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https://github.com/lukasmasuch/streamlit-pydantic

🪄 Auto-generate Streamlit UI from Pydantic Models and Dataclasses.
https://github.com/lukasmasuch/streamlit-pydantic

dataclasses forms json-schema pydantic python streamlit streamlit-component streamlit-library type-hints typing

Last synced: about 15 hours ago
JSON representation

🪄 Auto-generate Streamlit UI from Pydantic Models and Dataclasses.

Host: GitHub
URL: https://github.com/lukasmasuch/streamlit-pydantic
Owner: lukasmasuch
License: mit
Created: 2021-07-09T23:03:31.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2024-08-18T18:31:02.000Z (about 1 month ago)
Last Synced: 2024-09-24T13:33:52.382Z (3 days ago)
Topics: dataclasses, forms, json-schema, pydantic, python, streamlit, streamlit-component, streamlit-library, type-hints, typing
Language: Python
Homepage: https://st-pydantic.streamlit.app
Size: 469 KB
Stars: 415
Watchers: 8
Forks: 56
Open Issues: 31
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Codeowners: .github/CODEOWNERS
- Support: .github/SUPPORT.md

Awesome Lists containing this project

README

        


    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](https://github.com/samuelcolvin/pydantic/) models or [dataclasses](https://docs.python.org/3/library/dataclasses.html). 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.



---

## 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

```bash

pip install streamlit-pydantic

```

### Usage

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

    ```python

    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())

    ```

2. Run the Streamlit server on the Python script: `streamlit run my_script.py`

3. You can find additional examples in the [examples](#examples) section below.

## Examples

---



     👉  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](./examples) or on the [playground](https://st-pydantic.streamlit.app/).

### Simple Form

```python

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())

```

### Date Validation

```python

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())

```

### Dataclasses Support

```python

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))

```

### Complex Nested Model

```python

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())

```

### Render Input

```python

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"

)

```

### Render Output

```python

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

```python

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())

```

## Support & Feedback

| Type                     | Channel                                              |

| ------------------------ | ------------------------------------------------------ |

| 🐛  **Bug Reports**       |                                  |

| ✨  **Feature Requests**  |                                  |

| 👩‍💻  **Usage Questions**   |    |

| 📢  **Announcements**  |  |


## Documentation

The API documentation can be found [here](./docs). To generate UI elements, you can use the high-level [`pydantic_form`](./docs/streamlit_pydantic.ui_renderer.md#function-pydantic_form) method. Or the more flexible lower-level [`pydantic_input`](./docs/streamlit_pydantic.ui_renderer.md#function-pydantic_input) and [`pydantic_output`](./docs/streamlit_pydantic.ui_renderer.md#function-pydantic_output) methods. See the [examples](#examples) section on how to use those methods.

## Contribution

- Pull requests are encouraged and always welcome. Read our [contribution guidelines](https://github.com/lukasmasuch/streamlit-pydantic/tree/main/CONTRIBUTING.md) and check out [help-wanted](https://github.com/lukasmasuch/streamlit-pydantic/issues?utf8=%E2%9C%93&q=is%3Aopen+is%3Aissue+label%3A"help+wanted"+sort%3Areactions-%2B1-desc+) issues.

- Submit Github issues for any [feature request and enhancement](https://github.com/lukasmasuch/streamlit-pydantic/issues/new?assignees=&labels=type%3Aenhancement%2Cstatus%3Aneeds-triage&projects=&template=02_feature-request.yml), [bugs](https://github.com/lukasmasuch/streamlit-pydantic/issues/new?assignees=&labels=type%3Abug%2Cstatus%3Aneeds-triage&projects=&template=01_bug-report.yml), or [documentation](https://github.com/lukasmasuch/streamlit-pydantic/issues/new?assignees=&labels=type%3Adocs%2Cstatus%3Aneeds-triage&projects=&template=03_documentation.yml) problems.

- By participating in this project, you agree to abide by its [Code of Conduct](https://github.com/lukasmasuch/streamlit-pydantic/blob/main/.github/CODE_OF_CONDUCT.md).

- The [development section](#development) below contains information on how to build and test the project after you have implemented some changes.

## Development



  

  

  

  



This repo uses [Rye](https://rye.astral.sh/) for development. To get started, [install Rye](https://rye.astral.sh/) and sync the project:

```bash

rye sync

```

Run the playground app:

```bash

rye run playground

```

Run linting and type checks:

```bash

rye run checks

```

> [!TIP]

> The linting and formatting is using [ruff](https://github.com/astral-sh/ruff) and

> type-checking is done with [mypy](https://github.com/python/mypy). You can use

> the ruff and mypy extensions of your IDE to automatically run these checks

> during development.

Format the code:

```bash

rye run format

```

Run tests:

```bash

rye test

```

---

Licensed **MIT**.