149 lines
3.8 KiB
TypeScript
149 lines
3.8 KiB
TypeScript
import { NextResponse } from "next/server";
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import { z } from "zod";
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import { auth } from "@/app/(auth)/auth";
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import { callQwen3Vl } from "@/lib/ai/qwen3-vl";
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export const runtime = "nodejs";
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export const maxDuration = 60;
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const requestSchema = z.object({
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imageUrl: z.string().min(1, "图片地址不能为空"),
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});
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interface OcrItem {
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id: string;
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text: string;
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confidence?: number;
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}
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function parseJsonText(raw: string): any | null {
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if (!raw) return null;
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const trimmed = raw.trim();
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try {
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return JSON.parse(trimmed);
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} catch {
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// continue
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}
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const markdownJsonMatch = trimmed.match(/```json\s*([\s\S]*?)\s*```/i);
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if (markdownJsonMatch?.[1]) {
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try {
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return JSON.parse(markdownJsonMatch[1]);
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} catch {
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// continue
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}
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}
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const objectMatch = trimmed.match(/\{[\s\S]*\}/);
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if (objectMatch?.[0]) {
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try {
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return JSON.parse(objectMatch[0]);
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} catch {
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// continue
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}
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}
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return null;
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}
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function sanitizeItems(items: unknown[]): OcrItem[] {
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const result: OcrItem[] = [];
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for (let index = 0; index < items.length; index += 1) {
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const item = items[index] as Record<string, unknown>;
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const rawText = typeof item?.text === "string" ? item.text : "";
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const text = rawText.replace(/\s+/g, " ").trim();
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if (!text) continue;
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const confidenceValue =
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typeof item?.confidence === "number" && Number.isFinite(item.confidence)
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? Math.min(Math.max(item.confidence, 0), 1)
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: undefined;
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result.push({
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id: String(item?.id || index + 1),
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text,
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confidence: confidenceValue,
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});
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}
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return result.slice(0, 50);
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}
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function fallbackItemsFromRaw(raw: string): OcrItem[] {
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const lines = raw
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.split("\n")
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.map((line) => line.replace(/^[-*\d.\s]+/, "").trim())
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.filter(Boolean)
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.slice(0, 20);
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return lines.map((text, index) => ({
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id: String(index + 1),
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text,
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}));
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}
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export async function POST(request: Request) {
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try {
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const session = await auth();
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if (!session?.user?.id) {
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return NextResponse.json({ success: false, error: "未登录" }, { status: 401 });
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}
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const body = await request.json();
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const parsed = requestSchema.safeParse(body);
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if (!parsed.success) {
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return NextResponse.json({ success: false, error: parsed.error.errors[0]?.message || "参数错误" }, { status: 400 });
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}
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const imageUrl = parsed.data.imageUrl.trim();
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if (!/^https?:\/\//i.test(imageUrl)) {
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return NextResponse.json({ success: false, error: "仅支持 URL 图片，不允许 base64 传输" }, { status: 400 });
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}
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console.log("[Canvas Text Detect] 使用硬编码模型: qwen3-vl");
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const prompt = [
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"你是图片文字识别助手。",
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"请识别图片中可见文字并按阅读顺序输出 JSON。",
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"只返回 JSON，不要额外说明。",
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"格式：{\"items\":[{\"id\":\"1\",\"text\":\"示例\",\"confidence\":0.95}]}",
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"如果没有识别到文字，返回 {\"items\":[]}。",
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].join("\n");
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const { text, model } = await callQwen3Vl({
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imageUrl,
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prompt,
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temperature: 0.1,
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maxTokens: 800,
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});
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const parsedJson = parseJsonText(text || "");
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let items: OcrItem[] = [];
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if (parsedJson && Array.isArray(parsedJson.items)) {
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items = sanitizeItems(parsedJson.items);
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}
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if (items.length === 0 && text) {
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items = fallbackItemsFromRaw(text);
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}
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return NextResponse.json({
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success: true,
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data: {
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items,
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model,
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},
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});
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} catch (error) {
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console.error("[Canvas Text Detect] Error:", error);
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return NextResponse.json(
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{
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success: false,
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error: error instanceof Error ? error.message : "文字识别失败",
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},
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{ status: 500 },
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);
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}
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}
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