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feat: add Phase 4 AI tools with Python bridge and 6 new tools
Add Python bridge (packages/ai/src/bridge.ts) that calls Python scripts via child_process with venv-first fallback to system python3. Implements 6 AI-powered tools: - Remove Background: rembg-based with U2-Net/IS-Net models - Image Upscaling: Real-ESRGAN with Lanczos fallback - OCR/Text Extraction: Tesseract + PaddleOCR engines - Face/PII Blur: MediaPipe face detection with configurable blur - Object Eraser: LaMa inpainting with mask-based input - Smart Crop: Sharp attention-based entropy cropping (no Python needed) Each tool includes: Python script, TypeScript wrapper, API route, and React settings component. All Python scripts handle ImportError gracefully with clear installation messages.
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@@ -0,0 +1,68 @@
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import type { FastifyInstance, FastifyRequest, FastifyReply } from "fastify";
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import { randomUUID } from "node:crypto";
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import { basename } from "node:path";
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import { extractText } from "@stirling-image/ai";
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import { createWorkspace } from "../../lib/workspace.js";
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/**
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* OCR / text extraction route.
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* Returns JSON with extracted text rather than an image.
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*/
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export function registerOcr(app: FastifyInstance) {
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app.post(
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"/api/v1/tools/ocr",
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async (request: FastifyRequest, reply: FastifyReply) => {
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let fileBuffer: Buffer | null = null;
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let filename = "image";
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let settingsRaw: string | null = null;
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try {
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const parts = request.parts();
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for await (const part of parts) {
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if (part.type === "file") {
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const chunks: Buffer[] = [];
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for await (const chunk of part.file) {
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chunks.push(chunk);
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}
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fileBuffer = Buffer.concat(chunks);
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filename = basename(part.filename ?? "image");
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} else if (part.fieldname === "settings") {
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settingsRaw = part.value as string;
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}
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}
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} catch (err) {
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return reply.status(400).send({
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error: "Failed to parse multipart request",
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details: err instanceof Error ? err.message : String(err),
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});
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}
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if (!fileBuffer || fileBuffer.length === 0) {
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return reply.status(400).send({ error: "No image file provided" });
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}
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try {
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const settings = settingsRaw ? JSON.parse(settingsRaw) : {};
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const jobId = randomUUID();
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const workspacePath = await createWorkspace(jobId);
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const result = await extractText(fileBuffer, workspacePath, {
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engine: settings.engine,
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language: settings.language,
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});
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return reply.send({
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jobId,
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filename,
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text: result.text,
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engine: result.engine,
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});
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} catch (err) {
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return reply.status(422).send({
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error: "OCR failed",
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details: err instanceof Error ? err.message : "Unknown error",
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});
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}
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},
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);
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}
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