ui/README.md

ui/ — AI usage evaluator UI shell

Self-contained Gradio front-end. Runs standalone (no backend / ML deps) so it can be developed and deployed independently of the prompt_card workstream.

Run

python -m ui.app          # serves the Gradio app

Layout

ui/
  app.py            # gr.Blocks, event wiring, the staged-reveal generator
  theme.py          # design tokens, dark CSS, CDN head (fonts + Chart.js + flip/radar JS)
  data.py           # CardData contract + stub provider  ← shared data shape
  components/card.py# render CardData -> flip-card HTML + accordion bodies
  screens/          # intake / processing / result column builders
  parsing/          # Task 2: ChatGPT+Claude .json/.zip parser + zero-dep language gate
  scoring/          # Task 3: Scorer protocol + DummyScorer (heuristic placeholder)

The three adoption seams (where the real backend plugs in)

1. Parsing — ui/parsing/parser.py::parse_export(path) -> ParsedExport. Mirrors the real export shapes; the backend's prompt_card.adapters are the reuse target when the workstreams merge.
2. Scoring — ui/scoring/interface.py::Scorer. Implement score(parsed) -> ScoreResult with a real model and pass it where DummyScorer() is used in app.py. The UI is unchanged.
3. Card data — ui/data.py::CardData is the stable render contract. score_to_card maps a ScoreResult onto it.

Status (stubbed vs real)

• Real: export parsing, language split, processing facts (counts/date range/busiest slot).
• Scoring ADOPTED (ui/scoring/observable.py::ObservableScorer): the 5 axis scores, overall/tier, and per-axis confidence (measured Cohen's κ) now come from the backend prompt_card.observable_pipeline (base MiniCPM4.1-8B + the locked per-category LoRA hybrid). app.py calls get_scorer(), which returns the real scorer when OPENBMB_BASE_URL/OPENBMB_TOKEN are set, else falls back to DummyScorer (so the UI still runs standalone with zero ML deps). A live scoring failure degrades to the dummy mid-demo.
• Now REAL too: per-axis evidence quotes are the actual user turns where each axis fired (pipeline data["evidence"]; falls back to a representative turn only when an axis had zero positives), and the critical breakdown shows true per-type counts (pipeline data["critical_type_counts"]).
• Still heuristic/static: the per-axis tips and the improvement line (advice, not measurements).