{"version":3,"sources":["../src/index.ts","../src/functions.ts","../src/convert.ts"],"sourcesContent":["export { classifyDocument } from './functions';\nexport type { documentDictionary } from './types';\n\n\n","import { stringSimilarity } from \"string-similarity-js\";\nimport * as Tesseract from \"tesseract.js\";\nimport type { documentDictionary } from \"./types\";\nimport { convert } from \"./convert\";\n\nexport function findTargetWords(\n documentText: string,\n targetWords: string[],\n threshold: number = 0.75\n): string[] {\n const foundTargetWords: string[] = [];\n const documentWords = documentText.split(/\\s+/);\n\n documentWords.forEach((docWord) => {\n targetWords.forEach((targetWord) => {\n const similarity = stringSimilarity(\n docWord.toLowerCase(),\n targetWord.toLowerCase()\n );\n if (similarity >= threshold) {\n foundTargetWords.push(targetWord);\n }\n });\n });\n\n return foundTargetWords;\n}\n\nconst defaultDocumentDictionary: documentDictionary = {\n Militærbevis: [\n [\"førstegangstjeneste\", \"bevis\", \"avtjent\"],\n [\"attest\", \"førstegangstjeneste\"],\n [\"fullført\", \"førstegangstjeneste\"],\n ],\n Politiattest: [[\"politiattest\", \"politidistrikt\"], [\"police certificate\"]],\n Kompetansebevis: [[\"omfatter\", \"opplæring\", \"utdanningsprogram\"]],\n Legeerklæring: [[\"legeerklæring\", \"fødselsnummer\"]],\n Bostedsattest: [\n [\"registrerte\", \"opplysninger\", \"folkeregisteret\"],\n [\"bostedsattest\", \"bostedsadresse\", \"registrert\"],\n [\"registrert\", \"adressehistorikk\", \"folkeregisteret\"],\n ],\n};\n\ntype ClassifyDocumentOptions = {\n onProgress?: (progress: number) => void;\n customDocumentDictionary?: documentDictionary;\n maxNumPages?: number;\n};\n\nexport async function classifyDocument(\n file: File,\n options: ClassifyDocumentOptions = {}\n): Promise<{ classification: string; text: string }> {\n const {\n onProgress,\n customDocumentDictionary,\n maxNumPages = Infinity, // Default value if not provided\n } = options;\n\n // Merge the default dictionary with a custom one, if provided\n const documentDictionary = {\n ...defaultDocumentDictionary,\n ...customDocumentDictionary,\n };\n\n let BlobArray: Blob[] = [];\n\n if (file.type === \"application/pdf\") {\n const pdfPath = URL.createObjectURL(file);\n BlobArray = await convert(pdfPath, maxNumPages);\n console.log(\"Blob to read set by convert function\");\n } else if (file.type.startsWith(\"image/\")) {\n // Check for image type explicitly\n BlobArray.push(file);\n } else {\n throw new Error(\"Unsupported file type. Please provide a PDF or an image.\");\n }\n\n let progress = 0;\n const totalBlobs = Math.min(BlobArray.length, maxNumPages);\n let fullText = \"\";\n const targetWords = Array.from(\n new Set(Object.values(documentDictionary).flat(2))\n );\n try {\n for (let i = 0; i < totalBlobs; i++) {\n const text = await ocrBlob(BlobArray[i]);\n fullText += text;\n progress = Math.round(((i + 1) / totalBlobs) * 100);\n if (onProgress) {\n onProgress(progress);\n }\n\n const targetWordsFound = findTargetWords(text, targetWords);\n const classification = determineClassification(\n targetWordsFound,\n documentDictionary\n );\n if (classification !== \"Ukjent\") {\n return { classification, text: fullText };\n }\n }\n } catch (error) {\n console.log(\"Error in ocr process: \", error);\n }\n\n return { classification: \"Ukjent\", text: fullText };\n}\n\nfunction determineClassification(\n targetWordsFound: string[],\n documentDictionary: documentDictionary\n): string {\n for (const [classification, targetWordSets] of Object.entries(\n documentDictionary\n )) {\n for (const targetWords of targetWordSets) {\n if (targetWords.every((word) => targetWordsFound.includes(word))) {\n return classification;\n }\n }\n }\n return \"Ukjent\";\n}\n\nasync function ocrBlob(\n blob: Blob,\n onProgress?: (progress: number) => void\n): Promise {\n const reader = new FileReader();\n const worker = await Tesseract.createWorker([\"nor\", \"eng\"], 1, {\n logger: (m) => {\n if (m.status === \"recognizing text\" && onProgress) {\n onProgress(m.progress * 100);\n }\n },\n });\n await worker.setParameters({\n tessedit_pageseg_mode: Tesseract.PSM.AUTO_OSD,\n });\n return new Promise((resolve, reject) => {\n reader.onload = async () => {\n const imageDataUrl = reader.result as string;\n try {\n const {\n data: { text },\n } = await worker.recognize(imageDataUrl, {\n rotateAuto: true,\n });\n resolve(text);\n } catch (err) {\n console.error(err);\n reject(err);\n }\n };\n reader.readAsDataURL(blob);\n });\n}\n","import * as pdfjsLib from \"pdfjs-dist\";\n\npdfjsLib.GlobalWorkerOptions.workerSrc =\n \"https://cdn.jsdelivr.net/npm/pdfjs-dist@4.4.168/build/pdf.worker.min.mjs\";\n\nexport async function convert(pdfPath: string, maxNumPages: number): Promise {\n try {\n const loadingTask = pdfjsLib.getDocument(pdfPath);\n const doc = await loadingTask.promise;\n const totalPages = Math.min(doc.numPages, maxNumPages);\n let blobArray: Blob[] = [];\n\n for (let i = 1; 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