:

make install
make check

Run make help to see additional commands for development.

Licensed MIT. Created and maintained with ❤️ by developers from Berlin.

lukasmasuch/streamlit-pydantic

lukasmasuch

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