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chore: refactor

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This commit is contained in:
Ephraim Atta-Duncan
2025-10-30 12:20:52 +00:00
parent 00e33c5331
commit 134d5ac03e
6 changed files with 78 additions and 127 deletions
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4
.env.example
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@ -136,8 +136,12 @@ NEXT_PUBLIC_FEATURE_BILLING_ENABLED=
NEXT_PUBLIC_DISABLE_SIGNUP=
# [[AI]]
# AI Gateway
AI_GATEWAY_API_KEY=""
# OPTIONAL: API key for Google Generative AI (Gemini). Get your key from https://ai.google.dev
GOOGLE_GENERATIVE_AI_API_KEY=""
# OPTIONAL: Enable AI field detection debug mode to save preview images with bounding boxes
NEXT_PUBLIC_AI_DEBUG_PREVIEW=
# [[E2E Tests]]
E2E_TEST_AUTHENTICATE_USERNAME="Test User"

3
.gitignore vendored
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@ -60,3 +60,6 @@ CLAUDE.md
# agents
.specs
# ai debug previews
packages/assets/ai-previews/

33
apps/remix/app/components/general/envelope-editor/envelope-editor-fields-page.tsx
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@ -299,17 +299,10 @@ export const EnvelopeEditorFieldsPage = () => {
return;
}
console.log('Successfully captured page 1 as PNG Blob:', {
size: `${(blob.size / 1024).toFixed(2)} KB`,
type: blob.type,
});
console.log('Blob object:', blob);
console.log('[Auto Add Fields] Sending image to AI endpoint...');
const formData = new FormData();
formData.append('image', blob, 'page-1.png');
const response = await fetch('/api/ai/detect-object-and-draw', {
const response = await fetch('/api/ai/detect-form-fields', {
method: 'POST',
body: formData,
credentials: 'include',
@ -320,10 +313,6 @@ export const EnvelopeEditorFieldsPage = () => {
}
const detectedFields = await response.json();
console.log(
`[Auto Add Fields] Detected ${detectedFields.length} fields:`,
detectedFields,
);
if (!editorFields.selectedRecipient || !currentEnvelopeItem) {
toast({
@ -336,9 +325,12 @@ export const EnvelopeEditorFieldsPage = () => {
const pageCanvasRefs = getPageCanvasRefs(1);
if (!pageCanvasRefs) {
console.warn(
'[Auto Add Fields] Could not get page dimensions for minimum field enforcement',
);
toast({
title: t`Error`,
description: t`Failed to capture page. Please ensure the document is fully loaded.`,
variant: 'destructive',
});
return;
}
let addedCount = 0;
@ -381,20 +373,19 @@ export const EnvelopeEditorFieldsPage = () => {
});
addedCount++;
} catch (error) {
console.error(`Failed to add ${fieldType} field:`, error);
toast({
title: t`Error`,
description: t`Failed to add field. Please try again.`,
variant: 'destructive',
});
}
}
console.log(
`[Auto Add Fields] Successfully added ${addedCount} fields to the document`,
);
toast({
title: t`Success`,
description: t`Added ${addedCount} fields to the document`,
});
} catch (error) {
console.error('Auto add fields error:', error);
toast({
title: t`Error`,
description: t`An unexpected error occurred while capturing the page.`,

143
apps/remix/server/api/ai.ts
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@ -1,6 +1,5 @@
import { sValidator } from '@hono/standard-validator';
import { generateObject } from 'ai';
import { readFile, writeFile } from 'fs/promises';
import { mkdir, writeFile } from 'fs/promises';
import { Hono } from 'hono';
import { join } from 'path';
import sharp from 'sharp';
@ -8,13 +7,13 @@ import { Canvas, Image } from 'skia-canvas';
import { getSession } from '@documenso/auth/server/lib/utils/get-session';
import { AppError, AppErrorCode } from '@documenso/lib/errors/app-error';
import { env } from '@documenso/lib/utils/env';
import type { HonoEnv } from '../router';
import {
type TDetectObjectsResponse,
ZDetectObjectsAndDrawRequestSchema,
ZDetectObjectsRequestSchema,
ZDetectedObjectSchema,
type TDetectFormFieldsResponse,
ZDetectFormFieldsRequestSchema,
ZDetectedFormFieldSchema,
} from './ai.types';
/**
@ -91,15 +90,14 @@ When detecting thin horizontal lines for SIGNATURE, INITIALS, NAME, EMAIL, DATE,
- Expanded field: [ymin=420, xmin=200, ymax=500, xmax=600] (creates 80-unit tall field)
- This gives comfortable signing space while respecting the form layout`;
const runObjectDetection = async (imageBuffer: Buffer): Promise<TDetectObjectsResponse> => {
const runFormFieldDetection = async (imageBuffer: Buffer): Promise<TDetectFormFieldsResponse> => {
const compressedImageBuffer = await resizeAndCompressImage(imageBuffer);
const base64Image = compressedImageBuffer.toString('base64');
const result = await generateObject({
// model: google('gemini-2.5-pro'),
model: 'google/gemini-2.5-pro',
output: 'array',
schema: ZDetectedObjectSchema,
schema: ZDetectedFormFieldSchema,
messages: [
{
role: 'user',
@ -120,74 +118,37 @@ const runObjectDetection = async (imageBuffer: Buffer): Promise<TDetectObjectsRe
return result.object;
};
export const aiRoute = new Hono<HonoEnv>()
.post('/detect-objects', sValidator('json', ZDetectObjectsRequestSchema), async (c) => {
try {
await getSession(c.req.raw);
export const aiRoute = new Hono<HonoEnv>().post('/detect-form-fields', async (c) => {
try {
await getSession(c.req.raw);
const { imagePath } = c.req.valid('json');
const parsedBody = await c.req.parseBody();
const rawImage = parsedBody.image;
const imageCandidate = Array.isArray(rawImage) ? rawImage[0] : rawImage;
const parsed = ZDetectFormFieldsRequestSchema.safeParse({ image: imageCandidate });
const imageBuffer = await readFile(imagePath);
const detectedObjects = await runObjectDetection(imageBuffer);
return c.json<TDetectObjectsResponse>(detectedObjects);
} catch (error) {
console.error('Object detection failed:', error);
if (error instanceof AppError) {
throw error;
}
throw new AppError(AppErrorCode.UNKNOWN_ERROR, {
message: 'Failed to detect objects',
userMessage: 'An error occurred while detecting objects. Please try again.',
if (!parsed.success) {
throw new AppError(AppErrorCode.INVALID_REQUEST, {
message: 'Image file is required',
userMessage: 'Please upload a valid image file.',
});
}
})
.post('/detect-object-and-draw', async (c) => {
try {
await getSession(c.req.raw);
const imageBuffer = Buffer.from(await parsed.data.image.arrayBuffer());
const metadata = await sharp(imageBuffer).metadata();
const imageWidth = metadata.width;
const imageHeight = metadata.height;
const parsedBody = await c.req.parseBody();
const rawImage = parsedBody.image;
const imageCandidate = Array.isArray(rawImage) ? rawImage[0] : rawImage;
const parsed = ZDetectObjectsAndDrawRequestSchema.safeParse({ image: imageCandidate });
if (!imageWidth || !imageHeight) {
throw new AppError(AppErrorCode.INVALID_REQUEST, {
message: 'Unable to extract image dimensions',
userMessage: 'The image file appears to be invalid or corrupted.',
});
}
if (!parsed.success) {
throw new AppError(AppErrorCode.INVALID_REQUEST, {
message: 'Image file is required',
userMessage: 'Please upload a valid image file.',
});
}
const imageBlob = parsed.data.image;
const arrayBuffer = await imageBlob.arrayBuffer();
const imageBuffer = Buffer.from(arrayBuffer);
const metadata = await sharp(imageBuffer).metadata();
const imageWidth = metadata.width;
const imageHeight = metadata.height;
console.log(
`[detect-object-and-draw] Original image dimensions: ${imageWidth}x${imageHeight}`,
);
if (!imageWidth || !imageHeight) {
throw new AppError(AppErrorCode.INVALID_REQUEST, {
message: 'Unable to extract image dimensions',
userMessage: 'The image file appears to be invalid or corrupted.',
});
}
console.log('[detect-object-and-draw] Compressing image for Gemini API...');
console.log('[detect-object-and-draw] Calling Gemini API for form field detection...');
const detectedObjects = await runObjectDetection(imageBuffer);
console.log('[detect-object-and-draw] Gemini API call completed');
console.log(
`[detect-object-and-draw] Detected ${detectedObjects.length} objects, starting to draw...`,
);
const detectedFields = await runFormFieldDetection(imageBuffer);
if (env('NEXT_PUBLIC_AI_DEBUG_PREVIEW') === 'true') {
const padding = { left: 80, top: 20, right: 20, bottom: 40 };
const canvas = new Canvas(
imageWidth + padding.left + padding.right,
@ -210,7 +171,6 @@ export const aiRoute = new Hono<HonoEnv>()
ctx.stroke();
}
// Horizontal grid lines (every 100 units on 0-1000 scale)
for (let i = 0; i <= 1000; i += 100) {
const y = padding.top + (i / 1000) * imageHeight;
ctx.beginPath();
@ -221,8 +181,8 @@ export const aiRoute = new Hono<HonoEnv>()
const colors = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#FF00FF', '#00FFFF'];
detectedObjects.forEach((obj, index) => {
const [ymin, xmin, ymax, xmax] = obj.box_2d.map((coord) => coord / 1000);
detectedFields.forEach((field, index) => {
const [ymin, xmin, ymax, xmax] = field.box_2d.map((coord) => coord / 1000);
const x = xmin * imageWidth + padding.left;
const y = ymin * imageHeight + padding.top;
@ -235,7 +195,7 @@ export const aiRoute = new Hono<HonoEnv>()
ctx.fillStyle = colors[index % colors.length];
ctx.font = '20px Arial';
ctx.fillText(obj.label, x, y - 5);
ctx.fillText(field.label, x, y - 5);
});
ctx.strokeStyle = '#000000';
@ -284,26 +244,25 @@ export const aiRoute = new Hono<HonoEnv>()
.replace(/[-:]/g, '')
.replace(/\..+/, '')
.replace('T', '_');
const outputFilename = `detected_objects_${timestamp}.png`;
const outputPath = join(process.cwd(), outputFilename);
const outputFilename = `detected_form_fields_${timestamp}.png`;
const debugDir = join(process.cwd(), 'packages', 'assets', 'ai-previews');
const outputPath = join(debugDir, outputFilename);
await mkdir(debugDir, { recursive: true });
console.log('[detect-object-and-draw] Converting canvas to PNG buffer...');
const pngBuffer = await canvas.toBuffer('png');
console.log(`[detect-object-and-draw] Saving to: ${outputPath}`);
await writeFile(outputPath, pngBuffer);
console.log('[detect-object-and-draw] Image saved successfully!');
return c.json<TDetectObjectsResponse>(detectedObjects);
} catch (error) {
console.error('Object detection and drawing failed:', error);
if (error instanceof AppError) {
throw error;
}
throw new AppError(AppErrorCode.UNKNOWN_ERROR, {
message: 'Failed to detect objects and draw',
userMessage: 'An error occurred while detecting and drawing objects. Please try again.',
});
}
});
return c.json<TDetectFormFieldsResponse>(detectedFields);
} catch (error) {
if (error instanceof AppError) {
throw error;
}
throw new AppError(AppErrorCode.UNKNOWN_ERROR, {
message: 'Failed to detect form fields and generate preview',
userMessage: 'An error occurred while detecting form fields. Please try again.',
});
}
});

21
apps/remix/server/api/ai.types.ts
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@ -11,7 +11,7 @@ export const ZGenerateTextResponseSchema = z.object({
export type TGenerateTextRequest = z.infer<typeof ZGenerateTextRequestSchema>;
export type TGenerateTextResponse = z.infer<typeof ZGenerateTextResponseSchema>;
export const ZDetectedObjectSchema = z.object({
export const ZDetectedFormFieldSchema = z.object({
box_2d: z
.array(z.number())
.length(4)
@ -32,19 +32,12 @@ export const ZDetectedObjectSchema = z.object({
.describe('Documenso field type inferred from nearby label text or visual characteristics'),
});
export const ZDetectObjectsRequestSchema = z.object({
imagePath: z.string().min(1, 'Image path is required'),
// TODO: Replace with file upload - reference files.ts pattern
});
export const ZDetectObjectsResponseSchema = z.array(ZDetectedObjectSchema);
export type TDetectedObject = z.infer<typeof ZDetectedObjectSchema>;
export type TDetectObjectsRequest = z.infer<typeof ZDetectObjectsRequestSchema>;
export type TDetectObjectsResponse = z.infer<typeof ZDetectObjectsResponseSchema>;
export const ZDetectObjectsAndDrawRequestSchema = z.object({
export const ZDetectFormFieldsRequestSchema = z.object({
image: z.instanceof(Blob, { message: 'Image file is required' }),
});
export type TDetectObjectsAndDrawRequest = z.infer<typeof ZDetectObjectsAndDrawRequestSchema>;
export const ZDetectFormFieldsResponseSchema = z.array(ZDetectedFormFieldSchema);
export type TDetectedFormField = z.infer<typeof ZDetectedFormFieldSchema>;
export type TDetectFormFieldsRequest = z.infer<typeof ZDetectFormFieldsRequestSchema>;
export type TDetectFormFieldsResponse = z.infer<typeof ZDetectFormFieldsResponseSchema>;

1
turbo.json
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@ -46,6 +46,7 @@
"NEXT_PUBLIC_WEBAPP_URL",
"NEXT_PRIVATE_INTERNAL_WEBAPP_URL",
"NEXT_PUBLIC_POSTHOG_KEY",
"NEXT_PUBLIC_AI_DEBUG_PREVIEW",
"NEXT_PUBLIC_FEATURE_BILLING_ENABLED",
"NEXT_PUBLIC_DISABLE_SIGNUP",
"NEXT_PRIVATE_PLAIN_API_KEY",