commit 5de44e757932acc2fc2a0cd5ba4231731e8a62d7 Author: albert Date: Sun Mar 29 21:58:07 2026 -0400 Initial commit: Vision scanner for shelf/pantry product extraction diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..222e252 --- /dev/null +++ b/.env.example @@ -0,0 +1,10 @@ +PORT=8002 +VISION_AI_URL=http://localhost:8000/v1/chat/completions +VISION_AI_MODEL=qwen2.5vl-it:3b +VISION_AI_TIMEOUT=120000 +OLLAMA_HOST=http://192.168.0.15:11434 +OLLAMA_EMBED_MODEL=nomic-embed-text +CHROMA_HOST=http://192.168.0.15:8000 +GEMINI_API_KEY= +MAX_CONCURRENT_TILES=4 +UPLOAD_DIR=C:\Users\sshuser\VisionScannerService\uploads diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..23ae443 --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +node_modules/ +dist/ +uploads/ +.env diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 0000000..ada06c4 --- /dev/null +++ b/package-lock.json @@ -0,0 +1,2548 @@ +{ + "name": "vision-scanner-service", + 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100644 index 0000000..368ab7f --- /dev/null +++ b/package.json @@ -0,0 +1,27 @@ +{ + "name": "vision-scanner-service", + "version": "1.0.0", + "description": "Standalone vision scanner service for shelf/pantry photo scanning", + "main": "dist/server.js", + "scripts": { + "dev": "tsx watch src/server.ts", + "build": "tsc", + "start": "node dist/server.js" + }, + "dependencies": { + "@google/generative-ai": "^0.21.0", + "chromadb": "^1.9.2", + "dotenv": "^16.4.7", + "express": "^5.1.0", + "multer": "^1.4.5-lts.2", + "node-windows": "^1.0.0-beta.8", + "sharp": "^0.33.5", + "zxing-wasm": "^3.0.1" + }, + "devDependencies": { + "@types/express": "^5.0.2", + "@types/multer": "^1.4.12", + "tsx": "^4.19.4", + "typescript": "^5.8.2" + } +} diff --git a/service-install.js b/service-install.js new file mode 100644 index 0000000..a26b252 --- /dev/null +++ b/service-install.js @@ -0,0 +1,27 @@ +const Service = require("node-windows").Service; +const path = require("path"); + +const svc = new Service({ + name: "Vision Scanner Service", + description: "Standalone vision scanner service for shelf/pantry photo scanning", + script: path.join(__dirname, "dist", "server.js"), + nodeOptions: [], + env: [ + { name: "PATH", value: process.env.PATH }, + ], +}); + +svc.on("install", () => { + console.log("Service installed. Starting..."); + svc.start(); +}); + +svc.on("start", () => { + console.log("Service started!"); +}); + +svc.on("error", (err) => { + console.error("Error:", err); +}); + +svc.install(); diff --git a/service-uninstall.js b/service-uninstall.js new file mode 100644 index 0000000..d35351e --- /dev/null +++ b/service-uninstall.js @@ -0,0 +1,18 @@ +const Service = require("node-windows").Service; +const path = require("path"); + +const svc = new Service({ + name: "Vision Scanner Service", + description: "Standalone vision scanner service for shelf/pantry photo scanning", + script: path.join(__dirname, "dist", "server.js"), +}); + +svc.on("uninstall", () => { + console.log("Service uninstalled."); +}); + +svc.on("error", (err) => { + console.error("Error:", err); +}); + +svc.uninstall(); diff --git a/src/chroma.ts b/src/chroma.ts new file mode 100644 index 0000000..f6d3d8e --- /dev/null +++ b/src/chroma.ts @@ -0,0 +1,39 @@ +import { ChromaClient, Collection } from "chromadb"; +import { config } from "./config"; + +let client: ChromaClient | null = null; +let collection: Collection | null = null; + +async function getCollection(): Promise { + if (!collection) { + client = new ChromaClient({ path: config.chromaHost }); + collection = await client.getOrCreateCollection({ + name: "product_images", + metadata: { "hnsw:space": "cosine" }, + }); + } + return collection; +} + +export interface ChromaQueryResult { + ids: string[][]; + distances: number[][] | null; + metadatas: (Record | null)[][] | null; +} + +export async function queryProducts( + queryEmbeddings: number[][], + nResults: number = 3 +): Promise { + const coll = await getCollection(); + return coll.query({ queryEmbeddings, nResults }) as unknown as ChromaQueryResult; +} + +export async function getCount(): Promise { + try { + const coll = await getCollection(); + return await coll.count(); + } catch { + return 0; + } +} diff --git a/src/config.ts b/src/config.ts new file mode 100644 index 0000000..7d05f05 --- /dev/null +++ b/src/config.ts @@ -0,0 +1,14 @@ +export const config = { + port: parseInt(process.env.PORT || "8002", 10), + visionAiUrl: process.env.VISION_AI_URL || "http://localhost:8000/v1/chat/completions", + visionAiModel: process.env.VISION_AI_MODEL || "qwen2.5vl-it:3b", + visionAiTimeout: parseInt(process.env.VISION_AI_TIMEOUT || "120000", 10), + ollamaHost: process.env.OLLAMA_HOST || "http://192.168.0.15:11434", + ollamaEmbedModel: process.env.OLLAMA_EMBED_MODEL || "nomic-embed-text", + chromaHost: process.env.CHROMA_HOST || "http://192.168.0.15:8000", + geminiApiKey: process.env.GEMINI_API_KEY || "", + geminiModel: process.env.GEMINI_MODEL || "gemini-2.5-flash", + maxConcurrentTiles: parseInt(process.env.MAX_CONCURRENT_TILES || "4", 10), + uploadDir: process.env.UPLOAD_DIR || "uploads", + maxFileSize: 10 * 1024 * 1024, // 10MB +}; diff --git a/src/embeddings.ts b/src/embeddings.ts new file mode 100644 index 0000000..44c23c8 --- /dev/null +++ b/src/embeddings.ts @@ -0,0 +1,30 @@ +import { config } from "./config"; + +/** + * Call Ollama embed endpoint for text embeddings. + */ +export async function embed(text: string): Promise { + const controller = new AbortController(); + const timeout = setTimeout(() => controller.abort(), 60000); + + try { + const response = await fetch(`${config.ollamaHost}/api/embed`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + signal: controller.signal, + body: JSON.stringify({ + model: config.ollamaEmbedModel, + input: text, + }), + }); + + if (!response.ok) { + throw new Error(`Ollama embed returned ${response.status}`); + } + + const data = (await response.json()) as { embeddings: number[][] }; + return data.embeddings[0]; + } finally { + clearTimeout(timeout); + } +} diff --git a/src/enrich.ts b/src/enrich.ts new file mode 100644 index 0000000..3bade20 --- /dev/null +++ b/src/enrich.ts @@ -0,0 +1,192 @@ +import sharp from "sharp"; +import { readBarcodes, type ReaderOptions } from "zxing-wasm/reader"; +import { callVision } from "./vision"; + +const READER_OPTIONS: ReaderOptions = { + tryHarder: true, + tryRotate: true, + tryDownscale: true, + tryDenoise: true, + formats: ["EAN-13", "EAN-8", "UPC-A", "UPC-E", "Code 128"], + maxNumberOfSymbols: 1, +}; + +const ENRICHMENT_PROMPT = `Analyze this food product image. Extract any of the following that are visible: + +1. Product name and brand +2. Nutrition Facts (structured): calories, protein_g, carbs_g, fat_g, fiber_g, sugar_g, sodium_mg, serving_size, serving_unit, servings_per_container +3. Ingredients list (raw text as printed on label) +4. UPC/Barcode number + +Return a single JSON object: +{"name": "...", "brand": "...", "nutrition": {...}, "ingredients_raw": "...", "upc": "..."} +Only include fields you can read from the image. Return ONLY the JSON, no other text.`; + +interface EnrichmentResult { + name?: string; + brand?: string; + nutrition?: Record; + ingredients_raw?: string; + upc?: string; +} + +/** + * Extract product info from a single close-up product photo. + * + * Two-phase approach: + * 1. Barcode detection via zxing-wasm (fast, no AI) + * 2. Vision model extraction for name, brand, nutrition, ingredients + * + * Results are merged — zxing UPC takes priority over vision-detected UPC. + */ +export async function extractProductInfo( + imageBuffer: Buffer +): Promise { + // Phase 1: Fast barcode detection (no AI) + const barcodeUpc = await detectBarcode(imageBuffer); + + // Phase 2: Vision model extraction for nutrition/ingredients/name/brand + const visionResult = await extractViaVision(imageBuffer); + + // Merge: barcode UPC wins over vision-detected UPC + const result: EnrichmentResult = { ...visionResult }; + if (barcodeUpc) { + result.upc = barcodeUpc; + } + + return result; +} + +// --- Barcode detection via zxing-wasm --- + +async function detectBarcode(imageBuffer: Buffer): Promise { + const meta = await sharp(imageBuffer).metadata(); + const width = meta.width || 0; + const height = meta.height || 0; + + // Strategy 1: Full image as PNG + const pngFull = await sharp(imageBuffer).png().toBuffer(); + const result = await tryDecode(pngFull); + if (result) return result; + + // Strategy 2: Enhanced — grayscale + sharpen + high contrast + const pngEnhanced = await sharp(imageBuffer) + .grayscale() + .sharpen({ sigma: 2 }) + .normalize() + .png() + .toBuffer(); + const result2 = await tryDecode(pngEnhanced); + if (result2) return result2; + + // Strategy 3: Crop bottom half (barcodes are usually on the bottom) + if (height > 200) { + const bottomHalf = await sharp(imageBuffer) + .extract({ left: 0, top: Math.floor(height / 2), width, height: Math.floor(height / 2) }) + .sharpen({ sigma: 1.5 }) + .png() + .toBuffer(); + const result3 = await tryDecode(bottomHalf); + if (result3) return result3; + } + + // Strategy 4: Crop bottom-right quadrant + if (width > 200 && height > 200) { + const bottomRight = await sharp(imageBuffer) + .extract({ + left: Math.floor(width / 2), + top: Math.floor(height / 2), + width: Math.floor(width / 2), + height: Math.floor(height / 2), + }) + .sharpen({ sigma: 1.5 }) + .png() + .toBuffer(); + const result4 = await tryDecode(bottomRight); + if (result4) return result4; + } + + return null; +} + +async function tryDecode(pngBuffer: Buffer): Promise { + try { + const results = await readBarcodes(new Uint8Array(pngBuffer), READER_OPTIONS); + const valid = results.filter((r) => r.isValid); + if (valid.length === 0) return null; + + const best = valid[0]; + const upc = best.text.replace(/\D/g, ""); + if (upc.length >= 8 && upc.length <= 14) { + console.log(`Barcode detected: ${upc} (format: ${best.format})`); + return upc; + } + } catch (err) { + console.warn(`Barcode decode error: ${err instanceof Error ? err.message : err}`); + } + return null; +} + +// --- Vision model extraction --- + +/** + * Clean raw model output: strip markdown fences, JS comments, trailing commas. + * Small models often wrap JSON in ```json ... ``` or add // comments. + */ +function cleanJsonText(raw: string): string { + let text = raw; + // Strip markdown code fences + text = text.replace(/```(?:json)?\s*/gi, "").replace(/```/g, ""); + // Strip single-line JS comments (// ...) + text = text.replace(/\/\/[^\n]*/g, ""); + // Strip trailing commas before } or ] + text = text.replace(/,\s*([}\]])/g, "$1"); + return text; +} + +async function extractViaVision(imageBuffer: Buffer): Promise { + const base64 = imageBuffer.toString("base64"); + + let raw: string; + try { + raw = await callVision(ENRICHMENT_PROMPT, base64); + } catch (err) { + console.error("Vision model call failed:", err instanceof Error ? err.message : err); + return {}; + } + + // Clean markdown fences, comments, then find JSON object + const cleaned = cleanJsonText(raw); + const start = cleaned.indexOf("{"); + const end = cleaned.lastIndexOf("}"); + if (start === -1 || end <= start) { + console.warn(`Failed to parse enrichment response: ${raw.slice(0, 200)}`); + return {}; + } + + try { + const data = JSON.parse(cleaned.slice(start, end + 1)) as Record; + const result: EnrichmentResult = {}; + + if (data.name && typeof data.name === "string") { + result.name = data.name.trim(); + } + if (data.brand && typeof data.brand === "string") { + result.brand = data.brand.trim(); + } + if (data.nutrition && typeof data.nutrition === "object" && data.nutrition !== null) { + result.nutrition = data.nutrition as Record; + } + if (data.ingredients_raw && typeof data.ingredients_raw === "string") { + result.ingredients_raw = data.ingredients_raw.trim(); + } + if (data.upc) { + result.upc = String(data.upc).trim(); + } + + return result; + } catch { + console.warn(`Failed to parse enrichment JSON: ${raw.slice(0, 200)}`); + return {}; + } +} diff --git a/src/gemini.ts b/src/gemini.ts new file mode 100644 index 0000000..248325b --- /dev/null +++ b/src/gemini.ts @@ -0,0 +1,80 @@ +import { config } from "./config"; + +let genaiModule: typeof import("@google/generative-ai") | null = null; + +async function getClient() { + if (!config.geminiApiKey) { + throw new Error("GEMINI_API_KEY not configured"); + } + if (!genaiModule) { + genaiModule = await import("@google/generative-ai"); + } + return new genaiModule.GoogleGenerativeAI(config.geminiApiKey); +} + +/** + * Send an image + prompt to Gemini vision. + */ +export async function geminiVision(prompt: string, imageBase64: string): Promise { + const ai = await getClient(); + const model = ai.getGenerativeModel({ model: config.geminiModel }); + + const result = await model.generateContent([ + prompt, + { + inlineData: { + mimeType: "image/jpeg", + data: imageBase64, + }, + }, + ]); + + return result.response.text(); +} + +/** + * Identify ALL food/grocery products visible in a photo. + */ +export async function geminiIdentifyProducts( + imageBase64: string +): Promise[]> { + const prompt = `Identify ALL food and grocery products visible in this photo. \ +There may be one product or many (e.g. a grocery haul, a shelf, a receipt). + +Return ONLY a JSON array of objects. Each object must have: +- "name": product name (string) +- "brand": brand name if visible, otherwise "" (string). Produce typically has no brand. +- "category": one of "produce", "dairy", "meat", "seafood", "bakery", "snacks", "beverages", "frozen", "pantry", "condiments", "other" (string) +- "is_organic": "yes" or "no" based on visible labels (string) + +Return ONLY the JSON array, no other text.`; + + const raw = await geminiVision(prompt, imageBase64); + + try { + const start = raw.indexOf("["); + const end = raw.lastIndexOf("]"); + if (start !== -1 && end > start) { + const items = JSON.parse(raw.slice(start, end + 1)); + return (items as Record[]).filter( + (i) => typeof i === "object" && i !== null && i.name + ); + } + } catch { + // try single object + } + + try { + const start = raw.indexOf("{"); + const end = raw.lastIndexOf("}"); + if (start !== -1 && end > start) { + const item = JSON.parse(raw.slice(start, end + 1)) as Record; + if (item.name) return [item]; + } + } catch { + // unparseable + } + + console.warn(`Failed to parse Gemini product identification: ${raw.slice(0, 200)}`); + return []; +} diff --git a/src/matching.ts b/src/matching.ts new file mode 100644 index 0000000..d091301 --- /dev/null +++ b/src/matching.ts @@ -0,0 +1,67 @@ +import { embed } from "./embeddings"; +import { queryProducts } from "./chroma"; + +export interface MatchedItem extends Record { + matched_product_id: number | null; + match_confidence: number; + reference_image_url: string | null; +} + +/** + * For each item, try to match against ChromaDB product_images embeddings. + * No PostgreSQL — ILIKE fallback happens in the backend. + */ +export async function matchToKnownProducts( + items: Record[] +): Promise { + const results: MatchedItem[] = []; + + for (const item of items) { + const name = String(item.name || ""); + const brand = String(item.brand || ""); + + const matched: MatchedItem = { + ...item, + matched_product_id: null, + match_confidence: 0, + reference_image_url: null, + }; + + if (!name) { + results.push(matched); + continue; + } + + const queryText = [name, brand].filter(Boolean).join(", "); + + try { + const embedding = await embed(queryText); + const chromaResults = await queryProducts([embedding], 3); + + if ( + chromaResults.distances && + chromaResults.distances[0] && + chromaResults.distances[0].length > 0 + ) { + const bestDistance = chromaResults.distances[0][0]; + const confidence = Math.max(0, 1 - bestDistance); + + if (confidence > 0.5 && chromaResults.metadatas?.[0]?.[0]) { + const meta = chromaResults.metadatas[0][0]; + matched.matched_product_id = (meta.product_id as number) || null; + matched.match_confidence = Math.round(confidence * 1000) / 1000; + matched.reference_image_url = (meta.image_url as string) || null; + } + } + } catch (err) { + console.debug( + `ChromaDB match failed for '${name}':`, + err instanceof Error ? err.message : err + ); + } + + results.push(matched); + } + + return results; +} diff --git a/src/pantry.ts b/src/pantry.ts new file mode 100644 index 0000000..2f0a970 --- /dev/null +++ b/src/pantry.ts @@ -0,0 +1,91 @@ +import { tileImage, scanTiles, saveScanImage } from "./tiling"; +import { parseJsonItems, deduplicateItems } from "./parsing"; +import { matchToKnownProducts } from "./matching"; +import { geminiIdentifyProducts } from "./gemini"; + +const PANTRY_PROMPT = `Identify ALL food and grocery products visible in this photo of a pantry, fridge, or kitchen. +For each product, extract: +- Product name (as shown on the label) +- Brand (if visible) +- Category (produce/dairy/meat/seafood/bakery/snacks/beverages/frozen/pantry/condiments/other) +- Approximate quantity (e.g. "2 cans", "1 bottle", "half gallon") + +Return ONLY a JSON array of objects with keys: "name", "brand", "category", "quantity_desc" +Example: [{"name": "Greek Yogurt", "brand": "Fage", "category": "dairy", "quantity_desc": "2 containers"}] +Return ONLY the JSON array, no other text.`; + +const MIN_LOCAL_RESULTS = 2; + +interface PantryItem extends Record { + name: string; + brand: string; + category: string; + quantity_desc: string; +} + +function cleanPantryItems(items: Record[]): PantryItem[] { + const cleaned: PantryItem[] = []; + + for (const item of items) { + if (typeof item !== "object" || item === null) continue; + const name = String(item.name || "").trim(); + if (!name) continue; + + cleaned.push({ + name, + brand: String(item.brand || "").trim(), + category: String(item.category || "other").trim().toLowerCase(), + quantity_desc: String(item.quantity_desc || "1").trim(), + }); + } + + return cleaned; +} + +export async function scanPantryPhoto( + imageBuffer: Buffer +): Promise> { + const { scanId } = saveScanImage(imageBuffer, "pantry"); + let modelUsed = "local"; + + // Step 1: Tile and scan with local model + const tiles = await tileImage(imageBuffer); + const rawTexts = await scanTiles(tiles, PANTRY_PROMPT); + let items: Record[] = cleanPantryItems(parseJsonItems(rawTexts)); + items = deduplicateItems(items); + + // Step 2: Gemini fallback if too few results + if (items.length < MIN_LOCAL_RESULTS) { + console.log(`Local model found ${items.length} pantry items, falling back to Gemini`); + try { + const fullBase64 = imageBuffer.toString("base64"); + const geminiItems = await geminiIdentifyProducts(fullBase64); + const normalized: PantryItem[] = geminiItems.map((gi) => ({ + name: String(gi.name || ""), + brand: String(gi.brand || ""), + category: String(gi.category || "other"), + quantity_desc: "1", + })); + if (normalized.length > items.length) { + items = normalized; + modelUsed = "gemini"; + } + } catch (err) { + console.error("Gemini fallback failed for pantry scan:", err instanceof Error ? err.message : err); + } + } + + // Step 3: Match against known products (ChromaDB only) + const matched = await matchToKnownProducts(items); + + // Add index + const indexed = matched.map((item, i) => ({ ...item, index: i })); + + console.log(`Pantry scan ${scanId}: ${indexed.length} products via ${modelUsed}`); + return { + scan_id: scanId, + items: indexed, + total_found: indexed.length, + model_used: modelUsed, + }; +} diff --git a/src/parsing.ts b/src/parsing.ts new file mode 100644 index 0000000..1a063b0 --- /dev/null +++ b/src/parsing.ts @@ -0,0 +1,84 @@ +/** + * Extract JSON arrays from raw model output texts, merge into single list. + */ +export function parseJsonItems(rawTexts: string[]): Record[] { + const allItems: Record[] = []; + + for (const text of rawTexts) { + // Try array first + try { + const start = text.indexOf("["); + const end = text.lastIndexOf("]"); + if (start !== -1 && end > start) { + const items = JSON.parse(text.slice(start, end + 1)); + if (Array.isArray(items)) { + for (const item of items) { + if (typeof item === "object" && item !== null) { + allItems.push(item as Record); + } + } + continue; + } + } + } catch { + // fall through to single object + } + + // Try single object + try { + const start = text.indexOf("{"); + const end = text.lastIndexOf("}"); + if (start !== -1 && end > start) { + const item = JSON.parse(text.slice(start, end + 1)); + if (typeof item === "object" && item !== null && (item as Record).name) { + allItems.push(item as Record); + } + } + } catch { + // unparseable + } + } + + return allItems; +} + +function tokenSet(name: string): Set { + return new Set(name.toLowerCase().split(/\s+/)); +} + +/** + * Remove near-duplicate items based on token overlap (>80% = duplicate). + */ +export function deduplicateItems( + items: Record[], + key: string = "name" +): Record[] { + const unique: Record[] = []; + + for (const item of items) { + const name = String(item[key] || "").trim(); + if (!name) continue; + + const tokens = tokenSet(name); + let isDup = false; + + for (const existing of unique) { + const existingTokens = tokenSet(String(existing[key] || "")); + if (tokens.size === 0 || existingTokens.size === 0) continue; + + const intersection = new Set([...tokens].filter((t) => existingTokens.has(t))); + const overlap = intersection.size / Math.max(tokens.size, existingTokens.size); + + if (overlap > 0.8) { + isDup = true; + break; + } + } + + if (!isDup) { + unique.push(item); + } + } + + return unique; +} diff --git a/src/server.ts b/src/server.ts new file mode 100644 index 0000000..360631c --- /dev/null +++ b/src/server.ts @@ -0,0 +1,176 @@ +import "dotenv/config"; +import express, { Request, Response, NextFunction } from "express"; +import multer from "multer"; +import { config } from "./config"; +import { scanShelfPhoto } from "./shelf"; +import { scanPantryPhoto } from "./pantry"; +import { extractProductInfo } from "./enrich"; +import { getCount } from "./chroma"; + +const ALLOWED_MIMES = new Set([ + "image/jpeg", + "image/png", + "image/webp", + "image/gif", + "image/heic", + "image/heif", +]); + +const app = express(); + +const upload = multer({ + storage: multer.memoryStorage(), + limits: { + fileSize: config.maxFileSize, + files: 1, + }, + fileFilter: (_req, file, cb) => { + if (ALLOWED_MIMES.has(file.mimetype)) { + cb(null, true); + } else { + cb(new Error("Invalid file type. Allowed: JPEG, PNG, WebP, GIF, HEIC, HEIF")); + } + }, +}); + +// --- Health check --- + +app.get("/health", async (_req: Request, res: Response) => { + const status: Record = { + status: "ok", + vision_model: config.visionAiModel, + }; + + // Check vision model + try { + const visionUrl = config.visionAiUrl.replace("/v1/chat/completions", "/v1/models"); + const resp = await fetch(visionUrl, { signal: AbortSignal.timeout(5000) }); + status.vision = resp.ok ? "connected" : `status ${resp.status}`; + } catch (err) { + status.vision = `unreachable: ${err instanceof Error ? err.message : err}`; + status.status = "degraded"; + } + + // Check Ollama + try { + const resp = await fetch(`${config.ollamaHost}/api/tags`, { + signal: AbortSignal.timeout(5000), + }); + status.ollama = resp.ok ? "connected" : `status ${resp.status}`; + } catch (err) { + status.ollama = `unreachable: ${err instanceof Error ? err.message : err}`; + status.status = "degraded"; + } + + // Check ChromaDB + try { + const count = await getCount(); + status.chroma = "connected"; + status.chroma_count = count; + } catch (err) { + status.chroma = `unreachable: ${err instanceof Error ? err.message : err}`; + status.status = "degraded"; + } + + res.json(status); +}); + +// --- Scan endpoints --- + +app.post( + "/scan/shelf", + upload.single("image"), + async (req: Request, res: Response, next: NextFunction) => { + try { + const file = req.file; + if (!file) { + res.status(400).json({ error: "No image provided" }); + return; + } + + const storeName = req.body?.store_name; + if (!storeName) { + res.status(400).json({ error: "store_name is required" }); + return; + } + + const result = await scanShelfPhoto(file.buffer, storeName); + res.json(result); + } catch (err) { + next(err); + } + } +); + +app.post( + "/scan/pantry", + upload.single("image"), + async (req: Request, res: Response, next: NextFunction) => { + try { + const file = req.file; + if (!file) { + res.status(400).json({ error: "No image provided" }); + return; + } + + const result = await scanPantryPhoto(file.buffer); + res.json(result); + } catch (err) { + next(err); + } + } +); + +// --- Product enrichment --- + +app.post( + "/enrich/product", + upload.single("image"), + async (req: Request, res: Response, next: NextFunction) => { + try { + const file = req.file; + if (!file) { + res.status(400).json({ error: "No image provided" }); + return; + } + + const result = await extractProductInfo(file.buffer); + res.json(result); + } catch (err) { + next(err); + } + } +); + +// --- 404 --- + +app.use((_req: Request, res: Response) => { + res.status(404).json({ error: "Not found" }); +}); + +// --- Error handler --- + +app.use((err: Error, _req: Request, res: Response, _next: NextFunction) => { + if (err instanceof multer.MulterError && err.code === "LIMIT_FILE_SIZE") { + res.status(413).json({ error: `File too large. Maximum size is ${config.maxFileSize / 1024 / 1024}MB.` }); + return; + } + + if (err.message.startsWith("Invalid file type")) { + res.status(400).json({ error: err.message }); + return; + } + + console.error("Scan error:", err.message); + res.status(502).json({ error: "Failed to process image" }); +}); + +// --- Start --- + +app.listen(config.port, "0.0.0.0", () => { + console.log(`Vision Scanner Service listening on port ${config.port}`); + console.log(`Vision AI: ${config.visionAiUrl} (model: ${config.visionAiModel})`); + console.log(`Ollama: ${config.ollamaHost} (embed: ${config.ollamaEmbedModel})`); + console.log(`ChromaDB: ${config.chromaHost}`); +}); + diff --git a/src/shelf.ts b/src/shelf.ts new file mode 100644 index 0000000..29c71ec --- /dev/null +++ b/src/shelf.ts @@ -0,0 +1,103 @@ +import { tileImage, scanTiles, saveScanImage } from "./tiling"; +import { parseJsonItems, deduplicateItems } from "./parsing"; +import { matchToKnownProducts } from "./matching"; +import { geminiVision } from "./gemini"; + +const SHELF_PROMPT = `Look at this photo of a store shelf or price display. For each product visible, extract: +- Product name (as shown on the label/tag) +- Price (the number on the price tag) +- Size/weight if visible (e.g. "32 oz", "5 lb", "1 gal") +- Unit price if shown on the tag (e.g. "$0.25/oz") + +Return ONLY a JSON array of objects with keys: "name", "price", "quantity", "unit", "unit_price" +Example: [{"name": "Kirkland Organic Eggs", "price": 7.99, "quantity": 24, "unit": "ct", "unit_price": null}] +Return ONLY the JSON array, no other text.`; + +const GEMINI_SHELF_PROMPT = `Look at this photo of a store shelf or price display. For each product you can see, extract: +- Product name (as shown on the label/tag) +- Price (the number on the price tag) +- Size/weight if visible (e.g. "32 oz", "5 lb", "1 gal") +- Unit price if shown on the tag (e.g. "$0.25/oz") + +Return ONLY a JSON array of objects with keys: "name", "price", "quantity", "unit", "unit_price" +Be precise with numbers. Return ONLY the JSON array, no other text.`; + +const MIN_LOCAL_RESULTS = 2; + +interface ShelfItem extends Record { + name: string; + price: number; + quantity: number; + unit: string; + unit_price: number | null; + store_name: string; +} + +function cleanShelfItems( + items: Record[], + storeName: string +): ShelfItem[] { + const cleaned: ShelfItem[] = []; + + for (const item of items) { + if (typeof item !== "object" || item === null) continue; + const name = String(item.name || "").trim(); + const price = Number(item.price); + if (!name || isNaN(price) || price <= 0) continue; + + cleaned.push({ + name, + price, + quantity: Number(item.quantity) || 1, + unit: String(item.unit || "unit"), + unit_price: item.unit_price ? Number(item.unit_price) : null, + store_name: storeName, + }); + } + + return cleaned; +} + +export async function scanShelfPhoto( + imageBuffer: Buffer, + storeName: string +): Promise> { + const { scanId } = saveScanImage(imageBuffer, "shelf"); + let modelUsed = "local"; + + // Step 1: Tile and scan with local model + const tiles = await tileImage(imageBuffer); + const rawTexts = await scanTiles(tiles, SHELF_PROMPT); + let items: Record[] = cleanShelfItems(parseJsonItems(rawTexts), storeName); + items = deduplicateItems(items); + + // Step 2: Gemini fallback if too few results + if (items.length < MIN_LOCAL_RESULTS) { + console.log(`Local model found ${items.length} items, falling back to Gemini`); + try { + const fullBase64 = imageBuffer.toString("base64"); + const raw = await geminiVision(GEMINI_SHELF_PROMPT, fullBase64); + const geminiItems = cleanShelfItems(parseJsonItems([raw]), storeName); + if (geminiItems.length > items.length) { + items = geminiItems; + modelUsed = "gemini"; + } + } catch (err) { + console.error("Gemini fallback failed for shelf scan:", err instanceof Error ? err.message : err); + } + } + + // Step 3: Match against known products (ChromaDB only) + const matched = await matchToKnownProducts(items); + + // Add index + const indexed = matched.map((item, i) => ({ ...item, index: i })); + + console.log(`Shelf scan ${scanId}: ${indexed.length} products via ${modelUsed}`); + return { + scan_id: scanId, + items: indexed, + total_found: indexed.length, + model_used: modelUsed, + }; +} diff --git a/src/tiling.ts b/src/tiling.ts new file mode 100644 index 0000000..57a5871 --- /dev/null +++ b/src/tiling.ts @@ -0,0 +1,100 @@ +import sharp from "sharp"; +import * as fs from "fs"; +import * as path from "path"; +import * as crypto from "crypto"; +import { config } from "./config"; +import { callVision } from "./vision"; + +export interface TileResult { + buffer: Buffer; + base64: string; +} + +/** + * Split image into grid tiles with overlap, returning JPEG buffers. + */ +export async function tileImage( + imageBuffer: Buffer, + grid: [number, number] = [2, 2], + overlap: number = 0.10 +): Promise { + const metadata = await sharp(imageBuffer).metadata(); + const w = metadata.width || 0; + const h = metadata.height || 0; + + if (w === 0 || h === 0) return []; + + const [cols, rows] = grid; + const tileW = Math.floor(w / cols); + const tileH = Math.floor(h / rows); + const overlapX = Math.floor(tileW * overlap); + const overlapY = Math.floor(tileH * overlap); + + const tilePromises: Promise[] = []; + + for (let row = 0; row < rows; row++) { + for (let col = 0; col < cols; col++) { + const x1 = Math.max(0, col * tileW - overlapX); + const y1 = Math.max(0, row * tileH - overlapY); + const x2 = Math.min(w, (col + 1) * tileW + overlapX); + const y2 = Math.min(h, (row + 1) * tileH + overlapY); + + tilePromises.push( + sharp(imageBuffer) + .extract({ left: x1, top: y1, width: x2 - x1, height: y2 - y1 }) + .jpeg({ quality: 85 }) + .toBuffer() + .then((buffer) => ({ + buffer, + base64: buffer.toString("base64"), + })) + ); + } + } + + return Promise.all(tilePromises); +} + +/** + * Send all tiles to vision model in parallel (throttled), return raw text per tile. + */ +export async function scanTiles( + tiles: TileResult[], + prompt: string +): Promise { + const maxConcurrent = config.maxConcurrentTiles; + const results: string[] = []; + + // Process in batches to throttle GPU usage + for (let i = 0; i < tiles.length; i += maxConcurrent) { + const batch = tiles.slice(i, i + maxConcurrent); + const batchResults = await Promise.all( + batch.map(async (tile) => { + try { + return await callVision(prompt, tile.base64); + } catch (err) { + console.warn("Vision tile scan failed:", err instanceof Error ? err.message : err); + return ""; + } + }) + ); + results.push(...batchResults); + } + + return results.filter((r) => r.trim().length > 0); +} + +/** + * Save uploaded image to disk. Returns scan_id and file_path. + */ +export function saveScanImage( + imageBuffer: Buffer, + scanType: string +): { scanId: string; filePath: string } { + const scanId = crypto.randomUUID().slice(0, 12); + const dirPath = path.join(config.uploadDir, scanType); + fs.mkdirSync(dirPath, { recursive: true }); + const filePath = path.join(dirPath, `${scanId}.jpg`); + fs.writeFileSync(filePath, imageBuffer); + return { scanId, filePath }; +} diff --git a/src/vision.ts b/src/vision.ts new file mode 100644 index 0000000..0e073cb --- /dev/null +++ b/src/vision.ts @@ -0,0 +1,46 @@ +import { config } from "./config"; + +/** + * Call local vision model via OpenAI-compatible API. + */ +export async function callVision(prompt: string, imageBase64: string): Promise { + const controller = new AbortController(); + const timeout = setTimeout(() => controller.abort(), config.visionAiTimeout); + + try { + const response = await fetch(config.visionAiUrl, { + method: "POST", + headers: { "Content-Type": "application/json" }, + signal: controller.signal, + body: JSON.stringify({ + model: config.visionAiModel, + messages: [ + { + role: "user", + content: [ + { type: "text", text: prompt }, + { + type: "image_url", + image_url: { + url: `data:image/jpeg;base64,${imageBase64}`, + }, + }, + ], + }, + ], + max_tokens: 2048, + }), + }); + + if (!response.ok) { + throw new Error(`Vision AI returned ${response.status}`); + } + + const data = (await response.json()) as { + choices?: { message?: { content?: string } }[]; + }; + return data.choices?.[0]?.message?.content?.trim() || ""; + } finally { + clearTimeout(timeout); + } +} diff --git a/tsconfig.json b/tsconfig.json new file mode 100644 index 0000000..898907e --- /dev/null +++ b/tsconfig.json @@ -0,0 +1,17 @@ +{ + "compilerOptions": { + "target": "ES2022", + "module": "commonjs", + "lib": ["ES2022"], + "outDir": "dist", + "rootDir": "src", + "strict": true, + "esModuleInterop": true, + "skipLibCheck": true, + "forceConsistentCasingInFileNames": true, + "resolveJsonModule": true, + "declaration": true + }, + "include": ["src"], + "exclude": ["node_modules", "dist"] +}