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feat: offline OCR (Tesseract) + embedding category classifier (@xenova/transformers)

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Tesseract OCR (PHP, server-side):
- Dockerfile: adds tesseract-ocr + tesseract-ocr-ita + libgd-dev (gd extension)
- api/index.php: new tesseractReadExpiry() — decodes base64 image, pre-processes with GD (2× upscale, greyscale, auto-contrast, sharpen), runs tesseract CLI with ita+eng PSM-6, extracts date with multi-pattern regex (DD/MM/YYYY, MM/YYYY, ISO, named-month), returns YYYY-MM-DD + confidence
- geminiReadExpiry() now: (1) tries Tesseract first; (2) falls back to Gemini Vision if OCR returns null or no date found; (3) passes source ('ocr'|'gemini') in response

@xenova/transformers embedding classifier (browser-side):
- index.html: ES-module bootstrap that lazy-loads 'Xenova/all-MiniLM-L6-v2' quantized (~23 MB, cached in browser) via window._getCategoryPipeline(); pre-warms on first scan page visit
- assets/js/app.js: classifyCategoryByEmbedding(name) — embeds product name + 16 category anchor descriptions, cosine similarity, threshold 0.30; results cached in _embeddingCache Map
- autoDetectCategory(): after keyword map misses, fires classifyCategoryByEmbedding async and updates select when resolved (respects manuallySet flag)
- createQuickProduct(): if regex returned 'altro', silently patches category with embedding result via a background api call
This commit is contained in:
dadaloop82
2026-05-03 13:17:14 +00:00
parent c814d99d1f
commit a6c2fb93cf
4 changed files with 363 additions and 13 deletions
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assets/js/app.js
+142 -2
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@@ -1086,6 +1086,106 @@ function guessCategoryFromName(name) {
return 'altro';
}
// ─────────────────────────────────────────────────────────────────────────────
// Embedding-based category classifier (async, @xenova/transformers)
// ─────────────────────────────────────────────────────────────────────────────
// Canonical descriptions for each local category (used as embedding anchors).
const _CATEGORY_DESCRIPTIONS = {
latticini: 'latte yogurt formaggio burro panna mozzarella latticini dairy',
carne: 'carne pollo manzo maiale vitello prosciutto salame bresaola meat',
pesce: 'pesce tonno salmone merluzzo gamberi seafood fish',
frutta: 'frutta mela banana arancia pera fragola uva kiwi fruit',
verdura: 'verdura insalata zucchina carota cipolla spinaci tomato vegetables',
pasta: 'pasta spaghetti penne fusilli riso risotto noodles rice',
pane: 'pane fette biscottate grissini cracker toast bread bakery',
surgelati: 'surgelati congelato frozen gelato ice cream',
bevande: 'acqua birra vino succo caffè tè bevande drinks beverages',
condimenti: 'olio aceto sale zucchero farina ketchup maionese senape spezie condiments',
snack: 'biscotti cioccolato patatine snack caramelle wafer merendine',
conserve: 'conserve pelati passata marmellata miele legumi ceci beans canned',
cereali: 'cereali muesli granola fiocchi d\'avena oat breakfast cereal',
igiene: 'sapone shampoo dentifricio deodorante igiene personale hygiene',
pulizia: 'detersivo detergente pulizia casa sgrassatore cleaning',
altro: 'prodotto generico varie altro miscellaneous',
};
// In-memory cache: productName → category (avoids re-embedding the same product)
const _embeddingCache = new Map();
/**
* Cosine similarity between two Float32Array vectors.
*/
function _cosineSim(a, b) {
let dot = 0, na = 0, nb = 0;
for (let i = 0; i < a.length; i++) {
dot += a[i] * b[i];
na += a[i] * a[i];
nb += b[i] * b[i];
}
return dot / (Math.sqrt(na) * Math.sqrt(nb) + 1e-9);
}
/**
* Mean-pool a [1, tokens, dims] tensor → Float32Array of length dims.
*/
function _meanPool(tensor) {
const [, tokens, dims] = tensor.dims;
const data = tensor.data;
const out = new Float32Array(dims);
for (let t = 0; t < tokens; t++) {
for (let d = 0; d < dims; d++) {
out[d] += data[t * dims + d];
}
}
for (let d = 0; d < dims; d++) out[d] /= tokens;
return out;
}
/**
* Async: returns the best-matching category key for `productName`.
* Returns null if the model is unavailable or similarity is too low.
* THRESHOLD 0.30 — below this the regex fallback is more reliable.
*/
async function classifyCategoryByEmbedding(productName) {
if (!productName) return null;
const key = productName.toLowerCase().trim();
if (_embeddingCache.has(key)) return _embeddingCache.get(key);
if (typeof window._getCategoryPipeline !== 'function') return null;
const pipe = await window._getCategoryPipeline();
if (!pipe) return null;
try {
const labels = Object.keys(_CATEGORY_DESCRIPTIONS);
const texts = [key, ...labels.map(l => _CATEGORY_DESCRIPTIONS[l])];
// Embed all texts in one batched call for efficiency
const output = await pipe(texts, { pooling: 'mean', normalize: true });
const vectors = labels.map((_, i) => {
const t = output[i + 1];
// output[i] may be a Tensor or already a plain array-like
return t.dims ? _meanPool(t) : new Float32Array(t.data ?? t);
});
const queryVec = output[0].dims
? _meanPool(output[0])
: new Float32Array(output[0].data ?? output[0]);
let bestLabel = null, bestSim = 0;
for (let i = 0; i < labels.length; i++) {
const sim = _cosineSim(queryVec, vectors[i]);
if (sim > bestSim) { bestSim = sim; bestLabel = labels[i]; }
}
const result = (bestSim >= 0.30 && bestLabel !== 'altro') ? bestLabel : null;
_embeddingCache.set(key, result);
return result;
} catch (e) {
console.warn('[EverShelf] Embedding classify error:', e);
return null;
}
}
// Determine safety level for expired products
// Returns { level: 'danger'|'warning'|'ok', icon, label, tip }
function getExpiredSafety(item, daysExpired) {
@@ -2024,7 +2124,12 @@ function showPage(pageId, param = null) {
}
loadInventory();
break;
case 'scan': initScanner(); clearQuickNameResults(); updateSpesaBanner(); break;
case 'scan': initScanner(); clearQuickNameResults(); updateSpesaBanner();
// Pre-warm the embedding model the first time user visits scan page
if (typeof window._getCategoryPipeline === 'function' && !window._categoryPipelineReady) {
window._getCategoryPipeline(); // fire-and-forget
}
break;
case 'products': loadAllProducts(); break;
case 'shopping': loadShoppingList(); break;
case 'recipe': loadRecipeArchive(); break;
@@ -4470,7 +4575,7 @@ function selectQuickProduct(product) {
async function createQuickProduct(name) {
showLoading(true);
// Auto-detect category from name
// Auto-detect category from name (sync regex first)
const category = guessCategoryFromName(name);
try {
@@ -4494,6 +4599,27 @@ async function createQuickProduct(name) {
showLoading(false);
clearQuickNameResults();
showToast('Prodotto creato!', 'success');
// If regex gave 'altro', try embedding in background and silently update
if (category === 'altro' && typeof classifyCategoryByEmbedding === 'function') {
classifyCategoryByEmbedding(name).then(async embCat => {
if (!embCat || !result.id) return;
try {
await api('product_save', {}, 'POST', {
id: result.id,
name: name,
brand: '',
category: embCat,
unit: 'pz',
default_quantity: 1,
});
if (currentProduct && currentProduct.id === result.id) {
currentProduct.category = embCat;
}
} catch (_) { /* silent */ }
});
}
showProductAction();
} else {
showLoading(false);
@@ -4614,6 +4740,20 @@ function autoDetectCategory() {
return;
}
}
// ── Embedding fallback: async, only when keywords didn't match ──────────
// Kick off model load (no-op if already loaded/loading) and update the
// select once the result is ready. Only runs when pipeline is available.
if (typeof classifyCategoryByEmbedding === 'function') {
classifyCategoryByEmbedding(document.getElementById('pf-name').value).then(embCat => {
if (!embCat) return;
// Re-check manuallySet — user might have picked something while awaiting
const sel = document.getElementById('pf-category');
if (!sel || sel.dataset.manuallySet === 'true') return;
sel.value = embCat;
onCategoryChange(true);
});
}
}
function onCategoryChange(fromAutoDetect = false) {