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Parent(s):
68b221c
add model tree example
Browse files- .gitignore +2 -0
- README.md +3 -3
- app.py +69 -422
- requirements.txt +43 -5
.gitignore
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venv/
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*.pyc
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README.md
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---
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title:
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emoji:
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colorFrom: indigo
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colorTo: purple
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sdk: docker
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pinned: true
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license: mit
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short_description:
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---
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Check out marimo at <https://github.com/marimo-team/marimo>
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---
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title: Hub Model Tree Stats
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emoji: π΄
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colorFrom: indigo
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colorTo: purple
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sdk: docker
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pinned: true
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license: mit
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short_description: Get aggregated stats about derived models for an organization or author.
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---
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Check out marimo at <https://github.com/marimo-team/marimo>
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app.py
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import marimo
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__generated_with = "0.9.
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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("#
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return
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@app.cell
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def __(mo):
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slider = mo.ui.slider(1, 22)
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return (slider,)
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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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-
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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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-
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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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-
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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 __(mo):
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mo.md(
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"""
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Tip: This is a tutorial notebook. You can create your own notebooks
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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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@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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## 1. Reactive execution
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-
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A marimo notebook is made up of small blocks of Python code called
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cells.
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-
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marimo reads your cells and models the dependencies among them: whenever
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a cell that defines a global variable is run, marimo
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**automatically runs** all cells that reference that variable.
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-
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Reactivity keeps your program state and outputs in sync with your code,
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making for a dynamic programming environment that prevents bugs before they
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happen.
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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 __(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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-
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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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)
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)
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return
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@app.cell
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def __():
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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.
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{
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"Tip: execution order": (
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"""
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The order of cells on the page has no bearing on
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the order in which cells are executed: marimo knows that a cell
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reading a variable must run after the cell that defines it. This
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frees you to organize your code in the way that makes the most
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sense for you.
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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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)
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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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)
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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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## 2. UI elements
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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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marimo provides a library of UI elements to choose from under
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`marimo.ui`.
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"""
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)
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return
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@app.cell
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def __(mo):
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mo.md("""**π Some UI elements.** Try interacting with the below elements.""")
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return
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@app.cell
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def __(mo):
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return (icon,)
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@app.cell
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def __(icon, mo):
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repetitions = mo.ui.slider(1, 16, label=f"number of {icon.value}: ")
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return (repetitions,)
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@app.cell
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def __(icon, repetitions):
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icon, repetitions
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return
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@app.cell
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def __(icon, mo, repetitions):
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mo.md("# " + icon.value * repetitions.value)
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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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## 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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- easily versioned with git, yielding minimal diffs
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- legible for both humans and machines
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- formattable using your tool of choice,
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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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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 __(
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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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in a terminal to start the marimo notebook server. From here
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you can create a new notebook or edit existing ones.
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**Running as apps.** Use
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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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- `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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"""
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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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def __(mo, tips):
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mo.accordion(tips)
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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("""## Finally, a fun fact""")
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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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The name "marimo" is a reference to a type of algae that, under
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the right conditions, clumps together to form a small sphere
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called a "marimo moss ball". Made of just strands of algae, these
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beloved assemblages are greater than the sum of their parts.
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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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tips = {
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"Saving": (
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"""
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**Saving**
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- _Name_ your app using the box at the top of the screen, or
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with `Ctrl/Cmd+s`. You can also create a named app at the
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command line, e.g., `marimo edit app_name.py`.
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-
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- _Save_ by clicking the save icon on the bottom right, or by
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inputting `Ctrl/Cmd+s`. By default marimo is configured
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to autosave.
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"""
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),
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"Running": (
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"""
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1. _Run a cell_ by clicking the play ( β· ) button on the top
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right of a cell, or by inputting `Ctrl/Cmd+Enter`.
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2. _Run a stale cell_ by clicking the yellow run button on the
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right of the cell, or by inputting `Ctrl/Cmd+Enter`. A cell is
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stale when its code has been modified but not run.
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3. _Run all stale cells_ by clicking the play ( β· ) button on
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the bottom right of the screen, or input `Ctrl/Cmd+Shift+r`.
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"""
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),
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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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461 |
-
Configure the editor by clicking the gears icon near the top-right
|
462 |
-
of the screen.
|
463 |
-
"""
|
464 |
-
),
|
465 |
-
}
|
466 |
-
return (tips,)
|
467 |
-
|
468 |
-
|
469 |
if __name__ == "__main__":
|
470 |
app.run()
|
|
|
1 |
import marimo
|
2 |
|
3 |
+
__generated_with = "0.9.14"
|
4 |
+
app = marimo.App(width="medium")
|
5 |
|
6 |
|
7 |
+
@app.cell(hide_code=True)
|
8 |
def __():
|
9 |
import marimo as mo
|
10 |
+
import duckdb
|
11 |
+
import pandas
|
12 |
+
import numpy
|
13 |
+
import altair as alt
|
14 |
+
import plotly.express as px
|
15 |
|
16 |
+
mo.md("# π€ Hub Model Tree")
|
17 |
+
return alt, duckdb, mo, numpy, pandas, px
|
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|
18 |
|
19 |
|
20 |
@app.cell(hide_code=True)
|
21 |
def __(mo):
|
22 |
+
mo.md("""This is powered by the [Hub Stats](https://huggingface.co/datasets/cfahlgren1/hub-stats) dataset which you can query via the [SQL Console](https://huggingface.co/datasets/cfahlgren1/hub-stats?sql_console=true). The model tree metric is where a model tags a parent model as a `base_model`. The `hub-stats` dataset gets updated daily. Try it out by putting an organization or model author in search box and hit enter.""")
|
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|
23 |
return
|
24 |
|
25 |
|
26 |
@app.cell
|
27 |
+
def __(duckdb):
|
28 |
+
duckdb.sql("CREATE VIEW models as SELECT * FROM '~/Downloads/models.parquet'")
|
29 |
+
return (models,)
|
30 |
|
31 |
|
32 |
@app.cell(hide_code=True)
|
33 |
def __(mo):
|
34 |
+
author_input = mo.ui.text(placeholder="Search...", label="Author")
|
|
|
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|
|
|
35 |
|
36 |
+
ctes = """
|
37 |
+
WITH author_models AS (
|
38 |
+
SELECT id
|
39 |
+
FROM models
|
40 |
+
WHERE author = '{}'
|
41 |
+
),
|
42 |
+
model_tags AS (
|
43 |
+
SELECT
|
44 |
+
id,
|
45 |
+
UNNEST(tags) AS tag
|
46 |
+
FROM models
|
47 |
)
|
48 |
+
"""
|
49 |
+
def get_model_children_counts(author: str) -> str:
|
50 |
+
return f"""
|
51 |
+
{ctes.format(author)}
|
52 |
+
SELECT
|
53 |
+
am.id as parent_model_id,
|
54 |
+
COUNT(DISTINCT m.id) as num_direct_children
|
55 |
+
FROM author_models am
|
56 |
+
INNER JOIN model_tags m
|
57 |
+
ON m.tag = 'base_model:' || am.id
|
58 |
+
GROUP BY am.id
|
59 |
+
ORDER BY num_direct_children DESC;
|
60 |
+
"""
|
61 |
+
|
62 |
+
def get_total_childen_count(author: str) -> str:
|
63 |
+
return f"""
|
64 |
+
{ctes.format(author)}
|
65 |
+
SELECT
|
66 |
+
COUNT(DISTINCT m.id) as num_direct_children
|
67 |
+
FROM author_models am
|
68 |
+
LEFT JOIN model_tags m
|
69 |
+
ON m.tag = 'base_model:' || am.id
|
70 |
+
"""
|
71 |
+
return (
|
72 |
+
author_input,
|
73 |
+
ctes,
|
74 |
+
get_model_children_counts,
|
75 |
+
get_total_childen_count,
|
76 |
)
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
77 |
|
78 |
|
79 |
@app.cell
|
80 |
def __(mo):
|
81 |
+
mo.md("## Search by Author")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
82 |
return
|
83 |
|
84 |
|
85 |
@app.cell(hide_code=True)
|
86 |
+
def __(author_input, mo):
|
87 |
+
mo.vstack([author_input, mo.md("_ex: meta-llama, google, mistralai, Qwen_")])
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
88 |
return
|
89 |
|
90 |
|
91 |
@app.cell(hide_code=True)
|
92 |
+
def __(author_input, duckdb, get_total_childen_count, mo):
|
93 |
+
result = duckdb.sql(get_total_childen_count(author_input.value)).fetchall()
|
94 |
+
mo.vstack([mo.md("### Direct Child Models"), mo.md(f"_The number of models that have tagged a {author_input.value} model as a `base_model`_"), mo.stat(result[0][0])])
|
95 |
+
return (result,)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
96 |
|
97 |
|
98 |
@app.cell(hide_code=True)
|
99 |
+
def __(author_input, duckdb, get_model_children_counts):
|
100 |
+
df = duckdb.sql(get_model_children_counts(author_input.value)).fetchdf()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
101 |
|
102 |
+
df
|
103 |
+
return (df,)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
104 |
|
105 |
|
106 |
@app.cell(hide_code=True)
|
107 |
+
def __(df, mo, px):
|
108 |
+
_plot = px.bar(
|
109 |
+
df, x="parent_model_id", y="num_direct_children", log_y=True
|
|
|
|
|
|
|
|
|
110 |
)
|
|
|
|
|
111 |
|
112 |
+
mo.ui.plotly(_plot)
|
|
|
|
|
113 |
return
|
114 |
|
115 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
116 |
if __name__ == "__main__":
|
117 |
app.run()
|
requirements.txt
CHANGED
@@ -1,5 +1,43 @@
|
|
1 |
-
|
2 |
-
|
3 |
-
|
4 |
-
|
5 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
altair==5.4.1
|
2 |
+
anyio==4.6.2.post1
|
3 |
+
attrs==24.2.0
|
4 |
+
click==8.1.7
|
5 |
+
docutils==0.21.2
|
6 |
+
duckdb==1.1.3
|
7 |
+
exceptiongroup==1.2.2
|
8 |
+
h11==0.14.0
|
9 |
+
idna==3.10
|
10 |
+
importlib_metadata==8.5.0
|
11 |
+
itsdangerous==2.2.0
|
12 |
+
jedi==0.19.1
|
13 |
+
Jinja2==3.1.4
|
14 |
+
jsonschema==4.23.0
|
15 |
+
jsonschema-specifications==2024.10.1
|
16 |
+
marimo==0.9.14
|
17 |
+
Markdown==3.7
|
18 |
+
MarkupSafe==3.0.2
|
19 |
+
narwhals==1.13.2
|
20 |
+
numpy==2.0.2
|
21 |
+
packaging==24.1
|
22 |
+
pandas==2.2.3
|
23 |
+
parso==0.8.4
|
24 |
+
plotly==5.24.1
|
25 |
+
psutil==6.1.0
|
26 |
+
Pygments==2.18.0
|
27 |
+
pymdown-extensions==10.12
|
28 |
+
python-dateutil==2.9.0.post0
|
29 |
+
pytz==2024.2
|
30 |
+
PyYAML==6.0.2
|
31 |
+
referencing==0.35.1
|
32 |
+
rpds-py==0.20.1
|
33 |
+
ruff==0.7.2
|
34 |
+
six==1.16.0
|
35 |
+
sniffio==1.3.1
|
36 |
+
starlette==0.41.2
|
37 |
+
tenacity==9.0.0
|
38 |
+
tomlkit==0.13.2
|
39 |
+
typing_extensions==4.12.2
|
40 |
+
tzdata==2024.2
|
41 |
+
uvicorn==0.32.0
|
42 |
+
websockets==12.0
|
43 |
+
zipp==3.20.2
|