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align=\"center\"\u003e\n  \u003ca href=\"https://fastdash.app/\"\u003e\u003cimg src=\"https://storage.googleapis.com/fast_dash/0.1.8/logo.png\" alt=\"Fast Dash\" width=300\u003e\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n    \u003cem\u003eTurn any Python function into a web app with a single decorator ⚡\u003c/em\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://pypi.python.org/pypi/fast_dash\"\u003e\n    \u003cimg src=\"https://img.shields.io/pypi/v/fast_dash?color=%2334D058\u0026labelColor=black\" alt=\"Release Status\"\u003e\n\u003c/a\u003e\n\u003ca href=\"https://github.com/dkedar7/fast_dash/actions\"\u003e\n    \u003cimg src=\"https://github.com/dkedar7/fast_dash/actions/workflows/release.yml/badge.svg?labelColor=black\" alt=\"CI Status\"\u003e\n\u003c/a\u003e\n\u003ca href=\"https://github.com/dkedar7/fast_dash/blob/main/LICENSE\"\u003e\n    \u003cimg src=\"https://img.shields.io/github/license/dkedar7/fast_dash?color=2334D058\u0026labelColor=black\" alt=\"MIT License\"\u003e\n\u003c/a\u003e\n\u003ca href=\"https://docs.fastdash.app/\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/Docs-MkDocs-\u003cCOLOR\u003e.svg?labelColor=black\u0026color=%2334D058\" alt=\"Documentation\"\u003e\n\u003c/a\u003e\n\u003ca href=\"https://pepy.tech/project/fast-dash\"\u003e\n    \u003cimg src=\"https://static.pepy.tech/personalized-badge/fast-dash?period=total\u0026units=international_system\u0026left_color=black\u0026right_color=brightgreen\u0026left_text=Downloads\" alt=\"Downloads\"\u003e\n\u003c/a\u003e\n\u003ca href=\"https://codecov.io/gh/dkedar7/fast_dash\"\u003e\n    \u003cimg src=\"https://codecov.io/gh/dkedar7/fast_dash/graph/badge.svg?token=9QJvbeQ1g8\" alt=\"Coverage\"\u003e\n\u003c/a\u003e\n\u003c/p\u003e\n\n---\n\n- **Documentation**: [docs.fastdash.app](https://docs.fastdash.app)\n- **Source**: [github.com/dkedar7/fast_dash](https://github.com/dkedar7/fast_dash)\n- **Install**: `pip install fast-dash`\n- **Claude Code users**: `/plugin marketplace add dkedar7/fast_dash` then `/plugin install fast-dash@fast-dash` to load the Fast Dash skill — agents will pick it up automatically when you ask them to \"turn this function into a web app\".\n\n---\n\n## What is Fast Dash?\n\nFast Dash inspects your Python function's signature, picks UI components from the type hints and default values, and serves the result as a [Plotly Dash](https://github.com/plotly/dash) app — usually in under five lines of code. No frontend, no callbacks, no boilerplate.\n\nIt exists for one job: collapse the gap between a working Python function and a shareable interactive web app.\n\n![Fast Dash demo](https://storage.googleapis.com/fast_dash/0.2.6/FastDashDemo.png)\n\n## 30-second example\n\n```bash\npip install fast-dash\n```\n\n```python\nfrom fast_dash import fastdash\n\n@fastdash\ndef greet(name: str = \"world\") -\u003e str:\n    return f\"Hello, {name}!\"\n\n# Serving on http://127.0.0.1:8080\n```\n\nThat's the entire app. Open the URL, type a name, click Run, see the response.\n\n## How it works\n\nThe `@fastdash` decorator does three things at import time:\n\n1. **Inspects the function signature** — each parameter becomes an input component, the return becomes an output component.\n2. **Picks components from type hints and defaults** — `int` → number input, `bool` → checkbox, `pd.DataFrame` → table, etc. (full table below).\n3. **Builds a Dash app and starts the server** — the function body is wired as the callback that runs when the user clicks Run.\n\nYou can override any of this by passing components explicitly via `inputs=` and `outputs=`, or by skipping the decorator and using the `FastDash(...)` class directly.\n\n## Type hint → component reference\n\nInputs (parameter type → UI component):\n\n| Type hint | Component |\n| --- | --- |\n| `str` | Textarea |\n| `str` with `default=[...]` | Single-select dropdown |\n| `int`, `float` | Number input |\n| `int`/`float` with `range(...)` default | Slider |\n| `bool` | Checkbox |\n| `list` | Multi-select dropdown |\n| `dict` (with default) | Multi-select dropdown (keys) |\n| `datetime.date` | Date picker |\n| `PIL.Image.Image` | Image upload |\n| `Literal[\"a\", \"b\"]` | Single-select dropdown |\n| `enum.Enum` subclass | Single-select dropdown |\n| `Annotated[int, range(0, 100)]` | Slider |\n| `Annotated[str, [\"a\", \"b\"]]` | Single-select dropdown |\n| `Optional[T]` | As `T`, but nullable |\n| Any Dash component instance | Used directly |\n| Any Fast Dash component (`Text`, `Slider`, ...) | Used directly |\n\nOutputs (return type → UI component):\n\n| Return type | Component |\n| --- | --- |\n| `str`, `int`, `float`, etc. | Text (rendered as `\u003ch1\u003e`) |\n| `pd.DataFrame` | Table |\n| `PIL.Image.Image`, `matplotlib.figure.Figure` | Image |\n| `plotly.graph_objects.Figure` (or string-form `\"go.Figure\"`, `\"Figure\"`) | Plotly chart |\n| Tuple of types | Multiple outputs (one component each) |\n| Any Fast Dash component (`Graph`, `Image`, ...) | Used directly |\n\n```python\nfrom fast_dash import fastdash, Graph\n\n@fastdash\ndef chart(rows: int = 100) -\u003e Graph:\n    import plotly.express as px\n    return px.scatter(px.data.iris().head(rows), x=\"sepal_width\", y=\"sepal_length\")\n```\n\nUnknown hints fall back to text. Source-introspection failures (REPL, `exec`) fall back to generic `OUTPUT_1`, `OUTPUT_2` labels — the app still works.\n\n### Built-in components\n\nThe package exports ready-to-use Fast Dash components you can pass directly as `inputs=` or `outputs=`:\n\n| Component | Use as | Notes |\n| --- | --- | --- |\n| `Text`, `TextArea`, `PasswordInput` | input or output | Single-line, multi-line, masked text |\n| `NumberInput`, `Slider` | input | Numeric with optional bounds |\n| `Switch` | input | Toggle (True / False) |\n| `MultiSelect` | input | Multi-select dropdown |\n| `DateInput`, `DateRange` | input | Single date / date range picker |\n| `ColorInput` | input | Color picker, returns hex |\n| `Upload`, `UploadImage` | input | File / image upload |\n| `Graph`, `Image`, `Table`, `Markdown` | output | Plotly chart, image, DataFrame table, rendered Markdown |\n| `Chat` | output | Streaming chat history (with `stream=True`) |\n| `Download` | output | Triggers a browser download |\n\n## Common patterns\n\n**Multiple inputs and outputs**\n\n```python\nfrom fast_dash import fastdash\n\n@fastdash\ndef describe(text: str, count: int = 3) -\u003e str:\n    \"\"\"Repeat text `count` times.\"\"\"\n    return \" · \".join([text] * count)\n```\n\n**Mosaic layout** for arranging multiple outputs:\n\n```python\nfrom fast_dash import fastdash, Graph\nimport plotly.express as px\nimport pandas as pd\n\n@fastdash(mosaic=\"AB\\nAC\")\ndef dashboard(rows: int = 100) -\u003e (Graph, Graph, Graph):\n    df = px.data.iris().head(rows)\n    return (\n        px.scatter(df, x=\"sepal_width\", y=\"sepal_length\", color=\"species\"),\n        px.histogram(df, x=\"petal_width\"),\n        px.box(df, y=\"petal_length\", color=\"species\"),\n    )\n```\n\nThe mosaic string is ASCII art (inspired by Matplotlib's `subplot_mosaic`). Each letter corresponds to one output, in order.\n\n**Wrapping arbitrary Dash components** with `Fastify`:\n\n```python\nfrom fast_dash import fastdash, Fastify\nfrom dash import dcc\n\ncustom_slider = Fastify(dcc.Slider(min=0, max=100, value=50), \"value\")\n\n@fastdash\ndef my_app(x: custom_slider) -\u003e str:\n    return f\"You picked {x}\"\n```\n\n**Cascading inputs** with `depends_on` — wire one input's options to another input's value:\n\n```python\nfrom fast_dash import fastdash, depends_on\n\ncountries = {\n    \"USA\": [\"California\", \"Texas\", \"New York\"],\n    \"India\": [\"Maharashtra\", \"Karnataka\", \"Delhi\"],\n}\n\n@fastdash\ndef pick_state(\n    country: str = list(countries),\n    state: str = depends_on(\"country\", lambda c: countries[c]),\n) -\u003e str:\n    return f\"{state}, {country}\"\n```\n\nThe resolver receives the parent input's current value. Return:\n- a **list** to set the dependent dropdown's options (and clear its value),\n- a **dict** like `{\"data\": [...], \"value\": ...}` to set both, or\n- a **scalar** to set just the value.\n\n**Skip the decorator** when you want more control over the lifecycle:\n\n```python\nfrom fast_dash import FastDash\n\ndef my_fn(x: int) -\u003e int: return x * 2\n\napp = FastDash(callback_fn=my_fn, title=\"Doubler\", port=8050)\napp.run()\n```\n\n**Multiple functions in a single tabbed app** — pass a list of callbacks:\n\n```python\nfrom fast_dash import FastDash\n\ndef greet(name: str) -\u003e str:\n    return f\"Hello, {name}!\"\n\ndef add(a: int, b: int) -\u003e int:\n    return a + b\n\napp = FastDash([greet, add], tab_titles=[\"Greeter\", \"Adder\"])\napp.run()\n```\n\nEach function gets its own tab with independent inputs, outputs, and callbacks. `tab_titles` is optional — without it, tabs are named after the functions.\n\n**Multi-step pipelines** with `steps=` — chain functions into a wizard, threading outputs forward via `from_step`:\n\n```python\nfrom fast_dash import FastDash, from_step\nimport pandas as pd\n\ndef load_data(rows: int = 100) -\u003e pd.DataFrame:\n    \"\"\"Load a sample dataset.\"\"\"\n    return pd.DataFrame({\"x\": range(rows), \"y\": [i * 2 for i in range(rows)]})\n\ndef double(data=from_step(load_data)) -\u003e pd.DataFrame:\n    \"\"\"Double every value.\"\"\"\n    return data * 2\n\ndef summarise(data=from_step(double), prefix: str = \"Result:\") -\u003e str:\n    \"\"\"One-line summary.\"\"\"\n    return f\"{prefix} {len(data)} rows, sum={data.values.sum()}\"\n\nFastDash(steps=[load_data, double, summarise], title=\"Pipeline Demo\").run()\n```\n\nEach step is shown one at a time with a stepper progress indicator. Click **Run** to execute the active step, then **Next** to advance. Use `from_step(prev_fn)` as a parameter default to wire an upstream output into a downstream input. Steps without `from_step` parameters can mix in regular UI inputs (like `prefix` above).\n\n## Decorator options\n\nMost apps need none of these — defaults are sensible. Pass any of them as kwargs to `@fastdash(...)` or `FastDash(...)`.\n\n| Option | Default | Effect |\n| --- | --- | --- |\n| `title` | function name | App title shown in the header |\n| `inputs`, `outputs` | inferred | Override component selection |\n| `mosaic` | `None` | ASCII layout for multiple outputs |\n| `theme` | `\"JOURNAL\"` | Any [Bootswatch](https://bootswatch.com/) theme name |\n| `port` | `8080` | Port to serve on |\n| `mode` | `None` | Set to `\"jupyterlab\"`, `\"inline\"`, or `\"external\"` for notebook use |\n| `update_live` | `False` | Re-run on every input change instead of waiting for the Run button |\n| `about` | `True` | Show the docstring as an \"About\" modal; pass a string to override |\n| `minimal` | `False` | Hide chrome (header, footer, nav) for embedding |\n| `branding` | `False` | Show the Fast Dash rocket footer |\n| `stream` | `False` | Enable streaming outputs (see docs) |\n\nThe full list lives in the [docs](https://docs.fastdash.app).\n\n## Limits and gotchas\n\n- **Output labels are inferred from the `return` line of your source.** If the source can't be retrieved (REPL, `exec`, frozen environments), Fast Dash falls back to generic `OUTPUT_1`, `OUTPUT_2` labels rather than crashing. Pass `output_labels=[...]` explicitly to control them.\n- **Reusing component instances across inputs and outputs** can mutate shared attributes. Construct fresh components per slot (or use `inputs=Text` rather than `inputs=text_instance`).\n- **The `theme` arg expects a Bootswatch name**, not a CSS URL. The chrome (header, navbar, buttons, inputs) is rendered with Mantine components and does not pick up Bootswatch accent colors or fonts — only the dark/light mode flips for known dark themes (`CYBORG`, `DARKLY`, `QUARTZ`, `SLATE`, `SOLAR`, `SUPERHERO`, `VAPOR`). Bootswatch CSS still loads and styles `dbc`-rendered bits (e.g. data tables).\n\n## Development\n\n```bash\ngit clone https://github.com/dkedar7/fast_dash.git\ncd fast_dash\nuv pip install -e \".[test]\"   # or: pip install -e \".[test]\"\nuv pip install \"dash[testing]\"\nuv run --no-sync pytest tests/\n```\n\nSelenium tests need a `chromedriver` matching your installed Chrome version (`brew install --cask chromedriver` on macOS).\n\nThe `tests/` directory is the canonical example collection — `tests/examples.py` and `tests/test_typing_hints.py` cover most patterns the library supports.\n\n## Project structure\n\n```\nfast_dash/\n  fast_dash.py     # FastDash class + @fastdash decorator + callback wiring\n  Components.py    # Layout (AppLayout) + type-hint inference + built-in components\n  utils.py         # Source introspection, theme mapping, docstring parsing\n  assets/          # Static CSS served by Dash\ntests/             # pytest suite (also serves as runnable examples)\ndocs/              # MkDocs site source\n```\n\n## License\n\nMIT. Built on top of [Plotly Dash](https://github.com/plotly/dash).\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdkedar7%2Ffast_dash","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdkedar7%2Ffast_dash","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdkedar7%2Ffast_dash/lists"}