push new demo
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app.py
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import marimo
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__generated_with = "0.
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app = marimo.App()
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@app.cell
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def
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import marimo as mo
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mo.md("# Welcome to marimo! ππ")
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return (mo,)
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@app.cell
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def
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@app.cell
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def __(mo, slider):
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mo.md(
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f"""
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marimo is a **reactive** Python notebook.
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This means that unlike traditional notebooks, marimo notebooks **run
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automatically** when you modify them or
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interact with UI elements, like this slider: {slider}.
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{"##" + "π" * slider.value}
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"""
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)
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return
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@app.cell(hide_code=True)
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def __(mo):
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mo.accordion(
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{
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"Tip: disabling automatic execution": mo.md(
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rf"""
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marimo lets you disable automatic execution: just go into the
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notebook settings and set
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"Runtime > On Cell Change" to "lazy".
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When the runtime is lazy, after running a cell, marimo marks its
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descendants as stale instead of automatically running them. The
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lazy runtime puts you in control over when cells are run, while
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still giving guarantees about the notebook state.
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"""
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)
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}
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)
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return
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@app.cell(hide_code=True)
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def
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mo.md(
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"""
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by entering `marimo edit` at the command line.
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"""
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).callout()
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return
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"""
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## 1. Reactive execution
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happen.
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"""
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return
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@app.cell(hide_code=True)
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def __(changed, mo):
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(
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mo.md(
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f"""
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**β¨ Nice!** The value of `changed` is now {changed}.
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When you updated the value of the variable `changed`, marimo
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**reacted** by running this cell automatically, because this cell
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references the global variable `changed`.
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Reactivity ensures that your notebook state is always
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consistent, which is crucial for doing good science; it's also what
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enables marimo notebooks to double as tools and apps.
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"""
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)
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if changed
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else mo.md(
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"""
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**π See it in action.** In the next cell, change the value of the
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variable `changed` to `True`, then click the run button.
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"""
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return
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@app.cell
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def
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@app.cell
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def
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"""
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**Global names must be unique.** To enable reactivity, marimo imposes a
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constraint on how names appear in cells: no two cells may define the same
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variable.
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"""
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)
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return
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@app.cell(hide_code=True)
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def __(mo):
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mo.accordion(
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{
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"Tip: encapsulation": (
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"""
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By encapsulating logic in functions, classes, or Python modules,
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you can minimize the number of global variables in your notebook.
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"""
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)
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}
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)
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return
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@app.cell(hide_code=True)
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def __(mo):
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mo.accordion(
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{
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"Tip: private variables": (
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"""
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Variables prefixed with an underscore are "private" to a cell, so
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they can be defined by multiple cells.
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"""
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)
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}
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return
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@app.cell
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def
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Cells can output interactive UI elements. Interacting with a UI
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element **automatically triggers notebook execution**: when
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you interact with a UI element, its value is sent back to Python, and
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every cell that references that element is re-run.
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"""
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return
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@app.cell
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def
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@app.cell
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def
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@app.cell
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def
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return (
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@app.cell
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def
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@app.cell
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def
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@app.cell(hide_code=True)
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def __(mo):
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mo.md(
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"""
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## 3. marimo is just Python
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marimo cells parse Python (and only Python), and marimo notebooks are
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stored as pure Python files β outputs are _not_ included. There's no
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magical syntax.
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The Python files generated by marimo are:
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- usable as Python scripts, with UI elements taking their default
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values, and
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- importable by other modules (more on that in the future).
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"""
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)
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return
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@app.cell(hide_code=True)
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def __(mo):
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mo.md(
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"""
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## 4. Running notebooks as apps
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marimo notebooks can double as apps. Click the app window icon in the
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bottom-right to see this notebook in "app view."
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Serve a notebook as an app with `marimo run` at the command-line.
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Of course, you can use marimo just to level-up your
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notebooking, without ever making apps.
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"""
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)
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return
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@app.cell(hide_code=True)
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def __(mo):
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mo.md(
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"""
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## 5. The `marimo` command-line tool
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**Creating and editing notebooks.** Use
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```
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marimo edit
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```
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marimo run notebook.py
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```
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to start a webserver that serves your notebook as an app in read-only mode,
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with code cells hidden.
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**Convert a Jupyter notebook.** Convert a Jupyter notebook to a marimo
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notebook using `marimo convert`:
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```
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marimo convert your_notebook.ipynb > your_app.py
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```
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**Tutorials.** marimo comes packaged with tutorials:
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- `dataflow`: more on marimo's automatic execution
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- `ui`: how to use UI elements
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- `markdown`: how to write markdown, with interpolated values and
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LaTeX
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- `plots`: how plotting works in marimo
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- `sql`: how to use SQL
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- `layout`: layout elements in marimo
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- `fileformat`: how marimo's file format works
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- `markdown-format`: for using `.md` files in marimo
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- `for-jupyter-users`: if you are coming from Jupyter
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Start a tutorial with `marimo tutorial`; for example,
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```
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marimo tutorial dataflow
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```
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In addition to tutorials, we have examples in our
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[our GitHub repo](https://www.github.com/marimo-team/marimo/tree/main/examples).
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"""
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)
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return
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@app.cell(hide_code=True)
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def __(mo):
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mo.md(
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"""
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## 6. The marimo editor
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Here are some tips to help you get started with the marimo editor.
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"""
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)
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return
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@app.cell
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def
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@app.cell
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def
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"Console Output": (
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"""
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Console output (e.g., `print()` statements) is shown below a
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cell.
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"""
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),
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"Creating, Moving, and Deleting Cells": (
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"""
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1. _Create_ a new cell above or below a given one by clicking
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the plus button to the left of the cell, which appears on
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mouse hover.
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2. _Move_ a cell up or down by dragging on the handle to the
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right of the cell, which appears on mouse hover.
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3. _Delete_ a cell by clicking the trash bin icon. Bring it
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back by clicking the undo button on the bottom right of the
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screen, or with `Ctrl/Cmd+Shift+z`.
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"""
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),
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"Disabling Automatic Execution": (
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"""
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Via the notebook settings (gear icon) or footer panel, you
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can disable automatic execution. This is helpful when
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working with expensive notebooks or notebooks that have
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side-effects like database transactions.
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"""
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),
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"Disabling Cells": (
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"""
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You can disable a cell via the cell context menu.
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marimo will never run a disabled cell or any cells that depend on it.
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This can help prevent accidental execution of expensive computations
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when editing a notebook.
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"""
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),
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"Code Folding": (
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"""
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You can collapse or fold the code in a cell by clicking the arrow
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icons in the line number column to the left, or by using keyboard
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shortcuts.
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Use the command palette (`Ctrl/Cmd+k`) or a keyboard shortcut to
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quickly fold or unfold all cells.
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"""
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),
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"Code Formatting": (
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"""
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If you have [ruff](https://github.com/astral-sh/ruff) installed,
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you can format a cell with the keyboard shortcut `Ctrl/Cmd+b`.
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"""
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),
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"Command Palette": (
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"""
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Use `Ctrl/Cmd+k` to open the command palette.
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"""
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),
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"Keyboard Shortcuts": (
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"""
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Open the notebook menu (top-right) or input `Ctrl/Cmd+Shift+h` to
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view a list of all keyboard shortcuts.
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"""
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),
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"Configuration": (
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"""
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Configure the editor by clicking the gears icon near the top-right
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of the screen.
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"""
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),
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}
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return (tips,)
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if __name__ == "__main__":
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# /// script
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# requires-python = ">=3.12"
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# dependencies = [
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# "groq==0.18.0",
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# "pandas==2.2.3",
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# "marimo",
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# ]
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# ///
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import marimo
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__generated_with = "0.11.6"
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app = marimo.App(width="medium")
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@app.cell
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def _():
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import marimo as mo
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return (mo,)
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@app.cell
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def _(mo):
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groq_api_key = mo.ui.text(label='Enter your groq api key here', kind='password')
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| 25 |
+
groq_api_key
|
| 26 |
+
return (groq_api_key,)
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| 27 |
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| 28 |
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| 29 |
@app.cell(hide_code=True)
|
| 30 |
+
def _(mo):
|
| 31 |
mo.md(
|
| 32 |
+
r"""
|
| 33 |
+
# LLM for NER
|
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|
| 34 |
|
| 35 |
+
- do a [Few-shot prompting](https://huggingface.co/docs/transformers/main/en/tasks/prompting#few-shot-prompting) and repeat multiple times.
|
| 36 |
|
| 37 |
+
The default prompt is:
|
| 38 |
+
```python
|
| 39 |
+
Return a list of named entities in the text with your confidence score on a scale of 0 to 1 for this tag.
|
| 40 |
+
The available entities are: "ADE", "Dosage", "Drug", "Duration", "Form", "Frequency", "Reason", "Route" and "Strength".
|
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|
| 41 |
|
| 42 |
+
For example:
|
| 43 |
+
Text: MEDICATIONS : Lipitor , Tylenol with Codeine , Dilantin , previously on Decadron q.i.d .
|
| 44 |
+
Named Entities: <start> Lipitor (Drug: 0.87), Tylenol (Drug: 0.59) <end>
|
| 45 |
|
| 46 |
+
Here is your task:
|
| 47 |
+
Text: The patient then developed oral sores and rash in the chest the night before admission which rapidly spread to the face , trunk , and upper extremities within the last 24 hours.
|
| 48 |
+
Named Entities:
|
| 49 |
|
| 50 |
+
Remember to answer in the exact form of the example.
|
| 51 |
+
```
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|
| 52 |
"""
|
| 53 |
)
|
| 54 |
return
|
| 55 |
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| 56 |
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|
| 57 |
@app.cell
|
| 58 |
+
def _(mo, models):
|
| 59 |
+
# define some params
|
| 60 |
+
call_groq_times = mo.ui.slider(10, 100, 10, 30, label='How many times do you want to call groq')
|
| 61 |
+
model_ner = mo.ui.dropdown(options=models, value="llama3-8b-8192", label="Choose a LLM")
|
| 62 |
+
ner_text = mo.ui.text_area(value="", label='Type your text here or leave it to default:')
|
| 63 |
+
ner_tags = mo.ui.text_area(value="", label="Type the ner tags here or leave it to default:", placeholder='e.g. Country, Person')
|
| 64 |
+
checkbox_ner = mo.ui.checkbox(label=' Whether to call groq api')
|
| 65 |
+
mo.vstack([mo.md("# Experiment"), mo.hstack([call_groq_times, model_ner]), mo.hstack([ner_text, ner_tags]), checkbox_ner], align='center')
|
| 66 |
+
return call_groq_times, checkbox_ner, model_ner, ner_tags, ner_text
|
| 67 |
|
| 68 |
|
| 69 |
+
@app.cell
|
| 70 |
+
def _(
|
| 71 |
+
call_groq_times,
|
| 72 |
+
chat_completion,
|
| 73 |
+
checkbox_ner,
|
| 74 |
+
client,
|
| 75 |
+
extract_ner_from_assistant,
|
| 76 |
+
mo,
|
| 77 |
+
model_ner,
|
| 78 |
+
prompt_ner,
|
| 79 |
+
):
|
| 80 |
+
# calling groq
|
| 81 |
+
result = []
|
| 82 |
+
if checkbox_ner.value:
|
| 83 |
+
for _ in mo.status.progress_bar(range(call_groq_times.value), title='In Progress β¦', completion_title='Finished.'):
|
| 84 |
+
try:
|
| 85 |
+
answer = chat_completion(client, prompt_ner, model_ner.value)
|
| 86 |
+
result += extract_ner_from_assistant(answer)
|
| 87 |
+
except Exception:
|
| 88 |
+
pass
|
| 89 |
+
return answer, result
|
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|
| 90 |
|
| 91 |
|
| 92 |
+
@app.cell
|
| 93 |
+
def _(mo, pd, result):
|
| 94 |
+
# transform data
|
| 95 |
+
data = pd.DataFrame.from_dict(result)
|
| 96 |
+
# data.to_csv('data.csv')
|
| 97 |
+
# data = pl.from_dicts(result)
|
| 98 |
+
# data.write_csv('data.csv')
|
| 99 |
+
try:
|
| 100 |
+
transformed_df = mo.ui.dataframe(data)
|
| 101 |
+
except Exception:
|
| 102 |
+
df = pd.read_csv('data.csv')
|
| 103 |
+
transformed_df = mo.ui.dataframe(df)
|
| 104 |
+
return data, df, transformed_df
|
| 105 |
|
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|
| 106 |
|
| 107 |
+
@app.cell
|
| 108 |
+
def _(mo, transformed_df):
|
| 109 |
+
_md = mo.md(
|
| 110 |
+
r"""
|
| 111 |
+
The results are shown below, use __+ Add__ to apply different transforms and explore more:
|
| 112 |
+
------
|
| 113 |
"""
|
| 114 |
)
|
| 115 |
+
mo.vstack([_md, transformed_df])
|
| 116 |
return
|
| 117 |
|
| 118 |
|
| 119 |
@app.cell
|
| 120 |
+
async def _():
|
| 121 |
+
import os
|
| 122 |
+
import pandas as pd
|
| 123 |
+
from functools import reduce
|
| 124 |
+
import micropip
|
| 125 |
+
await micropip.install("ssl")
|
| 126 |
+
await micropip.install("groq")
|
| 127 |
+
from groq import Groq
|
| 128 |
+
return Groq, micropip, os, pd, reduce
|
| 129 |
|
| 130 |
|
| 131 |
@app.cell
|
| 132 |
+
def _():
|
| 133 |
+
# availabel models on groq
|
| 134 |
+
models = [
|
| 135 |
+
"llama3-8b-8192",
|
| 136 |
+
"llama3-70b-8192",
|
| 137 |
+
"llama2-70b-4096",
|
| 138 |
+
"mixtral-8x7b-32768",
|
| 139 |
+
"gemma-7b-it",
|
| 140 |
+
]
|
| 141 |
+
return (models,)
|
| 142 |
|
| 143 |
|
| 144 |
@app.cell
|
| 145 |
+
def _(Groq, groq_api_key):
|
| 146 |
+
client = Groq(api_key=groq_api_key.value)
|
| 147 |
+
return (client,)
|
| 148 |
|
| 149 |
|
| 150 |
@app.cell
|
| 151 |
+
def _():
|
| 152 |
+
default_sentence = "The patient then developed oral sores and rash in the chest the night before admission which rapidly spread to the face , trunk , and upper extremities within the last 24 hours."
|
| 153 |
+
default_tags = ["ADE", "Dosage", "Drug", "Duration", "Form", "Frequency", "Reason", "Route" and "Strength"]
|
| 154 |
+
return default_sentence, default_tags
|
| 155 |
|
| 156 |
|
| 157 |
@app.cell
|
| 158 |
+
def _(default_sentence, default_tags, ner_tags, ner_text):
|
| 159 |
+
prompt_ner = fr"""Return a list of named entities in the text with your confidence score on a scale of 0 to 1 for this tag.
|
| 160 |
+
The available entities are: {ner_tags.value.split(',') if ner_tags.value else default_tags}.
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 161 |
|
| 162 |
+
For example:
|
| 163 |
+
Text: MEDICATIONS : Lipitor , Tylenol with Codeine , Dilantin , previously on Decadron q.i.d .
|
| 164 |
+
Named Entities: <start> Lipitor (Drug: 0.87), Tylenol (Drug: 0.59) <end>
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
| 165 |
|
| 166 |
+
Here is your task:
|
| 167 |
+
Text: {ner_text.value if ner_text.value else default_sentence}
|
| 168 |
+
Named Entities:
|
| 169 |
|
| 170 |
+
Remember to answer in the exact form of the example.
|
| 171 |
+
"""
|
| 172 |
+
prompt_ner
|
| 173 |
+
return (prompt_ner,)
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
|
|
|
| 174 |
|
| 175 |
|
| 176 |
@app.cell
|
| 177 |
+
def _():
|
| 178 |
+
def chat_completion(client, prompt, model):
|
| 179 |
+
completion = client.chat.completions.create(
|
| 180 |
+
messages=[
|
| 181 |
+
{
|
| 182 |
+
"role": "system",
|
| 183 |
+
"content": "you will help me with some NER tasks."
|
| 184 |
+
},
|
| 185 |
+
# set a user message for the assistant to respond to.
|
| 186 |
+
{
|
| 187 |
+
"role": "user",
|
| 188 |
+
"content": prompt,
|
| 189 |
+
}
|
| 190 |
+
],
|
| 191 |
+
# The language model which will generate the completion.
|
| 192 |
+
model=model,
|
| 193 |
+
temperature=0.5,
|
| 194 |
+
max_tokens=100,
|
| 195 |
+
top_p=1,
|
| 196 |
+
stop='<end>',
|
| 197 |
+
# If set, partial message deltas will be sent.
|
| 198 |
+
stream=False,
|
| 199 |
+
)
|
| 200 |
+
answer = completion.choices[0].message.content
|
| 201 |
+
return answer
|
| 202 |
+
return (chat_completion,)
|
| 203 |
|
| 204 |
|
| 205 |
+
@app.cell
|
| 206 |
+
def _(reduce):
|
| 207 |
+
def extract_ner_from_assistant(answer: str) -> list[dict]:
|
| 208 |
+
# initialize a generator
|
| 209 |
+
tokens = (token for token in answer.split())
|
| 210 |
+
# iterate through tokens until <start>
|
| 211 |
+
for token in tokens:
|
| 212 |
+
if token == "<start>":
|
| 213 |
+
break
|
| 214 |
+
# e.g. ['oral sores (ADE: 0.98)', 'rash (ADE: 0.98)']
|
| 215 |
+
records = " ".join(list(tokens)).split(",")
|
| 216 |
+
# clean data
|
| 217 |
+
result = map(
|
| 218 |
+
lambda record: reduce(
|
| 219 |
+
lambda acc, elem: {**acc, **{elem[0]: elem[1]}},
|
| 220 |
+
zip(
|
| 221 |
+
["named entity", "tag", "score"],
|
| 222 |
+
[
|
| 223 |
+
" ".join(record.split()[:-2]),
|
| 224 |
+
str(record.split()[-2])[1:-1],
|
| 225 |
+
float(str(record.split()[-1])[:-1]),
|
| 226 |
+
],
|
| 227 |
+
),
|
| 228 |
+
{}, ## initial value of accumulator
|
| 229 |
+
),
|
| 230 |
+
records,
|
| 231 |
+
)
|
| 232 |
+
return list(result)
|
| 233 |
+
return (extract_ner_from_assistant,)
|
|
|
|
|
|
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|
| 234 |
|
| 235 |
|
| 236 |
if __name__ == "__main__":
|