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https://github.com/trubrics/streamlit-feedback
Collect user feedback from within your Streamlit app
https://github.com/trubrics/streamlit-feedback
Last synced: 21 days ago
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Collect user feedback from within your Streamlit app
- Host: GitHub
- URL: https://github.com/trubrics/streamlit-feedback
- Owner: trubrics
- License: mit
- Created: 2023-08-03T09:36:02.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-14T14:35:13.000Z (4 months ago)
- Last Synced: 2024-11-16T04:03:25.191Z (28 days ago)
- Language: JavaScript
- Homepage:
- Size: 239 KB
- Stars: 135
- Watchers: 1
- Forks: 15
- Open Issues: 23
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# streamlit-feedback
A Streamlit component to add **user feedback** to your apps.
```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,
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.
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
```
---
```python
from streamlit_feedback import streamlit_feedback
feedback = streamlit_feedback(feedback_type="faces")
feedback
```
---
```python
from streamlit_feedback import streamlit_feedback
feedback = streamlit_feedback(
feedback_type="faces",
optional_text_label="[Optional] Please provide an explanation",
)
feedback
```
---
```python
from streamlit_feedback import streamlit_feedback
feedback = streamlit_feedback(feedback_type="thumbs", align="flex-start")
feedback
```
## 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).