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indah

PyPI version Python versions License: Apache-2.0 CI

A Python UI framework for ephemeral cloud notebooks (Colab, Runpod). Reactive, single-port, no Node required.

Build an interactive UI from a single Python file and launch it straight from a Colab or Runpod cell. It keeps Streamlit's zero-config, single-port startup, adds the async performance of a real full-stack app, and ships its frontend pre-built so there is no Node, npm, or bun anywhere at install or runtime.

See it running in your browser - the full demo gallery, live, no install and no clone.

Status: Milestone 1 components shipped. On top of the MVP (reactive core, SSE transport, streaming, custom-component seam) it adds the full input set and layout containers, data-driven lists (chat/gallery), per-session state, file upload/download, and charting (server-PNG Plot plus client-side Chart, Heatmap, an interactive Table, Stat cards, and ImageOverlay). Install with pip install indah. Start with docs/PLAN.md for the plan and docs/SLICES.md for what is built.

Why another one

Pain indah's answer
Streamlit reruns the whole script on every interaction Reactive signals: only the affected components update (ADR-0003)
Gradio's layout and state model get awkward past a demo Plain Python components bound to state, custom layouts
Reflex needs a Node build step that breaks in transient containers Frontend ships pre-built in the wheel; zero runtime Node (ADR-0004)
Colab's proxy does not support WebSockets SSE + HTTP POST transport that passes the proxy (ADR-0002)

How it works

flowchart LR
    subgraph Cell["Colab / Runpod cell"]
        PY["Your Python app<br/>(reactive signals)"]
    end
    subgraph ASGI["Single ASGI app, one port"]
        CORE["Reactive core<br/>signals to JSON patches"]
        API["/api: SSE + POST"]
        STATIC["Pre-built Svelte shell<br/>(static assets in the wheel)"]
    end
    Browser["Browser via platform proxy"]

    PY --> CORE --> API
    STATIC -- served over HTTP --> Browser
    API -- SSE patches --> Browser
    Browser -- POST events --> API

One port, standard HTTP plus Server-Sent Events, so it works through the network proxies of Colab and Runpod without a tunnel or a local JavaScript toolchain.

Try it

Run the built-in demo from a clone:

git clone https://github.com/leejianrong/indah && cd indah
uv sync --extra dev
make demo            # prints a URL; binds the first free port from 8000

The demo streams a mock LLM token by token into a StreamText; below it, a Select switches a live DataFrame and a colour picker (a registered custom component) two-way binds a signal - all updating over SSE, with no WebSocket and no Node. Only the components that depend on a changed value are patched; there is no full-script rerun.

Other ways to run it:

make demo-notebook   # inline in a local JupyterLab cell
make demo-docker     # in Docker on an auto-picked free port

Prefer Docker with a stable http://indah.localhost/ hostname (via a machine-wide Traefik proxy)? make demo-traefik - see docs/DEV-DOCKER.md.

Your app

An app is a tree of components bound to reactive signals. Mutate a signal and only the components that read it update - no full-script rerun:

import indah
from indah import Signal, computed, Column, Slider, Text, Session

a, b = Signal(2), Signal(3)
total = computed(lambda: f"a + b = {a.value + b.value}")

page = Column(
    children=[
        Slider(a, min=0, max=10, label="a"),
        Slider(b, min=0, max=10, label="b"),
        Text(total),
    ]
)

indah.launch(
    indah.create_app(session=Session(page))
)  # prints the URL; embeds inline in Colab/Runpod

The frontend is a pre-built Svelte shell bundled in the wheel; no Node runs at install or runtime. See docs/SLICES.md for what's next.

Components

The starter set covers a typical AI demo (input, run, streamed output):

Component Use
Text a label bound to a signal, computed, or string
Button an on_click handler (sync or async def)
Slider / TextInput / Select inputs two-way bound to a signal
Image / Plot / DataFrame display a URL/bytes, a Matplotlib figure, or a table
StreamText a container that grows token by token over SSE
Column a vertical layout container

That is the starter subset. 0.2.0 also ships layout containers (Row, Grid, Card, Tabs, Sidebar, Expander), more inputs (Checkbox, Number, Radio, MultiSelect, Date), charting (Chart, Heatmap, Table, Stat, ImageOverlay), data-driven List / Chat / Gallery, and file Upload / Download. Building a dashboard that sits inside a service (live push, big tables, overlays, routing, editors)? Start with Build a dashboard. The full list is in the Components reference.

Need something the set does not cover? Register a custom component against the public JSON protocol, no framework fork and no Node build:

import indah

indah.register_component(
    "colorpicker",
    render={
        "tag": "input",
        "attrs": {"type": "color"},
        "bind": {"value": "value"},  # element value <- signal
        "on": {"input": {"event": "input", "prop": "value"}},  # UI change -> signal
    },
)

colour = indah.Signal("#ff8800")
picker = indah.custom("colorpicker", value=colour)  # two-way, like a built-in

The pre-built shell renders it from that declarative spec at runtime. The protocol is a documented, versioned public contract: see docs/protocol.md.

Examples

Every demo is live in the demo gallery (no install, no clone), opens in one click in Colab, and is a single Python file you can run as a script (ADR-0023).

Demo Live Colab Source
Streaming chatbot Open Open In Colab chatbot.py
Live training dashboard Open Open In Colab training_dashboard.py
Image generation Open Open In Colab diffusion.py
Poster generator Open Open In Colab poster.py
Image classifier Open Open In Colab upload_classify.py
Object detection Open Open In Colab object_detection.py
Hybrid charting Open Open In Colab charts.py
Stock peer analysis Open Open In Colab stocks.py
Pretty map Open Open In Colab prettymap.py
Audio analysis Open Open In Colab audio_analysis.py

Also: examples/starter_components.py (a ~20-line tour of the component set) and examples/demo.ipynb (the built-in demo inline in a notebook). The chatbot is walked through step by step in the docs: Build a chatbot.

The Colab notebooks are generated from the demo manifest by deploy/colab/make_colab.py; regenerate them after adding a demo.

Planning and design

Doc What
docs/PLAN.md Problem, solution, scope, requirements, architecture
docs/SLICES.md Vertical build increments with test plans
docs/QUESTIONS.md Decision register
docs/protocol.md The JSON UI protocol (public contract)
docs/adr/ Architecture Decision Records

Development

uv sync --extra dev
make check        # lint + fast tests (the pre-push gate)
make help         # list all targets

See AGENTS.md for repo conventions and docs/RELEASING.md for the release process.

License

Apache License 2.0.

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