Spaces:
Running
Running
new
Browse files
README.md
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Check out marimo at <https://github.com/marimo-team/marimo>
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Check out the configuration reference at <https://huggingface.co/docs/hub/spaces-config-reference>
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Check out marimo at <https://github.com/marimo-team/marimo>
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Check out the configuration reference at <https://huggingface.co/docs/hub/spaces-config-reference>
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Make sure you set your [access token](https://huggingface.co/blog/password-git-deprecation) before pushing/pulling.
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```bash
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git remote set-url origin https://<user_name>:<token>@huggingface.co/<repo_path>
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git pull origin
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git push origin
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```
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app.py
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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
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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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{"##" + "๐" * slider.value}
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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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mo.
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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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"""
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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
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def
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mo.
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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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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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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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**๐ 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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@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: 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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@app.cell
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def
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mo.
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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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)
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return
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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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return
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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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return (
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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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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 โ 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
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def
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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 __(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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```
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marimo run notebook.py
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```
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notebook using `marimo convert`:
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**Tutorials.** marimo comes packaged with tutorials:
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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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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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)
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return
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mo.accordion(tips)
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return
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mo.md("""## Finally, a fun fact""")
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return
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@app.cell
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def
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mo
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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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- _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 ( โท ) 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 ( โท ) 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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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 = "==3.10"
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# dependencies = [
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# "marimo",
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# "polars==1.23.0",
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# "sentence-transformers==3.4.1",
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# "umap-learn==0.5.7",
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# "llvmlite==0.44.0",
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# "altair==5.5.0",
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# "scikit-learn==1.6.1",
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# "numpy==2.1.3",
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# "mohtml==0.1.2",
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# ]
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# ///
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import marimo
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__generated_with = "0.11.9"
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app = marimo.App(width="medium")
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@app.cell
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def _(mo):
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mo.md("""### Bulk labelling demo""")
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return
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@app.cell
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def _(mo, use_default_switch):
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mo.stop(use_default_switch.value)
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uploaded_file = mo.ui.file(kind="area")
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uploaded_file
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return (uploaded_file,)
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@app.cell
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def _(mo):
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use_default_switch = mo.ui.switch(False, label="Use default dataset")
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use_default_switch
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return (use_default_switch,)
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@app.cell
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def _(mo):
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pos_label = mo.ui.text("pos", placeholder="positive label name")
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neg_label = mo.ui.text("neg", placeholder="negative label name")
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return neg_label, pos_label
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@app.cell
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def _(mo, pl, uploaded_f7ile, uploaded_file, use_default_switch):
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mo.stop(not use_default_switch.value and len(uploaded_file.value) == 0 , mo.md("**Submit a dataset or use default one to continue.**"))
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if use_default_switch.value:
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df = pl.read_csv("spam.csv")
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else:
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df = pl.read_csv(uploaded_f7ile.value[0].contents)
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texts = df["text"].to_list()
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return df, texts
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@app.cell
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def _(SentenceTransformer, mo, texts):
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with mo.status.spinner(subtitle="Creating embeddings ...") as _spinner:
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tfm = SentenceTransformer("all-MiniLM-L6-v2")
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X = tfm.encode(texts)
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return X, tfm
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@app.cell
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def _(X, mo):
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with mo.status.spinner(subtitle="Running UMAP ...") as _spinner:
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from umap import UMAP
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umap_tfm = UMAP()
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X_tfm = umap_tfm.fit_transform(X)
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return UMAP, X_tfm, umap_tfm
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@app.cell
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def _(add_label, mo, neg_label, pos_label, undo):
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btn_spam = mo.ui.button(label=f"Annotate {neg_label.value}", on_click=lambda d: add_label(neg_label.value))
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btn_ham = mo.ui.button(label=f"Annotate {pos_label.value}", on_click=lambda d: add_label(pos_label.value))
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btn_undo = mo.ui.button(label="Undo", on_click=lambda d: undo())
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return btn_ham, btn_spam, btn_undo
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@app.cell
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def _(chart, get_label, neg_label, pos_label, set_label):
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def add_label(lab):
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current_labels = get_label()
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if lab == neg_label.value:
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new_ham = list(set(current_labels[pos_label.value]).difference(chart.value["index"]))
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new_spam = list(set(current_labels[neg_label.value]).union(chart.value["index"]))
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if lab == pos_label.value:
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new_ham = list(set(current_labels[pos_label.value]).union(chart.value["index"]))
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new_spam = list(set(current_labels[neg_label.value]).difference(chart.value["index"]))
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set_label({neg_label.value: new_spam, pos_label.value: new_ham})
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return (add_label,)
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@app.cell
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+
def _(
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br,
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btn_ham,
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btn_spam,
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btn_undo,
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chart,
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form,
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json_download,
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mo,
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neg_label,
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pos_label,
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switch,
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):
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mo.vstack([
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mo.md("Assign label names"),
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mo.hstack([pos_label, neg_label]),
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mo.md("Explore the data"),
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mo.hstack([btn_ham, btn_spam, btn_undo, switch, json_download]),
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br(),
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form if switch.value else "",
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br() if switch.value else "",
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chart
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])
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return
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@app.cell
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def _(chart):
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chart.value["text"]
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return
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@app.cell
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def _(chart, get_label, neg_label, pos_label, set_label):
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def undo():
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current_labels = get_label()
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new_spam = set(current_labels[neg_label.value]).difference(chart.value["index"])
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new_ham = set(current_labels[pos_label.value]).difference(chart.value["index"])
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set_label({neg_label.value: list(new_spam), pos_label.value: list(new_ham)})
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return (undo,)
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@app.cell
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def _():
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from mohtml import br
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return (br,)
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@app.cell
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def _(get_label):
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get_label()
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return
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@app.cell
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def _(mo, neg_label, pos_label):
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get_label, set_label = mo.state({pos_label.value: [], neg_label.value: []})
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return get_label, set_label
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@app.cell
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def _(mo):
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text_input = mo.ui.text_area(label="Reference sentences")
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form = mo.md("""{text_input}""").batch(text_input=text_input).form()
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return form, text_input
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@app.cell
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def _(df_emb, labels, mo):
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from collections import Counter
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with mo.status.spinner(subtitle="Starting UI ...") as _spinner:
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df_emb
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Counter(labels)
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return (Counter,)
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@app.cell
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def _(df_emb, mo, pl):
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import json
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data = df_emb.filter(pl.col("label") != "unlabeled").select("text", "label").to_dicts()
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json_download = mo.download(
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data=json.dumps(data).encode("utf-8"),
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filename="data.json",
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mimetype="application/json",
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label="Download JSON",
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)
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return data, json, json_download
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@app.cell
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def _(df_emb, mo, scatter):
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chart = mo.ui.altair_chart(scatter(df_emb))
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return (chart,)
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@app.cell
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def _(mo):
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switch = mo.ui.switch(False, label="Use search")
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return (switch,)
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@app.cell
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def _(alt, neg_label, pos_label, switch):
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def scatter(df):
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return (alt.Chart(df)
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.mark_circle()
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.encode(
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x=alt.X("x:Q"),
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y=alt.Y("y:Q"),
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color=alt.Color("sim:Q") if switch.value else alt.Color("label:N", scale=alt.Scale(
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domain=['unlabeled', pos_label.value, neg_label.value],
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range=['steelblue', 'green', 'red']
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))
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).properties(width=500, height=500))
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return (scatter,)
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@app.cell
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+
def _(
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229 |
+
X,
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X_tfm,
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cosine_similarity,
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form,
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get_label,
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neg_label,
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np,
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pl,
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pos_label,
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texts,
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239 |
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tfm,
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240 |
+
):
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241 |
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df_emb = (
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pl.DataFrame({
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243 |
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"x": X_tfm[:, 0],
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"y": X_tfm[:, 1],
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"index": range(X.shape[0]),
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+
"text": texts
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+
}).with_columns(sim=pl.lit(1))
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248 |
)
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249 |
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250 |
+
if form.value:
|
251 |
+
query = tfm.encode([form.value["text_input"]])
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252 |
+
similarity = cosine_similarity(query, X)[0]
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253 |
+
df_emb = df_emb.with_columns(sim=similarity)
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254 |
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255 |
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spam = set(get_label()[neg_label.value])
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256 |
+
ham = set(get_label()[pos_label.value])
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+
labels = []
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259 |
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for i in range(df_emb.shape[0]):
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260 |
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if i in spam:
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+
labels.append(neg_label.value)
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262 |
+
elif i in ham:
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263 |
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labels.append(pos_label.value)
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264 |
+
else:
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265 |
+
labels.append("unlabeled")
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266 |
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267 |
+
df_emb = df_emb.with_columns(label=np.array(labels))
|
268 |
+
return df_emb, ham, i, labels, query, similarity, spam
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269 |
|
270 |
|
271 |
+
@app.cell
|
272 |
+
def _():
|
273 |
+
import marimo as mo
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274 |
+
import polars as pl
|
275 |
+
from sentence_transformers import SentenceTransformer
|
276 |
+
import altair as alt
|
277 |
+
import numpy as np
|
278 |
+
from sklearn.metrics.pairwise import cosine_similarity
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279 |
+
from sklearn.linear_model import LogisticRegression
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280 |
+
return (
|
281 |
+
LogisticRegression,
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282 |
+
SentenceTransformer,
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283 |
+
alt,
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284 |
+
cosine_similarity,
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285 |
+
mo,
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286 |
+
np,
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287 |
+
pl,
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288 |
)
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289 |
|
290 |
|
291 |
if __name__ == "__main__":
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spam.csv
ADDED
The diff for this file is too large to render.
See raw diff
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