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

1. 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())
   

2. Run the streamlit server on the python script: streamlit run my_script.py

3. 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.