Simplify getting-started notebook

- Cleaner pattern: mo.md() for docs, print() for runtime output
- Interactive UI controls that fall back to CLI args
- Works as both tutorial and script

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

getting-started.py

@@ -7,13 +7,13 @@
 # ]
 # ///
 """
-Getting Started with UV Scripts + Marimo
+Getting Started with Hugging Face Datasets
 
-This notebook demonstrates how marimo notebooks can work as both:
-- Interactive tutorials: `uvx marimo edit --sandbox getting-started.py`
-- Batch scripts: `uv run getting-started.py --dataset squad`
+This marimo notebook works in two modes:
+- Interactive: uvx marimo edit --sandbox getting-started.py
+- Script: uv run getting-started.py --dataset squad
 
-Run with --help to see available options.
+Same file, two experiences.
 """
 
 import marimo
@@ -24,101 +24,118 @@ app = marimo.App(width="medium")
 @app.cell
 def _():
     import marimo as mo
+    return (mo,)
+
 
+@app.cell
+def _(mo):
     mo.md(
         """
-        # Getting Started with UV Scripts
+        # Getting Started with Hugging Face Datasets
 
-        This notebook shows how to work with Hugging Face datasets using UV scripts.
+        This notebook shows how to load and explore datasets from the Hugging Face Hub.
 
-        **Two ways to run this:**
-        - **Interactive**: `uvx marimo edit --sandbox getting-started.py`
-        - **As a script**: `uv run getting-started.py --dataset squad`
+        **Run this notebook:**
+        - Interactive: `uvx marimo edit --sandbox getting-started.py`
+        - As a script: `uv run getting-started.py --dataset squad`
         """
     )
-    return (mo,)
+    return
 
 
 @app.cell
 def _(mo):
-    import argparse
-    import sys
+    mo.md(
+        """
+        ## Step 1: Configure
 
-    parser = argparse.ArgumentParser(
-        description="Load and explore a Hugging Face dataset"
-    )
-    parser.add_argument(
-        "--dataset",
-        default="stanfordnlp/imdb",
-        help="Dataset to load (default: stanfordnlp/imdb)",
-    )
-    parser.add_argument(
-        "--split",
-        default="train",
-        help="Split to load (default: train)",
-    )
-    parser.add_argument(
-        "--num-samples",
-        type=int,
-        default=5,
-        help="Number of samples to display (default: 5)",
+        Choose which dataset to load. In interactive mode, use the controls below.
+        In script mode, pass `--dataset` argument.
+        """
     )
+    return
+
 
-    # Parse known args to avoid issues with marimo's internal args
+@app.cell
+def _(mo):
+    import argparse
+
+    # Parse CLI args (works in both modes)
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--dataset", default="stanfordnlp/imdb")
+    parser.add_argument("--split", default="train")
+    parser.add_argument("--samples", type=int, default=5)
     args, _ = parser.parse_known_args()
 
+    # Interactive controls (only shown in notebook mode)
+    dataset_input = mo.ui.text(value=args.dataset, label="Dataset")
+    split_input = mo.ui.dropdown(["train", "test", "validation"], value=args.split, label="Split")
+    samples_input = mo.ui.slider(1, 20, value=args.samples, label="Samples")
+
+    mo.hstack([dataset_input, split_input, samples_input])
+    return args, argparse, dataset_input, parser, samples_input, split_input
+
+
+@app.cell
+def _(args, dataset_input, mo, samples_input, split_input):
+    # Use interactive values if available, otherwise CLI args
+    dataset_name = dataset_input.value or args.dataset
+    split_name = split_input.value or args.split
+    num_samples = samples_input.value or args.samples
+
+    print(f"Dataset: {dataset_name}, Split: {split_name}, Samples: {num_samples}")
+    return dataset_name, num_samples, split_name
+
+
+@app.cell
+def _(mo):
     mo.md(
-        f"""
-        ## Configuration
+        """
+        ## Step 2: Load Dataset
 
-        - **Dataset**: `{args.dataset}`
-        - **Split**: `{args.split}`
-        - **Samples to show**: `{args.num_samples}`
+        We use the `datasets` library to stream data directly from the Hub.
+        No need to download the entire dataset first!
         """
     )
-    return args, argparse, parser, sys
+    return
 
 
 @app.cell
-def _(args, mo):
+def _(dataset_name, split_name):
     from datasets import load_dataset
 
-    mo.md(f"Loading dataset `{args.dataset}`...")
-    return (load_dataset,)
+    print(f"Loading {dataset_name}...")
+    dataset = load_dataset(dataset_name, split=split_name)
+    print(f"Loaded {len(dataset):,} rows")
+    print(f"Features: {list(dataset.features.keys())}")
+    return dataset, load_dataset
 
 
 @app.cell
-def _(args, load_dataset, mo):
-    # Load the dataset
-    dataset = load_dataset(args.dataset, split=args.split)
-
+def _(mo):
     mo.md(
-        f"""
-        ## Dataset Info
+        """
+        ## Step 3: Explore the Data
 
-        - **Name**: {args.dataset}
-        - **Split**: {args.split}
-        - **Number of rows**: {len(dataset):,}
-        - **Features**: {list(dataset.features.keys())}
+        Let's look at a few samples from the dataset.
         """
     )
-    return (dataset,)
+    return
 
 
 @app.cell
-def _(args, dataset, mo):
-    # Show sample data
-    samples = dataset.select(range(min(args.num_samples, len(dataset))))
+def _(dataset, mo, num_samples):
+    # Select samples and display
+    samples = dataset.select(range(min(num_samples, len(dataset))))
+    df = samples.to_pandas()
 
-    mo.md(f"## Sample Data (first {len(samples)} rows)")
-    return (samples,)
+    # Truncate long text for display
+    for col in df.select_dtypes(include=["object"]).columns:
+        df[col] = df[col].apply(lambda x: str(x)[:200] + "..." if len(str(x)) > 200 else x)
 
-
-@app.cell
-def _(samples):
-    # Display as a table (marimo will render this nicely)
-    samples.to_pandas()
-    return
+    print(df.to_string())  # Shows in script mode
+    mo.ui.table(df)  # Shows in interactive mode
+    return df, samples
 
 
 @app.cell
@@ -127,13 +144,9 @@ def _(mo):
         """
         ## Next Steps
 
-        Now that you've seen the basics, try:
-
-        1. **Change the dataset**: Run with `--dataset emotion` or any HF dataset
-        2. **Explore interactively**: Edit cells, add visualizations
-        3. **Run as batch**: `uv run getting-started.py --dataset squad --num-samples 10`
-
-        Check out more UV scripts at [uv-scripts on Hugging Face](https://huggingface.co/uv-scripts)
+        - Try different datasets: `squad`, `emotion`, `wikitext`
+        - Run on HF Jobs: `hf jobs uv run --flavor cpu-basic ... getting-started.py`
+        - Check out more UV scripts at [uv-scripts](https://huggingface.co/uv-scripts)
         """
     )
     return