streamlit-feedback 0.1.4
Streamlit component that allows you to collect user feedback in your apps
Requires Python: >=3.7

# streamlit-feedback

A Streamlit component to add **user feedback** to your apps.

<img src="./assets/thumbs.png"  width="600">

```python
from streamlit_feedback import streamlit_feedback
feedback = streamlit_feedback(feedback_type="thumbs")
feedback
```

## Examples

[Here](streamlit_feedback/examples.py) are many examples of how the feedback component can be added to your app. Each function represents a different app.

> [!IMPORTANT]  
> The `streamlit_feedback` component triggers a page reload when submitted, this is how streamlit components work. The `on_submit` function is only then run when your app reaches the `streamlit-feedback()` call on the rerun.


## Install

```sh
pip install streamlit-feedback
```

## Usage
It can be used with these parameters:

```python
def streamlit_feedback(
    feedback_type,
    optional_text_label=None,
    max_text_length=None,
    review_on_positive=True,
    disable_with_score=None,
    on_submit=None,
    args=(),
    kwargs={},
    align="flex-end",
    key=None,
):
    """Create a new instance of "streamlit_feedback".

    Parameters
    ----------
    feedback_type: str
        The type of feedback; "thumbs" or "faces".
    optional_text_label: str or None
        An optional label to add as a placeholder to the textbox.
        If None, the "thumbs" or "faces" will not be accompanied by textual feedback.
    max_text_length: int or None
        Defaults to None. If set, enables the multi-line functionality and determines the maximum characters the textbox allows. Else, displays the default one-line textbox.
    disable_with_score: str
        An optional score to disable the component. Must be a "thumbs" emoji or a "faces" emoji. Can be used to pass state from one component to another.
    review_on_positive: bool
        Defaults to True. When set to False, it only asks for textual feedback in case of a negative review, i.e., "thumbs down" or less than the happiest "faces" emoji.
    on_submit: callable
        An optional callback invoked when feedback is submitted. This function must accept at least one argument, the feedback response dict,
        allowing you to save the feedback to a database for example. Additional arguments can be specified using `args` and `kwargs`.
    args: tuple
        Additional positional arguments to pass to `on_submit`.
    kwargs: dict
        Additional keyword arguments to pass to `on_submit`.
    align: str
        Where to align the feedback component; "flex-end", "center" or "flex-start".
    key: str or None
        An optional key that uniquely identifies this component. If this is
        None, and the component's arguments are changed, the component will
        be re-mounted in the Streamlit frontend and lose its current state.

    Returns
    -------
    dict
        The user response, with the feedback_type, score and text fields. If on_submit returns a value, this value will be returned by the component.

    """
```

Here are some more examples:

```python
from streamlit_feedback import streamlit_feedback
feedback = streamlit_feedback(
    feedback_type="thumbs",
    optional_text_label="[Optional] Please provide an explanation",
)
feedback
```

<img src="./assets/thumbsandtext.png"  width="600">

---

```python
from streamlit_feedback import streamlit_feedback
feedback = streamlit_feedback(feedback_type="faces")
feedback
```

<img src="./assets/faces.png"  width="600">

---

```python
from streamlit_feedback import streamlit_feedback
feedback = streamlit_feedback(
    feedback_type="faces",
    optional_text_label="[Optional] Please provide an explanation",
)
feedback
```

<img src="./assets/facesandtext.png"  width="600">

---

```python
from streamlit_feedback import streamlit_feedback
feedback = streamlit_feedback(feedback_type="thumbs", align="flex-start")
feedback
```

<img src="./assets/thumbsstart.png"  width="600">

## Contributing

We welcome all contributions. To test & run the streamlit-feedback component locally, you will need to run both the frontend javascript component and the streamlit server.

1. Run the component frontend

```
cd streamlit_feedback/frontend && npm run start
```

2. Run the server

```python
streamlit run streamlit_feedback/__init__.py
```

See more details in [CONTRIBUTING.md](CONTRIBUTING.md).