[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_153":154,"_155":156},"routes/_app.x.$handle.$name",{"_95":94,"_32":45,"_103":85,"_27":53,"_13":14,"_104":-5,"_11":105,"_106":107,"_108":109,"_110":111,"_112":113,"_20":-5,"_114":-5,"_115":-5,"_116":25,"_117":118,"_119":120,"_121":-5,"_122":-5,"_123":124},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_43":44,"_32":45,"_38":46,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":53,"_54":-5,"_18":55,"_56":-5,"_57":25,"_58":-5,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":78,"_79":80,"_81":82,"_83":25},"version",16,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":37,"_38":39,"_40":14,"_41":42,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","main.ts","latestPath","fileId","1294763c-7958-11f1-8900-1607ee4eb77e","name","fileType","http","content","import { OpenAI } from \"https://esm.town/v/std/openai\";\n\nfunction toBase64(bytes: Uint8Array): string {\n let binary = \"\";\n const chunk = 0x8000;\n for (let i = 0; i < bytes.length; i += chunk) {\n binary += String.fromCharCode.apply(\n null,\n bytes.subarray(i, i + chunk) as unknown as number[],\n );\n }\n return btoa(binary);\n}\n\nconst HTML = /* html */ `\n\n\n\n\n截图转可编辑表格 → 复制到 PPT / 导出 Excel\n\n\n\n\n

截图转可编辑表格

粘贴表格截图 → 识别（支持合并单元格）→ 编辑 → 复制到 PPT / 导出 Excel

\n\n

\n 点击此处后按 Ctrl/Cmd + V 粘贴截图，或把图片拖进来\n

\n\n

$\"预览\"$

\n\n \n\n

\n 提示：识别结果可直接在表格里编辑；「复制到 PPT」会保留微软雅黑+黑色边框+合并单元格。
\n 「导出 Excel」会保留数据与合并单元格（Excel 字体/边框可在 Excel 中一键套用样式）。\n

\n\n\n\n`;\n\nexport default async function (req: Request): Promise {\n if (req.method === \"POST\") {\n console.log(\n \"STEP1 收到POST | content-type:\",\n req.headers.get(\"content-type\"),\n \"| content-length:\",\n req.headers.get(\"content-length\"),\n );\n try {\n const form = await req.formData();\n console.log(\"STEP2 formData 解析完成\");\n const file = form.get(\"image\");\n console.log(\n \"STEP3 file是File?\",\n file instanceof File,\n \"| size:\",\n file instanceof File ? file.size : \"-\",\n );\n if (!(file instanceof File)) {\n return Response.json({ error: \"缺少图片数据\" }, { status: 400 });\n }\n\n const bytes = new Uint8Array(await file.arrayBuffer());\n console.log(\"STEP4 图片字节数:\", bytes.length);\n const dataUrl = \"data:\" + (file.type || \"image/jpeg\") + \";base64,\" +\n toBase64(bytes);\n console.log(\"STEP5 base64长度:\", dataUrl.length, \"，开始调用 OpenAI\");\n\n const openai = new OpenAI();\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n messages: [\n {\n role: \"system\",\n content:\n \"你是一个表格识别助手。用户会给你一张表格截图，请精确提取其中的表格内容，并识别合并单元格。\\\\n\" +\n \"只返回 JSON，格式为：\\\\n\" +\n '{\"rows\": [[\"A\",\"B\"],[\"C\",\"D\"]], \"merges\": [{\"s\":{\"r\":0,\"c\":0},\"e\":{\"r\":0,\"c\":1}}]}\\\\n' +\n \"规则：\\\\n\" +\n \"1. rows 是二维数组，每个内层数组是一行，元素按列顺序排列，所有行的列数必须一致，空单元格用空字符串。\\\\n\" +\n \"2. merges 描述合并单元格：s 是左上角坐标，e 是右下角坐标，行列均从 0 开始计数。没有合并则为空数组 []。\\\\n\" +\n \"3. 对于被合并覆盖的单元格，文本只放在其左上角对应的位置，被覆盖的其他位置在 rows 中填空字符串。\\\\n\" +\n \"不要输出任何解释或 markdown，只输出 JSON。\",\n },\n {\n role: \"user\",\n content: [\n {\n type: \"text\",\n text: \"请提取这张图片中的表格（含合并单元格）：\",\n },\n { type: \"image_url\", image_url: { url: dataUrl } },\n ],\n },\n ],\n response_format: { type: \"json_object\" },\n max_tokens: 16000,\n });\n\n const finishReason = completion.choices[0]?.finish_reason;\n let raw = (completion.choices[0]?.message?.content ?? \"{}\").trim();\n raw = raw.replace(/^\\`\\`\\`json/i, \"\").replace(/^\\`\\`\\`/, \"\").replace(\n /\\`\\`\\`$/,\n \"\",\n ).trim();\n console.log(\n \"STEP6 OpenAI返回 | finish_reason:\",\n finishReason,\n \"| 输出长度:\",\n raw.length,\n );\n\n let parsed;\n try {\n parsed = JSON.parse(raw);\n } catch (_) {\n const hint = finishReason === \"length\"\n ? \"表格内容过多，模型输出超长被截断，请把大表格分成上下两部分分别截图识别。\"\n : \"模型返回的数据不是合法JSON。\";\n return Response.json(\n { error: hint + \" 结尾片段：\" + raw.slice(-120) },\n { status: 500 },\n );\n }\n return Response.json({\n rows: parsed.rows ?? [],\n merges: parsed.merges ?? [],\n });\n } catch (e) {\n console.error(\"CATCH 识别出错:\", e);\n return Response.json({ error: String(e) }, { status: 500 });\n }\n }\n\n const u = new URL(req.url);\n return new Response(HTML.replace(\"__ENDPOINT__\", u.origin + u.pathname), {\n headers: { \"Content-Type\": \"text/html; charset=utf-8\" },\n });\n}","import { OpenAI } from \"https://esm.town/v/std/openai\";\n\nfunction toBase64(bytes: Uint8Array): string {\n let binary = \"\";\n const chunk = 0x8000;\n for (let i = 0; i < bytes.length; i += chunk) {\n binary += String.fromCharCode.apply(null, bytes.subarray(i, i + chunk) as unknown as number[]);\n }\n return btoa(binary);\n}\n\nconst HTML = /* html */ `\n\n\n\n\n截图转可编辑表格 → 复制到 PPT / 导出 Excel\n\n\n\n\n

截图转可编辑表格

粘贴表格截图 → 识别（支持合并单元格）→ 编辑 → 复制到 PPT / 导出 Excel

\n\n

\n 点击此处后按 Ctrl/Cmd + V 粘贴截图，或把图片拖进来\n

\n\n

$\"预览\"$

\n\n \n\n

\n\n\n\n`;\n\nexport default async function (req: Request): Promise {\n if (req.method === \"POST\") {\n console.log(\"STEP1 收到POST | content-type:\", req.headers.get(\"content-type\"), \"| content-length:\", req.headers.get(\"content-length\"));\n try {\n const form = await req.formData();\n console.log(\"STEP2 formData 解析完成\");\n const file = form.get(\"image\");\n console.log(\"STEP3 file是File?\", file instanceof File, \"| size:\", file instanceof File ? file.size : \"-\");\n if (!(file instanceof File)) return Response.json({ error: \"缺少图片数据\" }, { status: 400 });\n\n const bytes = new Uint8Array(await file.arrayBuffer());\n console.log(\"STEP4 图片字节数:\", bytes.length);\n const dataUrl = \"data:\" + (file.type || \"image/jpeg\") + \";base64,\" + toBase64(bytes);\n console.log(\"STEP5 base64长度:\", dataUrl.length, \"，开始调用 OpenAI\");\n\n const openai = new OpenAI();\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o-mini\",\n messages: [\n {\n role: \"system\",\n content:\n \"你是一个表格识别助手。用户会给你一张表格截图，请精确提取其中的表格内容，并识别合并单元格。\\\\n\" +\n \"只返回 JSON，格式为：\\\\n\" +\n '{\"rows\": [[\"A\",\"B\"],[\"C\",\"D\"]], \"merges\": [{\"s\":{\"r\":0,\"c\":0},\"e\":{\"r\":0,\"c\":1}}]}\\\\n' +\n \"规则：\\\\n\" +\n \"1. rows 是二维数组，每个内层数组是一行，元素按列顺序排列，所有行的列数必须一致，空单元格用空字符串。\\\\n\" +\n \"2. merges 描述合并单元格：s 是左上角坐标，e 是右下角坐标，行列均从 0 开始计数。没有合并则为空数组 []。\\\\n\" +\n \"3. 对于被合并覆盖的单元格，文本只放在其左上角对应的位置，被覆盖的其他位置在 rows 中填空字符串。\\\\n\" +\n \"不要输出任何解释或 markdown，只输出 JSON。\",\n },\n {\n role: \"user\",\n content: [\n { type: \"text\", text: \"请提取这张图片中的表格（含合并单元格）：\" },\n { type: \"image_url\", image_url: { url: dataUrl } },\n ],\n },\n ],\n response_format: { type: \"json_object\" },\n max_tokens: 16000,\n });\n\n const finishReason = completion.choices[0]?.finish_reason;\n let raw = (completion.choices[0]?.message?.content ?? \"{}\").trim();\n raw = raw.replace(/^\\`\\`\\`json/i, \"\").replace(/^\\`\\`\\`/, \"\").replace(/\\`\\`\\`$/, \"\").trim();\n console.log(\"STEP6 OpenAI返回 | finish_reason:\", finishReason, \"| 输出长度:\", raw.length);\n\n let parsed;\n try {\n parsed = JSON.parse(raw);\n } catch (_) {\n const hint = finishReason === \"length\"\n ? \"表格内容过多，模型输出超长被截断，请把大表格分成上下两部分分别截图识别。\"\n : \"模型返回的数据不是合法JSON。\";\n return Response.json({ error: hint + \" 结尾片段：\" + raw.slice(-120) }, { status: 500 });\n }\n return Response.json({ rows: parsed.rows ?? [], merges: parsed.merges ?? [] });\n } catch (e) {\n console.error(\"CATCH 识别出错:\", e);\n return Response.json({ error: String(e) }, { status: 500 });\n }\n }\n\n const u = new URL(req.url);\n return new Response(HTML.replace(\"__ENDPOINT__\", u.origin + u.pathname), {\n headers: { \"Content-Type\": \"text/html; charset=utf-8\" },\n });\n}","createdAt",["D",1784181189189],"branchVersion","branchId","fe0d3337-0dc5-422d-96a8-879efd307099","id","f267c0bd-eb7a-491f-9b6b-21ff6b9d1736","image-to-table",["D",1783355460069],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","","imageUrl","96c2793d-57c8-41d2-a49a-d1ee82f9dd24","pinnedFileId","pinPreview","pinMigrated","screenshotVersion","likeCount",0,"orgId","93515af8-c7b7-459c-9bf2-3bb3df310a75","tursoName","tursoHostname","tursoMeasuredAt","pinned","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm","user",{"_95":94,"_102":98},"org",{"_95":94,"_96":-5,"_97":98,"_18":55,"_99":100,"_101":25},"branch",{"_13":84,"_32":85,"_43":42,"_86":44,"_87":-5,"_88":-5,"_89":-5,"_90":-5,"_91":-5,"_92":45,"_93":94},"isOwned",22,"main","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","yuxiaoy","handle","displayName","avatar","https://img.clerk.com/eyJ0eXBlIjoiZGVmYXVsdCIsImlpZCI6Imluc18yTm10cGhQUklwU0tqZHNiOHNnNGd2ZjJURFUiLCJyaWQiOiJ1c2VyXzJObXd1QWdCZmVtaUh0bGU5TkswOG1sM0xheiIsImluaXRpYWxzIjoiRlkifQ","tier","free","canRestrictVals","profileImageUrl","branchName","adoptOrgId",{"_43":44,"_32":45,"_38":149,"_47":-5,"_48":25,"_49":50,"_51":50,"_52":53,"_54":-5,"_18":55,"_56":-5,"_57":25,"_58":-5,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":150,"_79":151,"_81":152,"_83":25},"tree",[144],"files",[144],"counts",{"_133":61,"_134":135,"_136":61,"_137":138,"_139":140},"contents",[125],"upstreamProject","upstreamBranch","emptyEnvVars","flags",[],"hasHttpFiles",true,"grants","orgs","customDomains",{},{"_43":126,"_32":28,"_30":31,"_127":-5,"_41":42,"_40":14,"_128":25,"_38":129,"_13":130,"_33":34,"_35":37,"_24":25,"_131":-5,"_27":132},"517d2020-581b-4a02-9d7b-ba7b0cefb6be","parentId","latest",["D",1784181189189],15,"label",[28],"pulls","branches",1,"forks","envVars",{"_141":61,"_143":61},"connections",{"_141":61,"_142":61},"count","unfilledCount","emptyCount",{"_43":126,"_32":28,"_30":31,"_127":-5,"_41":42,"_40":14,"_128":25,"_38":145,"_13":130,"_33":34,"_35":37,"_24":25,"_131":-5,"_146":147,"_27":148},["D",1784181189189],"children",[],[28],["D",1783355460069],{"_95":94,"_102":98},{"_95":94,"_96":-5,"_97":98,"_18":55,"_99":100,"_101":25},{"_13":84,"_32":85,"_43":42,"_86":44,"_87":-5,"_88":-5,"_89":-5,"_90":-5,"_91":-5,"_92":45,"_93":94},"ctx",{"_77":-5,"_122":186,"_187":188,"_189":190,"_191":25,"_192":25,"_193":25,"_194":120,"_195":-5,"_196":-7,"_197":-7,"_198":61,"_199":25,"_200":25,"_201":-5,"_202":25,"_203":25,"_204":25,"_205":-5},"ENV",{"_157":158,"_159":160,"_161":85,"_162":163,"_164":165,"_166":167,"_168":169,"_170":171,"_172":173,"_174":175,"_176":177,"_178":179,"_180":181,"_182":183,"_184":185},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

Diff · v16