| Name | wfork-streamlit-profiler JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | (fork of) Runtime profiler for Streamlit, powered by pyinstrument |
| upload_time | 2025-01-29 21:20:36 |
| maintainer | None |
| docs_url | None |
| author | Wyatt S Carpenter |
| requires_python | <4.0,>=3.7 |
| license | MIT |
| keywords |
|
| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# wfork-streamlit-profiler 🏄🏼
[](https://pypi.org/project/wfork-streamlit-profiler/)
**Runtime profiler for Streamlit, powered by [pyinstrument](https://github.com/joerick/pyinstrument).**
*wfork-streamlit-profiler is a fork of streamlit-profiler 0.2.4. If there are any newer versions of the original streamlit-profiler, you should probably use those instead. See the original project's github for more: https://github.com/jrieke/streamlit-profiler*
streamlit-profiler is a [Streamlit component](https://streamlit.io/components) that
helps you find out which parts of your app are slow. It profiles the code via
[pyinstrument](https://github.com/joerick/pyinstrument) and shows the results right
within your Streamlit app.
<sup>Alpha version, use with care.</sup>
---
<h3 align="center">
⏱️ <a href="https://share.streamlit.io/jrieke/streamlit-profiler/main/examples/basic.py">Live demo</a> ⏱️
</h3>
---
<p align="center">
<a href="https://share.streamlit.io/jrieke/streamlit-profiler/main/examples/basic.py"><img src="images/demo.png" width=600></a>
</p>
## Installation
```bash
pip install wfork-streamlit-profiler
```
## Usage
```python
import streamlit as st
from wfork_streamlit_profiler import Profiler
with Profiler():
st.title("My app")
# ... other code
# Or:
# p = Profiler()
# p.start()
# ...
# p.stop()
```
Then start your app as usual: `streamlit run my_app.py`
The `Profiler` class is an extension of `pyinstrument.Profiler`, so you can use
[all of its functions](https://pyinstrument.readthedocs.io/en/latest/reference.html#pyinstrument.Profiler).
Raw data
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"description": "# wfork-streamlit-profiler \ud83c\udfc4\ud83c\udffc\n\n[](https://pypi.org/project/wfork-streamlit-profiler/)\n\n**Runtime profiler for Streamlit, powered by [pyinstrument](https://github.com/joerick/pyinstrument).**\n\n*wfork-streamlit-profiler is a fork of streamlit-profiler 0.2.4. If there are any newer versions of the original streamlit-profiler, you should probably use those instead. See the original project's github for more: https://github.com/jrieke/streamlit-profiler*\n\nstreamlit-profiler is a [Streamlit component](https://streamlit.io/components) that\nhelps you find out which parts of your app are slow. It profiles the code via\n[pyinstrument](https://github.com/joerick/pyinstrument) and shows the results right\nwithin your Streamlit app.\n\n<sup>Alpha version, use with care.</sup>\n\n---\n\n<h3 align=\"center\">\n \u23f1\ufe0f <a href=\"https://share.streamlit.io/jrieke/streamlit-profiler/main/examples/basic.py\">Live demo</a> \u23f1\ufe0f\n</h3>\n\n---\n\n<p align=\"center\">\n <a href=\"https://share.streamlit.io/jrieke/streamlit-profiler/main/examples/basic.py\"><img src=\"images/demo.png\" width=600></a>\n</p>\n\n## Installation\n\n```bash\npip install wfork-streamlit-profiler\n```\n\n## Usage\n\n```python\nimport streamlit as st\nfrom wfork_streamlit_profiler import Profiler\n\nwith Profiler():\n st.title(\"My app\")\n # ... other code\n\n# Or:\n# p = Profiler()\n# p.start()\n# ...\n# p.stop()\n```\n\nThen start your app as usual: `streamlit run my_app.py`\n\nThe `Profiler` class is an extension of `pyinstrument.Profiler`, so you can use\n[all of its functions](https://pyinstrument.readthedocs.io/en/latest/reference.html#pyinstrument.Profiler).\n\n",
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