feat: remove formal/informal LDA analysis from the bicorder (v1.3.0)
The two-family cluster classification is a research finding, not a
diagnostic; embedding it in the tool caused recurring bugs:
- ascii_bicorder.py had an inverted LDA sign (institutional mapped to 9,
not 1) and a stale term check ('bureaucratic') that silently disabled
the calculation after the Feb 2026 rename — output was always null
- The web app and Python script had divergent sign conventions and
divergent version-mismatch behavior (skip vs. continue with stale model)
Changes:
- bicorder.json: drop the formal/informal analysis gradient; version 1.3.0
- ascii_bicorder.py: remove all LDA/model machinery; keep hardness and
polarization as the only automated analyses
- App.svelte: remove classifier import, model constant, LDA calculation,
and form-recommendation reactive block; dispatch automated analyses by
term_left instead of array index
- Delete bicorder-classifier.ts and FormRecommendation.svelte (the latter
was imported but never rendered)
- AnalysisTransitionBanner: remove recommendation alert and prop; fix
hardcoded index checks that referenced the removed gradient
- vite.config.ts / vite-env.d.ts: stop loading bicorder_model.json
The cluster classifier lives on as research in analysis/ (scripts and
model untouched there). bicorder.txt regenerated.
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@@ -6,18 +6,12 @@
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import AnalysisDisplay from './components/AnalysisDisplay.svelte';
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import ExportControls from './components/ExportControls.svelte';
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import HelpModal from './components/HelpModal.svelte';
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import FormRecommendation from './components/FormRecommendation.svelte';
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import AnalysisTransitionBanner from './components/AnalysisTransitionBanner.svelte';
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import HamburgerMenu from './components/HamburgerMenu.svelte';
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import Landing from './components/Landing.svelte';
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import { BicorderClassifier } from './bicorder-classifier';
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// Load bicorder data and model from build-time constants
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let data: BicorderState = JSON.parse(JSON.stringify(__BICORDER_DATA__));
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const model = __BICORDER_MODEL__;
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// Initialize classifier
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const classifier = new BicorderClassifier(model, data.version);
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// Initialize timestamp if not set
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if (!data.metadata.timestamp) {
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@@ -87,10 +81,12 @@
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});
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// Analysis screens (shown in both shortform and longform)
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// Show the useful gradient first (index 3), then the others
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const analysisOrder = [3, 0, 1, 2]; // useful, hardness, polarization, formal/informal
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// Show the useful gradient first, then the automated ones
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const analysisOrder = [2, 0, 1]; // useful, hardness, polarization
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analysisOrder.forEach((index) => {
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screens.push({ type: 'analysis', index, gradient: data.analysis[index] });
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if (index < data.analysis.length) {
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screens.push({ type: 'analysis', index, gradient: data.analysis[index] });
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}
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});
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// Export screen
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@@ -180,57 +176,6 @@
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.flatMap(set => set.gradients)
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.filter(g => !data.metadata.shortform || g.shortform).length;
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// Calculate form recommendation (shared by FormRecommendation and AnalysisTransitionBanner)
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let formRecommendation: any = null;
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let hasEnoughDataForRecommendation = false;
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$: {
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// Collect ratings from diagnostic data
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const ratings: Record<string, number> = {};
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let valueCount = 0;
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let shortFormTotal = 0;
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data.diagnostic.forEach((diagnosticSet) => {
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const setName = diagnosticSet.set_name;
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diagnosticSet.gradients.forEach((gradient) => {
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// Count shortform gradients
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if (gradient.shortform) {
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shortFormTotal++;
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}
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if (gradient.value !== null) {
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const dimensionName = `${setName}_${gradient.term_left}_vs_${gradient.term_right}`;
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ratings[dimensionName] = gradient.value;
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// Only count shortform values for the threshold
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if (gradient.shortform) {
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valueCount++;
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}
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}
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});
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});
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// Only calculate if at least half of shortform gradients are complete
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const threshold = Math.ceil(shortFormTotal / 2);
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hasEnoughDataForRecommendation = valueCount >= threshold;
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if (hasEnoughDataForRecommendation && data.metadata.shortform) {
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try {
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const prediction = classifier.predict(ratings, { detailed: true });
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const assessment = classifier.assessShortFormReadiness(ratings);
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formRecommendation = {
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...prediction,
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...assessment,
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};
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} catch (error) {
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console.error('Error calculating form recommendation:', error);
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formRecommendation = null;
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}
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} else {
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formRecommendation = null;
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}
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}
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// Load saved state from localStorage
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onMount(() => {
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const saved = localStorage.getItem('bicorder-state');
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@@ -328,79 +273,21 @@
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return Math.round(Math.max(1, Math.min(9, polarizationScore)));
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}
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function ldaScoreToScale(ldaScore: number | null): number | null {
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/**
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* Convert LDA score to the analysis[2] "formal vs informal" 1-9 scale.
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* LDA scores typically range from -4 to +4 (8 range); target is 1-9.
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*
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* The model's sign convention (see analysis/bicorder_model.json):
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* positive LDA → cluster 2 = Institutional/Bureaucratic = "formal"
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* negative LDA → cluster 1 = Relational/Cultural = "informal"
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* bicorder.json defines this gradient as 1 = formal, 9 = informal, so a
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* positive LDA score must map toward 1 (formal). The score is therefore
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* subtracted, not added.
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*
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* Formula: value = 5 - (ldaScore * 4/3)
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* - LDA +3 or more → 1 (formal / institutional / bureaucratic)
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* - LDA 0 → 5 (boundary, characteristics of both families)
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* - LDA -3 or less → 9 (informal / relational / cultural)
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*/
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if (ldaScore === null) return null;
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const value = 5 - (ldaScore * 4.0 / 3.0);
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// Clamp to 1-9 range and round
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return Math.round(Math.max(1, Math.min(9, value)));
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}
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function calculateFormalInformal(): number | null {
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// Collect all diagnostic gradients with their set and gradient info
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const ratings: Record<string, number> = {};
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data.diagnostic.forEach((diagnosticSet) => {
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const setName = diagnosticSet.set_name;
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diagnosticSet.gradients.forEach((gradient) => {
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if (gradient.value !== null) {
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// Dimension name must match the model's keys: SetName_left_vs_right
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const dimensionName = `${setName}_${gradient.term_left}_vs_${gradient.term_right}`;
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ratings[dimensionName] = gradient.value;
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}
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});
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});
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// Check if we have any ratings
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if (Object.keys(ratings).length === 0) return null;
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try {
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// Get prediction from classifier (need detailed: true to get ldaScore)
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const result = classifier.predict(ratings, { detailed: true });
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// Convert LDA score to the 1-9 formal/informal scale
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return ldaScoreToScale(result.ldaScore);
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} catch (error) {
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console.error('Error calculating formal/informal score:', error);
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return null;
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}
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}
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// Update automated analysis values reactively
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// Note: the formal/informal (LDA classifier) analysis was removed from the
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// bicorder in v1.3.0. Cluster classification lives on as research in
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// analysis/ (see scripts/bicorder_classifier.py).
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$: {
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data.analysis.forEach((item, index) => {
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if (item.automated) {
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if (index === 0) {
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// Hardness/Softness
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data.analysis[0].value = calculateHardness();
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} else if (index === 1) {
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// Polarized/Centrist
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data.analysis[1].value = calculatePolarization();
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} else if (index === 2) {
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// Formal/Informal (LDA classifier)
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data.analysis[2].value = calculateFormalInformal();
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if (item.term_left === 'hardness') {
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item.value = calculateHardness();
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} else if (item.term_left === 'polarized') {
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item.value = calculatePolarization();
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}
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}
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});
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}
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function handleMetadataUpdate(event: CustomEvent) {
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// Properly trigger reactivity for nested metadata changes
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data = {
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@@ -604,7 +491,6 @@
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{#if isFirstAnalysisScreen}
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<AnalysisTransitionBanner
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recommendation={formRecommendation}
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isShortForm={data.metadata.shortform}
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completedGradients={completedGradientsCount}
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allAnalysisGradients={data.analysis}
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@@ -1,272 +0,0 @@
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/**
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* Bicorder Cluster Classifier
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*
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* Real-time protocol classification for the Bicorder web app.
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* Predicts which protocol family (Relational/Cultural vs Institutional/Bureaucratic)
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* a protocol belongs to based on dimension ratings.
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*
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* Usage:
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* import { BicorderClassifier } from './bicorder-classifier.js';
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*
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* const classifier = new BicorderClassifier(modelData);
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* const result = classifier.predict(ratings);
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* console.log(`Cluster: ${result.clusterName} (${result.confidence}% confidence)`);
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*/
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export class BicorderClassifier {
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/**
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* @param {Object} model - Model data loaded from bicorder_model.json
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* @param {string} bicorderVersion - Version of bicorder.json being used
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*
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* Simple version-matching approach: The model includes a bicorder_version
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* field. When bicorder structure changes, update the version and retrain.
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*/
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constructor(model, bicorderVersion = null) {
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this.model = model;
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this.dimensions = model.dimensions;
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this.keyDimensions = model.key_dimensions;
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this.bicorderVersion = bicorderVersion;
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// Check version compatibility
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if (bicorderVersion && model.bicorder_version && bicorderVersion !== model.bicorder_version) {
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console.warn(`Model version (${model.bicorder_version}) doesn't match bicorder version (${bicorderVersion}). Results may be inaccurate.`);
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}
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}
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/**
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* Standardize values using the fitted scaler
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* @private
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*/
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_standardize(values) {
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return values.map((val, i) => {
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if (val === null || val === undefined) return null;
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return (val - this.model.scaler.mean[i]) / this.model.scaler.scale[i];
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});
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}
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/**
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* Calculate LDA score (position on discriminant axis)
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* @private
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*/
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_ldaScore(scaledValues) {
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// Fill missing values with 0 (mean in scaled space)
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const filled = scaledValues.map(v => v === null ? 0 : v);
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// Calculate: coef · x + intercept
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let score = this.model.lda.intercept;
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for (let i = 0; i < filled.length; i++) {
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score += this.model.lda.coefficients[i] * filled[i];
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}
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return score;
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}
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/**
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* Calculate Euclidean distance
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* @private
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*/
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_distance(a, b) {
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let sum = 0;
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for (let i = 0; i < a.length; i++) {
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const diff = a[i] - b[i];
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sum += diff * diff;
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}
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return Math.sqrt(sum);
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}
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/**
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* Predict cluster for given ratings
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*
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* @param {Object} ratings - Map of dimension names to values (1-9)
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* Can be partial - missing dimensions handled gracefully
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* @param {Object} options - Options
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* @param {boolean} options.detailed - Return detailed information (default: true)
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*
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* @returns {Object} Prediction result with:
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* - cluster: Cluster number (1 or 2)
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* - clusterName: Human-readable name
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* - confidence: Confidence percentage (0-100)
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* - completeness: Percentage of dimensions provided (0-100)
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* - recommendedForm: 'short' or 'long'
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* - ldaScore: Position on discriminant axis
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* - distanceToBoundary: Distance from cluster boundary
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*/
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predict(ratings, options = { detailed: true }) {
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// Convert ratings object to array
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const values = this.dimensions.map(dim => ratings[dim] ?? null);
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const providedCount = values.filter(v => v !== null).length;
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const completeness = providedCount / this.dimensions.length;
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// Fill missing with neutral value (5 = middle of 1-9 scale)
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const filled = values.map(v => v ?? 5);
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// Standardize
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const scaled = this._standardize(filled);
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// Calculate LDA score
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const ldaScore = this._ldaScore(scaled);
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// Predict cluster (LDA boundary at 0)
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// Positive score = cluster 2 (Institutional)
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// Negative score = cluster 1 (Relational)
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const cluster = ldaScore > 0 ? 2 : 1;
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const clusterName = this.model.cluster_names[cluster];
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// Calculate confidence based on distance from boundary
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const distanceToBoundary = Math.abs(ldaScore);
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// Confidence: higher when further from boundary
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// Normalize based on typical strong separation (3.0)
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let confidence = Math.min(1.0, distanceToBoundary / 3.0);
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// Adjust for completeness
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const adjustedConfidence = confidence * (0.5 + 0.5 * completeness);
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// Recommend form
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// Use long form when multiple issues are present:
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// 1. Low confidence (< 0.5)
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// 2. Low completeness (< 50% of dimensions)
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// 3. Near boundary (< 0.3 distance)
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// Require at least 2 conditions to be true
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const issues = [
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adjustedConfidence < this.model.thresholds.confidence_low,
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completeness < this.model.thresholds.completeness_low,
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distanceToBoundary < this.model.thresholds.boundary_distance_low
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];
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const issueCount = issues.filter(Boolean).length;
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const shouldUseLongForm = issueCount >= 2;
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const recommendedForm = shouldUseLongForm ? 'long' : 'short';
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const basicResult = {
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cluster,
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clusterName,
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confidence: Math.round(adjustedConfidence * 100),
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completeness: Math.round(completeness * 100),
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recommendedForm,
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};
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if (!options.detailed) {
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return basicResult;
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}
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// Calculate distances to cluster centroids
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const filledScaled = scaled.map(v => v ?? 0);
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const distances = {};
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for (const [clusterId, centroid] of Object.entries(this.model.cluster_centroids_scaled)) {
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distances[clusterId] = this._distance(filledScaled, centroid);
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}
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// Count key dimensions provided
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const keyDimensionsProvided = this.keyDimensions.filter(
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dim => ratings[dim] !== null && ratings[dim] !== undefined
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).length;
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return {
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...basicResult,
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ldaScore,
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distanceToBoundary,
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dimensionsProvided: providedCount,
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dimensionsTotal: this.dimensions.length,
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keyDimensionsProvided,
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keyDimensionsTotal: this.keyDimensions.length,
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distancesToCentroids: distances,
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rawConfidence: Math.round(confidence * 100),
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};
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}
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/**
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* Get explanation of classification
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*
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* @param {Object} ratings - Dimension ratings
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* @returns {string} Human-readable explanation
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*/
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explainClassification(ratings) {
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const result = this.predict(ratings, { detailed: true });
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const lines = [];
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lines.push(`Protocol Classification: ${result.clusterName}`);
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lines.push(`Confidence: ${result.confidence}%`);
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lines.push('');
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if (result.cluster === 2) {
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lines.push('This protocol leans toward Institutional/Bureaucratic characteristics:');
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lines.push(' • More likely to be formal, standardized, top-down');
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lines.push(' • May involve state/corporate enforcement');
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lines.push(' • Tends toward precise, documented procedures');
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} else {
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lines.push('This protocol leans toward Relational/Cultural characteristics:');
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lines.push(' • More likely to be emergent, community-based');
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lines.push(' • May involve voluntary participation');
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lines.push(' • Tends toward interpretive, flexible practices');
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}
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lines.push('');
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lines.push(`Distance from boundary: ${result.distanceToBoundary.toFixed(2)}`);
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if (result.distanceToBoundary < 0.5) {
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lines.push('⚠️ This protocol is near the boundary between families.');
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lines.push(' It may exhibit characteristics of both types.');
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}
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lines.push('');
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lines.push(`Completeness: ${result.completeness}% (${result.dimensionsProvided}/${result.dimensionsTotal} dimensions)`);
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if (result.completeness < 100) {
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lines.push('Note: Missing dimensions filled with neutral values (5)');
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lines.push(' Confidence improves with complete data');
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}
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lines.push('');
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lines.push(`Recommended form: ${result.recommendedForm.toUpperCase()}`);
|
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|
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if (result.recommendedForm === 'long') {
|
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lines.push('Reason: Use long form for:');
|
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if (result.confidence < 60) {
|
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lines.push(' • Low classification confidence');
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}
|
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if (result.completeness < 50) {
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lines.push(' • Incomplete data');
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||||
}
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if (result.distanceToBoundary < 0.5) {
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lines.push(' • Ambiguous positioning between families');
|
||||
}
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||||
} else {
|
||||
lines.push(`Reason: High confidence classification with ${result.completeness}% data`);
|
||||
}
|
||||
|
||||
return lines.join('\n');
|
||||
}
|
||||
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||||
/**
|
||||
* Get the list of key dimensions for short form
|
||||
* @returns {Array<string>} Dimension names
|
||||
*/
|
||||
getKeyDimensions() {
|
||||
return [...this.keyDimensions];
|
||||
}
|
||||
|
||||
/**
|
||||
* Check if enough key dimensions are provided for reliable short-form classification
|
||||
* @param {Object} ratings - Current ratings
|
||||
* @returns {Object} Assessment with recommendation
|
||||
*/
|
||||
assessShortFormReadiness(ratings) {
|
||||
const keyProvided = this.keyDimensions.filter(
|
||||
dim => ratings[dim] !== null && ratings[dim] !== undefined
|
||||
);
|
||||
|
||||
const coverage = keyProvided.length / this.keyDimensions.length;
|
||||
const isReady = coverage >= 0.75; // 75% of key dimensions
|
||||
|
||||
return {
|
||||
ready: isReady,
|
||||
keyDimensionsProvided: keyProvided.length,
|
||||
keyDimensionsTotal: this.keyDimensions.length,
|
||||
coverage: Math.round(coverage * 100),
|
||||
missingKeyDimensions: this.keyDimensions.filter(
|
||||
dim => !ratings[dim]
|
||||
),
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -3,7 +3,6 @@
|
||||
import AnalysisDisplay from './AnalysisDisplay.svelte';
|
||||
import type { AnalysisGradient } from '../types';
|
||||
|
||||
export let recommendation: any = null;
|
||||
export let isShortForm: boolean;
|
||||
export let completedGradients: number;
|
||||
export let allAnalysisGradients: AnalysisGradient[];
|
||||
@@ -15,8 +14,6 @@
|
||||
updateAnalysisNotes: { index: number; notes: string };
|
||||
}>();
|
||||
|
||||
$: hasRecommendation = recommendation?.recommendedForm === 'long';
|
||||
|
||||
let showAllAnalysis = false;
|
||||
|
||||
function handleSwitchToLongForm() {
|
||||
@@ -42,32 +39,6 @@
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Recommendation Alert (if applicable) -->
|
||||
{#if isShortForm && hasRecommendation && recommendation}
|
||||
<div class="recommendation-alert">
|
||||
<div class="alert-header">
|
||||
<span class="alert-icon">⚠</span>
|
||||
<strong>Long Form Recommended</strong>
|
||||
</div>
|
||||
<div class="alert-body">
|
||||
<p class="alert-message">
|
||||
{#if recommendation.confidence < 60}
|
||||
• Low classification confidence ({recommendation.confidence}%)<br>
|
||||
{/if}
|
||||
{#if recommendation.completeness < 50}
|
||||
• Incomplete data ({recommendation.completeness}% of dimensions)<br>
|
||||
{/if}
|
||||
{#if recommendation.distanceToBoundary < 0.5}
|
||||
• Protocol near boundary between families<br>
|
||||
{/if}
|
||||
{#if recommendation.coverage < 75}
|
||||
• Missing key dimensions for reliable short-form classification ({recommendation.coverage}% coverage)<br>
|
||||
{/if}
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
{/if}
|
||||
|
||||
<!-- Action Buttons -->
|
||||
<div class="action-buttons">
|
||||
<button class="action-btn view-analysis-btn" on:click={toggleAllAnalysis}>
|
||||
@@ -88,16 +59,14 @@
|
||||
<div class="all-analysis-section">
|
||||
<div class="analysis-header">Analysis Gradients</div>
|
||||
{#each allAnalysisGradients as gradient, index}
|
||||
{#if index !== 3}
|
||||
<div class="analysis-item">
|
||||
<AnalysisDisplay
|
||||
{gradient}
|
||||
focusedMode={false}
|
||||
on:change={(e) => dispatch('updateAnalysis', { index, value: e.detail })}
|
||||
on:notes={(e) => dispatch('updateAnalysisNotes', { index, notes: e.detail })}
|
||||
/>
|
||||
</div>
|
||||
{/if}
|
||||
<div class="analysis-item">
|
||||
<AnalysisDisplay
|
||||
{gradient}
|
||||
focusedMode={false}
|
||||
on:change={(e) => dispatch('updateAnalysis', { index, value: e.detail })}
|
||||
on:notes={(e) => dispatch('updateAnalysisNotes', { index, notes: e.detail })}
|
||||
/>
|
||||
</div>
|
||||
{/each}
|
||||
</div>
|
||||
{/if}
|
||||
@@ -199,39 +168,11 @@
|
||||
opacity: 0.7;
|
||||
}
|
||||
|
||||
.recommendation-alert {
|
||||
margin-top: 1rem;
|
||||
padding: 1rem;
|
||||
background: rgba(251, 191, 36, 0.1);
|
||||
border: 2px solid #fbbf24;
|
||||
border-radius: 4px;
|
||||
}
|
||||
|
||||
.alert-header {
|
||||
.banner-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 0.5rem;
|
||||
margin-bottom: 0.75rem;
|
||||
}
|
||||
|
||||
.alert-icon {
|
||||
font-size: 1.2rem;
|
||||
color: #fbbf24;
|
||||
}
|
||||
|
||||
.alert-header strong {
|
||||
font-size: 1rem;
|
||||
color: #fbbf24;
|
||||
}
|
||||
|
||||
.alert-body {
|
||||
padding-left: 1.7rem;
|
||||
}
|
||||
|
||||
.alert-message {
|
||||
margin: 0;
|
||||
font-size: 0.9rem;
|
||||
line-height: 1.5;
|
||||
gap: 1rem;
|
||||
margin-bottom: 0.5rem;
|
||||
}
|
||||
|
||||
.action-buttons {
|
||||
@@ -343,10 +284,6 @@
|
||||
font-size: 0.8rem;
|
||||
}
|
||||
|
||||
.alert-body {
|
||||
padding-left: 1rem;
|
||||
}
|
||||
|
||||
.action-btn {
|
||||
font-size: 0.9rem;
|
||||
padding: 0.6rem;
|
||||
|
||||
@@ -1,399 +0,0 @@
|
||||
<script lang="ts">
|
||||
import { createEventDispatcher } from 'svelte';
|
||||
|
||||
export let recommendation: any = null;
|
||||
export let hasEnoughData: boolean;
|
||||
export let isShortForm: boolean;
|
||||
|
||||
const dispatch = createEventDispatcher<{
|
||||
switchToLongForm: void;
|
||||
}>();
|
||||
|
||||
let isExpanded = false;
|
||||
|
||||
function toggleExpanded() {
|
||||
isExpanded = !isExpanded;
|
||||
}
|
||||
|
||||
function handleSwitchToLongForm() {
|
||||
dispatch('switchToLongForm');
|
||||
isExpanded = false;
|
||||
}
|
||||
|
||||
// Determine status: 'good' (green) or 'warning' (yellow/orange)
|
||||
$: status = recommendation?.recommendedForm === 'long' ? 'warning' : 'good';
|
||||
$: showIndicator = hasEnoughData && isShortForm && recommendation;
|
||||
</script>
|
||||
|
||||
{#if showIndicator}
|
||||
<div class="form-recommendation" class:expanded={isExpanded}>
|
||||
<button
|
||||
class="indicator"
|
||||
class:good={status === 'good'}
|
||||
class:warning={status === 'warning'}
|
||||
on:click={toggleExpanded}
|
||||
aria-label="Data quality indicator"
|
||||
title={status === 'good' ? 'Short form working well' : 'Long form recommended'}
|
||||
>
|
||||
<span class="light"></span>
|
||||
</button>
|
||||
|
||||
{#if isExpanded}
|
||||
<div class="panel-backdrop" on:click={toggleExpanded} on:keydown={() => {}} role="button" tabindex="-1">
|
||||
<div class="details-panel" on:click|stopPropagation on:keydown={() => {}} role="dialog" aria-modal="true">
|
||||
<div class="panel-header">
|
||||
<h3>Data Quality Assessment</h3>
|
||||
<button class="close-btn" on:click={toggleExpanded} aria-label="Close">+</button>
|
||||
</div>
|
||||
|
||||
<div class="panel-body">
|
||||
<div class="metric">
|
||||
<span class="metric-label">Classification Confidence:</span>
|
||||
<span class="metric-value" class:low={recommendation.confidence < 60}>
|
||||
{recommendation.confidence}%
|
||||
</span>
|
||||
</div>
|
||||
|
||||
<div class="metric">
|
||||
<span class="metric-label">Data Completeness:</span>
|
||||
<span class="metric-value" class:low={recommendation.completeness < 50}>
|
||||
{recommendation.completeness}% ({recommendation.dimensionsProvided}/{recommendation.dimensionsTotal} dimensions)
|
||||
</span>
|
||||
</div>
|
||||
|
||||
<div class="metric">
|
||||
<span class="metric-label">Key Dimensions:</span>
|
||||
<span class="metric-value" class:low={recommendation.coverage < 75}>
|
||||
{recommendation.coverage}% ({recommendation.keyDimensionsProvided}/{recommendation.keyDimensionsTotal})
|
||||
</span>
|
||||
</div>
|
||||
|
||||
<div class="classification">
|
||||
<div class="classification-label">Current Classification:</div>
|
||||
<div class="classification-value">
|
||||
<strong>{recommendation.clusterName}</strong>
|
||||
{#if recommendation.distanceToBoundary < 0.5}
|
||||
<span class="boundary-warning">(Near boundary)</span>
|
||||
{/if}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{#if recommendation.recommendedForm === 'long'}
|
||||
<div class="recommendation-message warning">
|
||||
<strong>⚠ Long Form Recommended</strong>
|
||||
<p>
|
||||
{#if recommendation.confidence < 60}
|
||||
• Low classification confidence<br>
|
||||
{/if}
|
||||
{#if recommendation.completeness < 50}
|
||||
• Incomplete data (less than 50% of dimensions)<br>
|
||||
{/if}
|
||||
{#if recommendation.distanceToBoundary < 0.5}
|
||||
• Protocol near boundary between families<br>
|
||||
{/if}
|
||||
{#if recommendation.coverage < 75}
|
||||
• Missing key dimensions for reliable short-form classification<br>
|
||||
{/if}
|
||||
</p>
|
||||
<button class="switch-btn" on:click={handleSwitchToLongForm}>
|
||||
Switch to Long Form & Restart →
|
||||
</button>
|
||||
<p class="note">Returns to the beginning. All your current values will be preserved.</p>
|
||||
</div>
|
||||
{:else}
|
||||
<div class="recommendation-message good">
|
||||
<strong>✓ Short Form Working Well</strong>
|
||||
<p>
|
||||
Your current data provides {recommendation.confidence}% confidence classification.
|
||||
Continue with short form or switch to long form for more detailed analysis.
|
||||
</p>
|
||||
</div>
|
||||
{/if}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
{/if}
|
||||
</div>
|
||||
{/if}
|
||||
|
||||
<style>
|
||||
.form-recommendation {
|
||||
position: relative;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
}
|
||||
|
||||
.indicator {
|
||||
width: 2rem;
|
||||
height: 2rem;
|
||||
border-radius: 3px;
|
||||
border: 1px solid var(--border-color);
|
||||
background: var(--bg-color);
|
||||
cursor: pointer;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
transition: all 0.3s ease;
|
||||
padding: 0;
|
||||
opacity: 0.4;
|
||||
min-height: auto;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.indicator:hover {
|
||||
opacity: 0.8;
|
||||
transform: scale(1.05);
|
||||
}
|
||||
|
||||
.light {
|
||||
width: 1rem;
|
||||
height: 1rem;
|
||||
border-radius: 50%;
|
||||
transition: all 0.3s ease;
|
||||
}
|
||||
|
||||
.indicator.good .light {
|
||||
background: #4ade80;
|
||||
box-shadow: 0 0 8px rgba(74, 222, 128, 0.5);
|
||||
}
|
||||
|
||||
.indicator.warning .light {
|
||||
background: #fbbf24;
|
||||
box-shadow: 0 0 8px rgba(251, 191, 36, 0.5);
|
||||
animation: pulse 2s ease-in-out infinite;
|
||||
}
|
||||
|
||||
@keyframes pulse {
|
||||
0%, 100% {
|
||||
opacity: 1;
|
||||
}
|
||||
50% {
|
||||
opacity: 0.5;
|
||||
}
|
||||
}
|
||||
|
||||
.panel-backdrop {
|
||||
/* Hidden on desktop - only visible on mobile */
|
||||
display: none;
|
||||
}
|
||||
|
||||
.details-panel {
|
||||
position: absolute;
|
||||
top: calc(100% + 0.5rem);
|
||||
right: 0;
|
||||
width: 400px;
|
||||
max-width: calc(100vw - 2rem);
|
||||
background: var(--bg-color);
|
||||
border: 2px solid var(--border-color);
|
||||
box-shadow: 0 4px 16px rgba(0, 0, 0, 0.3);
|
||||
animation: slideIn 0.2s ease-out;
|
||||
z-index: 1000;
|
||||
}
|
||||
|
||||
@keyframes slideIn {
|
||||
from {
|
||||
opacity: 0;
|
||||
transform: translateY(-10px);
|
||||
}
|
||||
to {
|
||||
opacity: 1;
|
||||
transform: translateY(0);
|
||||
}
|
||||
}
|
||||
|
||||
.panel-header {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
padding: 1rem;
|
||||
border-bottom: 1px solid var(--border-color);
|
||||
}
|
||||
|
||||
.panel-header h3 {
|
||||
margin: 0;
|
||||
font-size: 1rem;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
.close-btn {
|
||||
background: none;
|
||||
border: none;
|
||||
font-size: 1.5rem;
|
||||
cursor: pointer;
|
||||
color: var(--fg-color);
|
||||
opacity: 0.6;
|
||||
padding: 0;
|
||||
width: 2rem;
|
||||
height: 2rem;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
min-height: auto;
|
||||
}
|
||||
|
||||
.close-btn:hover {
|
||||
opacity: 1;
|
||||
background: none;
|
||||
}
|
||||
|
||||
.panel-body {
|
||||
padding: 1rem;
|
||||
max-height: 70vh;
|
||||
overflow-y: auto;
|
||||
}
|
||||
|
||||
.metric {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
align-items: center;
|
||||
padding: 0.5rem 0;
|
||||
border-bottom: 1px solid var(--border-color);
|
||||
font-size: 0.9rem;
|
||||
}
|
||||
|
||||
.metric-label {
|
||||
font-weight: 500;
|
||||
}
|
||||
|
||||
.metric-value {
|
||||
font-weight: bold;
|
||||
color: #4ade80;
|
||||
}
|
||||
|
||||
.metric-value.low {
|
||||
color: #fbbf24;
|
||||
}
|
||||
|
||||
.classification {
|
||||
margin: 1rem 0;
|
||||
padding: 0.75rem;
|
||||
background: var(--input-bg);
|
||||
border: 1px solid var(--border-color);
|
||||
}
|
||||
|
||||
.classification-label {
|
||||
font-size: 0.85rem;
|
||||
opacity: 0.8;
|
||||
margin-bottom: 0.5rem;
|
||||
}
|
||||
|
||||
.classification-value {
|
||||
font-size: 1rem;
|
||||
}
|
||||
|
||||
.classification-value strong {
|
||||
color: var(--fg-color);
|
||||
}
|
||||
|
||||
.boundary-warning {
|
||||
color: #fbbf24;
|
||||
font-size: 0.85rem;
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
.recommendation-message {
|
||||
margin-top: 1rem;
|
||||
padding: 1rem;
|
||||
border-radius: 4px;
|
||||
border: 2px solid;
|
||||
}
|
||||
|
||||
.recommendation-message.good {
|
||||
background: rgba(74, 222, 128, 0.1);
|
||||
border-color: #4ade80;
|
||||
}
|
||||
|
||||
.recommendation-message.warning {
|
||||
background: rgba(251, 191, 36, 0.1);
|
||||
border-color: #fbbf24;
|
||||
}
|
||||
|
||||
.recommendation-message strong {
|
||||
display: block;
|
||||
margin-bottom: 0.5rem;
|
||||
font-size: 1rem;
|
||||
}
|
||||
|
||||
.recommendation-message p {
|
||||
margin: 0.5rem 0;
|
||||
font-size: 0.85rem;
|
||||
line-height: 1.6;
|
||||
}
|
||||
|
||||
.switch-btn {
|
||||
width: 100%;
|
||||
margin-top: 1rem;
|
||||
padding: 0.75rem;
|
||||
font-size: 1rem;
|
||||
font-weight: bold;
|
||||
background: #fbbf24;
|
||||
color: #1a1a2e;
|
||||
border: none;
|
||||
cursor: pointer;
|
||||
transition: all 0.2s;
|
||||
}
|
||||
|
||||
.switch-btn:hover {
|
||||
background: #f59e0b;
|
||||
transform: translateY(-1px);
|
||||
box-shadow: 0 2px 8px rgba(251, 191, 36, 0.3);
|
||||
}
|
||||
|
||||
.note {
|
||||
font-size: 0.75rem;
|
||||
font-style: italic;
|
||||
opacity: 0.7;
|
||||
margin-top: 0.5rem;
|
||||
}
|
||||
|
||||
@media (max-width: 768px) {
|
||||
.indicator {
|
||||
width: 1.5rem;
|
||||
height: 1.5rem;
|
||||
}
|
||||
|
||||
.light {
|
||||
width: 0.75rem;
|
||||
height: 0.75rem;
|
||||
}
|
||||
|
||||
/* Modal-like on mobile */
|
||||
.panel-backdrop {
|
||||
display: flex;
|
||||
position: fixed;
|
||||
top: 0;
|
||||
left: 0;
|
||||
right: 0;
|
||||
bottom: 0;
|
||||
background-color: rgba(0, 0, 0, 0.7);
|
||||
justify-content: center;
|
||||
align-items: center;
|
||||
z-index: 2000;
|
||||
padding: 1rem;
|
||||
}
|
||||
|
||||
.details-panel {
|
||||
position: relative;
|
||||
top: auto;
|
||||
right: auto;
|
||||
width: 100%;
|
||||
max-width: 500px;
|
||||
max-height: 85vh;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
}
|
||||
|
||||
.panel-body {
|
||||
overflow-y: auto;
|
||||
flex: 1;
|
||||
}
|
||||
|
||||
.panel-header h3 {
|
||||
font-size: 0.9rem;
|
||||
}
|
||||
|
||||
.metric {
|
||||
font-size: 0.85rem;
|
||||
}
|
||||
}
|
||||
</style>
|
||||
Vendored
-1
@@ -2,7 +2,6 @@
|
||||
/// <reference types="vite/client" />
|
||||
|
||||
declare const __BICORDER_DATA__: any
|
||||
declare const __BICORDER_MODEL__: any
|
||||
|
||||
interface ImportMetaEnv {
|
||||
readonly VITE_APP_TITLE: string
|
||||
|
||||
Reference in new issue
Block a user