@react-lib-tech/react-text-extractor/dist/index.mjs.map

A tiny React component library (JS)

{"version":3,"sources":["../src/utils/scanProject.jsx","../src/utils/extractTextFromAst.jsx"],"sourcesContent":["// scanProject.js\nimport fs from \"fs\";\nimport path from \"path\";\nimport { pathToFileURL } from \"url\";\nimport { JSDOM } from \"jsdom\";\nimport React from \"react\";\nimport { renderToString } from \"react-dom/server\";\nimport OpenAI from \"openai\";\nimport { extractTextFromAst } from \"./extractTextFromAst\"; // keep your AST fallback\n\n// ✅ Load .gitignore\nfunction loadGitignore(rootPath) {\n  const gitignorePath = path.join(rootPath, \".gitignore\");\n  if (!fs.existsSync(gitignorePath)) return [];\n  const content = fs.readFileSync(gitignorePath, \"utf8\");\n  return content\n    .split(\"\\n\")\n    .map((line) => line.trim())\n    .filter((line) => line && !line.startsWith(\"#\"));\n}\n\n// ✅ Skip rules helper\nfunction shouldSkip(value, skipWord = []) {\n  if (!value) return true;\n  const cleaned = value.trim();\n  const lower = cleaned.toLowerCase();\n\n  const htmlTags = [\n    \"html\",\"head\",\"body\",\"div\",\"span\",\"p\",\"a\",\"img\",\"br\",\"hr\",\"ul\",\"ol\",\"li\",\n    \"table\",\"tr\",\"td\",\"th\",\"thead\",\"tbody\",\"tfoot\",\"form\",\"input\",\"button\",\n    \"select\",\"option\",\"textarea\",\"label\",\"script\",\"link\",\"meta\",\"style\",\n    \"h1\",\"h2\",\"h3\",\"h4\",\"h5\",\"h6\",\"nav\",\"header\",\"footer\",\"section\",\"article\",\n    \"aside\",\"main\",\"figure\",\"figcaption\",\"iframe\",\"canvas\",\"video\",\"audio\",\n    ...skipWord\n  ];\n\n  return (\n    !cleaned ||\n    htmlTags.includes(lower) ||\n    /^\\d+$/.test(cleaned) ||          // pure numbers\n    /^\\d+%$/.test(cleaned) ||         // percentages\n    /^#[0-9a-f]{3,6}$/i.test(cleaned) || // hex colors\n    /^[^a-zA-Z0-9]+$/.test(cleaned)   // only special chars\n  );\n}\n\n// ✅ Render a React component\nfunction extractTextJsdom(Component, file) {\n  try {\n    if (typeof Component !== \"function\") return null;\n\n    const element = React.createElement(Component);\n    const htmlString = renderToString(element);\n    const dom = new JSDOM(htmlString);\n    const document = dom.window.document;\n\n    let texts = [];\n\n    const bodyText = document.body.textContent.replace(/\\s+/g, \" \").trim();\n    if (bodyText) texts.push(bodyText);\n\n    const selectors = [\"placeholder\", \"title\", \"alt\", \"aria-label\"];\n    const allElements = document.querySelectorAll(\"*\");\n    allElements.forEach((el) => {\n      selectors.forEach((attr) => {\n        if (el.hasAttribute(attr)) {\n          const val = el.getAttribute(attr).trim();\n          if (val) texts.push(val);\n        }\n      });\n    });\n\n    return texts.length ? texts : null;\n  } catch (err) {\n    console.warn(`⚠️ Error rendering ${file}: ${err.message}`);\n    return null;\n  }\n}\n\n// ✅ Recursive walker\nasync function walkDir(folderPath, skipList = []) {\n  const results = [];\n  const entries = fs.readdirSync(folderPath);\n\n  for (const entry of entries) {\n    const fullPath = path.join(folderPath, entry);\n    const stats = fs.statSync(fullPath);\n    const tempPath = folderPath.replace(/\\\\/g, \"/\");\n\n    if (skipList.some((pattern) => tempPath.includes(pattern))) continue;\n\n    if (stats.isDirectory()) {\n      results.push(...(await walkDir(fullPath, skipList)));\n    } else if (stats.isFile() && (entry.endsWith(\".js\") || entry.endsWith(\".jsx\"))) {\n      let textArr = null;\n      try {\n        const moduleUrl = pathToFileURL(fullPath).href;\n        const imported = await import(moduleUrl);\n        const Component = imported.default || imported;\n        textArr = extractTextJsdom(Component, fullPath);\n      } catch {\n        textArr = extractTextFromAst(fullPath);\n      }\n      if (textArr) results.push({ file: fullPath, text: textArr });\n    }\n  }\n\n  return results;\n}\n\n// ✅ OpenAI helper\nasync function applyOpenAI(texts, {\n  userPrompt,\n  model = \"gpt-4o-mini\",\n  temperature = 0.3,\n  systemPrompt = \"You are a text transformation assistant.\",\n  apiKey\n}) {\n  if (!userPrompt) return texts;\n\n  const key = apiKey || process.env.OPENAI_API_KEY;\n  if (!key) {\n    console.error(\"❌ Missing OpenAI API key\");\n    return texts;\n  }\n\n  try {\n    const response = await fetch(\"https://api.openai.com/v1/chat/completions\", {\n      method: \"POST\",\n      headers: {\n        \"Content-Type\": \"application/json\",\n        \"Authorization\": `Bearer ${key}`\n      },\n      body: JSON.stringify({\n        model,\n        messages: [\n          { role: \"system\", content: systemPrompt },\n          { role: \"user\", content: `${userPrompt}\\n\\nHere is the text:\\n${texts.join(\"\\n\")}` }\n        ],\n        temperature\n      })\n    });\n\n    if (!response.ok) {\n      throw new Error(`HTTP ${response.status} ${response.statusText}`);\n    }\n\n    const json = await response.json();\n    const out = json.choices[0]?.message?.content?.trim();\n    return out ? out.split(\"\\n\").map(t => t.trim()).filter(Boolean) : texts;\n\n  } catch (err) {\n    console.error(\"⚠️ REST API error:\", err.message);\n    return texts;\n  }\n}\n\n\n// ✅ Main extractor\nexport async function ReactTextExtractor({\n  rootPath,\n  skipWord = [],\n  returnBoth = true,   // return raw + filtered\n  applyFilter = true,  // if false, returns raw only\n\n  // AI options\n  prompt = null,\n  aiModel = \"gpt-4o-mini\",\n  aiTemperature = 0.3,\n  aiSystemPrompt = \"You are a text transformation assistant.\",\n  openaiApiKey = null\n}) {\n  const gitignorePatterns = loadGitignore(rootPath);\n  const data = await walkDir(rootPath, gitignorePatterns);\n  const allTexts = data?.map((item) => item?.text?.textContent).flat().filter(Boolean) || [];\n\n  const rawUnique = [];\n  const seenRaw = new Set();\n\n  const filteredUnique = [];\n  const seenFiltered = new Set();\n\n  allTexts.forEach((item) => {\n    let cleaned = item.trim();\n    const key = cleaned.toLowerCase();\n\n    // raw collection\n    if (!seenRaw.has(key)) {\n      seenRaw.add(key);\n      rawUnique.push(cleaned);\n    }\n\n    // filtered collection\n    if (!shouldSkip(cleaned, skipWord) && !seenFiltered.has(key)) {\n      seenFiltered.add(key);\n      filteredUnique.push(cleaned);\n    }\n  });\n\n  let result = returnBoth ? { raw: rawUnique, filtered: filteredUnique }\n              : applyFilter ? filteredUnique\n              : rawUnique;\n\n  // ✅ Apply OpenAI transformation if requested\n  if (prompt) {\n    const aiOptions = {\n      userPrompt: prompt,\n      model: aiModel,\n      temperature: aiTemperature,\n      systemPrompt: aiSystemPrompt,\n      apiKey: openaiApiKey\n    };\n\n    if (returnBoth) {\n      result = {\n        raw: await applyOpenAI(rawUnique, aiOptions),\n        filtered: await applyOpenAI(filteredUnique, aiOptions),\n      };\n    } else {\n      result = await applyOpenAI(result, aiOptions);\n    }\n  }\n\n  return 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