feat: content engine usw
This commit is contained in:
@@ -17,6 +17,7 @@ export interface OptimizationTask {
|
||||
availableComponents?: ComponentDefinition[];
|
||||
instructions?: string;
|
||||
internalLinks?: { title: string; slug: string }[];
|
||||
customSources?: string[];
|
||||
}
|
||||
|
||||
export interface OptimizeFileOptions {
|
||||
@@ -211,7 +212,32 @@ export class AiBlogPostOrchestrator {
|
||||
console.log(`✅ Saved optimized file to: ${finalPath}`);
|
||||
}
|
||||
|
||||
private async generateVisualPrompt(content: string): Promise<string> {
|
||||
async generateSlug(content: string, title?: string, instructions?: string): Promise<string> {
|
||||
const response = await this.openai.chat.completions.create({
|
||||
model: "google/gemini-2.5-flash",
|
||||
messages: [
|
||||
{
|
||||
role: "system",
|
||||
content: `You generate SEO-optimized URL slugs for B2B blog posts based on the provided content.
|
||||
Return ONLY a JSON object with a single string field "slug".
|
||||
Example: {"slug": "how-to-optimize-react-performance"}
|
||||
Rules: Use lowercase letters, numbers, and hyphens only. No special characters. Keep it concise (2-5 words).`,
|
||||
},
|
||||
{ role: "user", content: `Title: ${title || "Unknown"}\n\nContent:\n${content.slice(0, 3000)}...${instructions ? `\n\nEDITOR INSTRUCTIONS:\nPlease strictly follow these instructions from the editor when generating the slug:\n${instructions}` : ""}` },
|
||||
],
|
||||
response_format: { type: "json_object" },
|
||||
});
|
||||
|
||||
try {
|
||||
const parsed = JSON.parse(response.choices[0].message.content || '{"slug": ""}');
|
||||
let slug = parsed.slug || "new-post";
|
||||
return slug.toLowerCase().replace(/[^a-z0-9]+/g, "-").replace(/^-+|-+$/g, "");
|
||||
} catch {
|
||||
return "new-post";
|
||||
}
|
||||
}
|
||||
|
||||
public async generateVisualPrompt(content: string, instructions?: string): Promise<string> {
|
||||
const response = await this.openai.chat.completions.create({
|
||||
model: this.model,
|
||||
messages: [
|
||||
@@ -227,7 +253,7 @@ FOCUS: The core metaphor or technical concept of the article.
|
||||
|
||||
Example output: "A complex network of glowing fiber optic nodes forming a recursive pyramid structure, technical blue lineart style."`,
|
||||
},
|
||||
{ role: "user", content: content.slice(0, 5000) },
|
||||
{ role: "user", content: `${content.slice(0, 5000)}${instructions ? `\n\nEDITOR INSTRUCTIONS:\nPlease strictly follow these instructions from the editor when generating the visual prompt:\n${instructions}` : ""}` },
|
||||
],
|
||||
max_tokens: 100,
|
||||
});
|
||||
@@ -303,6 +329,7 @@ Example output: "A complex network of glowing fiber optic nodes forming a recurs
|
||||
);
|
||||
const realPosts = await this.researchAgent.fetchRealSocialPosts(
|
||||
task.content.slice(0, 500),
|
||||
task.customSources
|
||||
);
|
||||
socialPosts.push(...realPosts);
|
||||
}
|
||||
@@ -470,7 +497,6 @@ BLOG POST BEST PRACTICES (MANDATORY):
|
||||
- MEME DIVERSITY: Du MUSST ZWINGEND für jedes Meme (sofern passend) abwechslungsreiche Templates nutzen. Um dies zu garantieren, wurde für diesen Artikel das folgende Template ausgewählt: '${forcedMeme}'. Du MUSST EXAKT DIESES TEMPLATE NUTZEN. Versuche nicht, es durch ein Standard-Template wie 'drake' zu ersetzen!
|
||||
- Zitat-Varianten: Wenn du Organisationen oder Studien zitierst, nutze ArticleQuote (mit isCompany=true für Firmen). Für Personen lass isCompany weg.
|
||||
- Füge zwingend ein prägnantes 'TL;DR' ganz am Anfang ein.
|
||||
- Füge ein sauberes TableOfContents ein.
|
||||
- Verwende unsere Komponenten stilvoll für Visualisierungen.
|
||||
- Agiere als hochprofessioneller Digital Architect und entferne alte MDX-Metadaten im Body.
|
||||
- Fazit: Schließe JEDEN Artikel ZWINGEND mit einem starken, klaren 'Fazit' ab.
|
||||
|
||||
@@ -0,0 +1 @@
|
||||
404: Not Found
|
||||
@@ -0,0 +1,30 @@
|
||||
[
|
||||
{
|
||||
"weights":
|
||||
[
|
||||
{"name":"conv0/filters","shape":[3,3,3,16],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.009007044399485869,"min":-1.2069439495311063}},
|
||||
{"name":"conv0/bias","shape":[16],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005263455241334205,"min":-0.9211046672334858}},
|
||||
{"name":"conv1/depthwise_filter","shape":[3,3,16,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.004001977630690033,"min":-0.5042491814669441}},
|
||||
{"name":"conv1/pointwise_filter","shape":[1,1,16,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.013836609615999109,"min":-1.411334180831909}},
|
||||
{"name":"conv1/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0015159862590771096,"min":-0.30926119685173037}},
|
||||
{"name":"conv2/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.002666276225856706,"min":-0.317286870876948}},
|
||||
{"name":"conv2/pointwise_filter","shape":[1,1,32,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.015265831292844286,"min":-1.6792414422128714}},
|
||||
{"name":"conv2/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0020280554598453,"min":-0.37113414915168985}},
|
||||
{"name":"conv3/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006100742489683862,"min":-0.8907084034938438}},
|
||||
{"name":"conv3/pointwise_filter","shape":[1,1,64,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.016276211832083907,"min":-2.0508026908425725}},
|
||||
{"name":"conv3/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.003394414279975143,"min":-0.7637432129944072}},
|
||||
{"name":"conv4/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006716050119961009,"min":-0.8059260143953211}},
|
||||
{"name":"conv4/pointwise_filter","shape":[1,1,128,256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.021875603993733724,"min":-2.8875797271728514}},
|
||||
{"name":"conv4/bias","shape":[256],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0041141652009066415,"min":-0.8187188749804216}},
|
||||
{"name":"conv5/depthwise_filter","shape":[3,3,256,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.008423839597141042,"min":-0.9013508368940915}},
|
||||
{"name":"conv5/pointwise_filter","shape":[1,1,256,512],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.030007277283014035,"min":-3.8709387695088107}},
|
||||
{"name":"conv5/bias","shape":[512],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.008402082966823203,"min":-1.4871686851277068}},
|
||||
{"name":"conv8/filters","shape":[1,1,512,25],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.028336129469030042,"min":-4.675461362389957}},
|
||||
{"name":"conv8/bias","shape":[25],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.002268134028303857,"min":-0.41053225912299807}}
|
||||
],
|
||||
"paths":
|
||||
[
|
||||
"tiny_face_detector_model.bin"
|
||||
]
|
||||
}
|
||||
]
|
||||
@@ -13,11 +13,14 @@
|
||||
}
|
||||
},
|
||||
"scripts": {
|
||||
"build": "tsup src/index.ts --format esm --dts --clean",
|
||||
"dev": "tsup src/index.ts --format esm --watch --dts",
|
||||
"build": "tsup",
|
||||
"dev": "tsup --watch",
|
||||
"lint": "eslint src"
|
||||
},
|
||||
"dependencies": {
|
||||
"@tensorflow/tfjs": "^4.22.0",
|
||||
"@vladmandic/face-api": "^1.7.15",
|
||||
"canvas": "^3.2.1",
|
||||
"sharp": "^0.33.2"
|
||||
},
|
||||
"devDependencies": {
|
||||
@@ -27,4 +30,4 @@
|
||||
"tsup": "^8.3.5",
|
||||
"typescript": "^5.0.0"
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,11 +1,40 @@
|
||||
import sharp from "sharp";
|
||||
import { Canvas, Image, ImageData } from "canvas";
|
||||
// Use the ESM no-bundle build to avoid the default Node entrypoint
|
||||
// which hardcodes require('@tensorflow/tfjs-node') and crashes in Docker.
|
||||
// This build uses pure @tensorflow/tfjs (JS-only, no native C++ bindings).
|
||||
// @ts-ignore - direct path import has no type declarations
|
||||
import * as faceapi from "@vladmandic/face-api/dist/face-api.esm-nobundle.js";
|
||||
import * as tf from "@tensorflow/tfjs";
|
||||
import path from "path";
|
||||
import { fileURLToPath } from "url";
|
||||
|
||||
// Polyfill required by face-api for Node.js
|
||||
faceapi.env.monkeyPatch({ Canvas, Image, ImageData } as any);
|
||||
|
||||
const __filename = fileURLToPath(import.meta.url);
|
||||
const __dirname = path.dirname(__filename);
|
||||
const MODEL_URL = path.join(__dirname, "../models");
|
||||
|
||||
// State flag to ensure we only load weights once
|
||||
let modelsLoaded = false;
|
||||
|
||||
async function loadModelsOnce() {
|
||||
if (modelsLoaded) return;
|
||||
// Initialize pure JS CPU backend (no native bindings needed)
|
||||
await tf.setBackend("cpu");
|
||||
await tf.ready();
|
||||
|
||||
// Load the microscopic TinyFaceDetector (~190KB)
|
||||
await faceapi.nets.tinyFaceDetector.loadFromDisk(MODEL_URL);
|
||||
modelsLoaded = true;
|
||||
}
|
||||
|
||||
export interface ProcessImageOptions {
|
||||
width: number;
|
||||
height: number;
|
||||
format?: "webp" | "jpeg" | "png" | "avif";
|
||||
quality?: number;
|
||||
openRouterApiKey?: string;
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -80,91 +109,6 @@ export function parseImgproxyOptions(
|
||||
return options;
|
||||
}
|
||||
|
||||
interface FaceDetection {
|
||||
x: number;
|
||||
y: number;
|
||||
width: number;
|
||||
height: number;
|
||||
}
|
||||
|
||||
/**
|
||||
* Detects faces using OpenRouter Vision API.
|
||||
* Uses a small preview to save bandwidth and tokens.
|
||||
*/
|
||||
async function detectFacesWithCloud(
|
||||
inputBuffer: Buffer,
|
||||
apiKey: string,
|
||||
): Promise<FaceDetection[]> {
|
||||
try {
|
||||
// Generate a small preview for vision API (max 512px)
|
||||
const preview = await sharp(inputBuffer)
|
||||
.resize(512, 512, { fit: "inside" })
|
||||
.jpeg({ quality: 60 })
|
||||
.toBuffer();
|
||||
|
||||
const base64Image = preview.toString("base64");
|
||||
|
||||
const response = await fetch(
|
||||
"https://openrouter.ai/api/v1/chat/completions",
|
||||
{
|
||||
method: "POST",
|
||||
headers: {
|
||||
Authorization: `Bearer ${apiKey}`,
|
||||
"Content-Type": "application/json",
|
||||
"HTTP-Referer": "https://mintel.me",
|
||||
"X-Title": "Mintel Image Service",
|
||||
},
|
||||
body: JSON.stringify({
|
||||
model: "google/gemini-3-flash-preview", // Fast, cheap, and supports vision
|
||||
messages: [
|
||||
{
|
||||
role: "user",
|
||||
content: [
|
||||
{
|
||||
type: "text",
|
||||
text: 'Detect all human faces in this image. Return ONLY a JSON array of bounding boxes like: [{"x": 0.1, "y": 0.2, "width": 0.05, "height": 0.05}]. Coordinates must be normalized (0 to 1). If no faces, return [].',
|
||||
},
|
||||
{
|
||||
type: "image_url",
|
||||
image_url: {
|
||||
url: `data:image/jpeg;base64,${base64Image}`,
|
||||
},
|
||||
},
|
||||
],
|
||||
},
|
||||
],
|
||||
response_format: { type: "json_object" },
|
||||
}),
|
||||
},
|
||||
);
|
||||
|
||||
if (!response.ok) {
|
||||
throw new Error(`OpenRouter API error: ${response.statusText}`);
|
||||
}
|
||||
|
||||
const data = (await response.json()) as any;
|
||||
const content = data.choices[0]?.message?.content;
|
||||
|
||||
if (!content) return [];
|
||||
|
||||
// The model might return directly or wrapped in a json field
|
||||
const parsed = typeof content === "string" ? JSON.parse(content) : content;
|
||||
const detections = (parsed.faces || parsed.detections || parsed) as any[];
|
||||
|
||||
if (!Array.isArray(detections)) return [];
|
||||
|
||||
return detections.map((d) => ({
|
||||
x: d.x,
|
||||
y: d.y,
|
||||
width: d.width,
|
||||
height: d.height,
|
||||
}));
|
||||
} catch (error) {
|
||||
console.error("Cloud face detection failed:", error);
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
export async function processImageWithSmartCrop(
|
||||
inputBuffer: Buffer,
|
||||
options: ProcessImageOptions,
|
||||
@@ -176,32 +120,41 @@ export async function processImageWithSmartCrop(
|
||||
throw new Error("Could not read image metadata");
|
||||
}
|
||||
|
||||
const detections = options.openRouterApiKey
|
||||
? await detectFacesWithCloud(inputBuffer, options.openRouterApiKey)
|
||||
: [];
|
||||
// Load ML models (noop if already loaded)
|
||||
await loadModelsOnce();
|
||||
|
||||
// Convert sharp image to a Node-compatible canvas Image for face-api
|
||||
const jpegBuffer = await sharpImage.jpeg().toBuffer();
|
||||
const img = new Image();
|
||||
img.src = jpegBuffer;
|
||||
const canvas = new Canvas(img.width, img.height);
|
||||
const ctx = canvas.getContext("2d");
|
||||
ctx.drawImage(img, 0, 0, img.width, img.height);
|
||||
|
||||
// Detect faces locally using the tiny model
|
||||
// Requires explicit any cast since the types expect HTML elements in browser contexts
|
||||
const detections = await faceapi.detectAllFaces(
|
||||
canvas as any,
|
||||
new faceapi.TinyFaceDetectorOptions(),
|
||||
);
|
||||
|
||||
let cropPosition: "center" | "attention" | number = "attention"; // Fallback to sharp's attention if no faces
|
||||
|
||||
// If faces are found, calculate the bounding box containing all faces
|
||||
if (detections.length > 0) {
|
||||
// Map normalized coordinates back to pixels
|
||||
const pixelDetections = detections.map((d) => ({
|
||||
x: d.x * (metadata.width || 0),
|
||||
y: d.y * (metadata.height || 0),
|
||||
width: d.width * (metadata.width || 0),
|
||||
height: d.height * (metadata.height || 0),
|
||||
}));
|
||||
|
||||
// We have faces! Calculate the bounding box that contains all of them
|
||||
let minX = metadata.width;
|
||||
let minY = metadata.height;
|
||||
let maxX = 0;
|
||||
let maxY = 0;
|
||||
|
||||
for (const det of pixelDetections) {
|
||||
if (det.x < minX) minX = Math.max(0, det.x);
|
||||
if (det.y < minY) minY = Math.max(0, det.y);
|
||||
if (det.x + det.width > maxX)
|
||||
maxX = Math.min(metadata.width, det.x + det.width);
|
||||
if (det.y + det.height > maxY)
|
||||
maxY = Math.min(metadata.height, det.y + det.height);
|
||||
for (const det of detections) {
|
||||
const box = det.box;
|
||||
if (box.x < minX) minX = Math.max(0, box.x);
|
||||
if (box.y < minY) minY = Math.max(0, box.y);
|
||||
if (box.x + box.width > maxX)
|
||||
maxX = Math.min(metadata.width, box.x + box.width);
|
||||
if (box.y + box.height > maxY)
|
||||
maxY = Math.min(metadata.height, box.y + box.height);
|
||||
}
|
||||
|
||||
const centerX = Math.floor(minX + (maxX - minX) / 2);
|
||||
@@ -213,32 +166,39 @@ export async function processImageWithSmartCrop(
|
||||
let cropWidth = metadata.width;
|
||||
let cropHeight = metadata.height;
|
||||
|
||||
// Determine the maximal crop window that maintains aspect ratio
|
||||
if (currentRatio > targetRatio) {
|
||||
cropWidth = Math.floor(metadata.height * targetRatio);
|
||||
} else {
|
||||
cropHeight = Math.floor(metadata.width / targetRatio);
|
||||
}
|
||||
|
||||
// Center the crop window over the center of the faces
|
||||
let cropX = Math.floor(centerX - cropWidth / 2);
|
||||
let cropY = Math.floor(centerY - cropHeight / 2);
|
||||
|
||||
// Keep crop window inside image bounds
|
||||
if (cropX < 0) cropX = 0;
|
||||
if (cropY < 0) cropY = 0;
|
||||
if (cropX + cropWidth > metadata.width) cropX = metadata.width - cropWidth;
|
||||
if (cropY + cropHeight > metadata.height)
|
||||
cropY = metadata.height - cropHeight;
|
||||
|
||||
// Pre-crop the image to isolate the faces before resizing
|
||||
sharpImage.extract({
|
||||
left: cropX,
|
||||
top: cropY,
|
||||
width: cropWidth,
|
||||
height: cropHeight,
|
||||
});
|
||||
|
||||
// As we manually calculated the exact focal box, we can now just center it
|
||||
cropPosition = "center";
|
||||
}
|
||||
|
||||
let finalImage = sharpImage.resize(options.width, options.height, {
|
||||
fit: "cover",
|
||||
position: detections.length > 0 ? "center" : "attention",
|
||||
position: cropPosition,
|
||||
});
|
||||
|
||||
const format = options.format || "webp";
|
||||
|
||||
19
packages/image-processor/tsup.config.ts
Normal file
19
packages/image-processor/tsup.config.ts
Normal file
@@ -0,0 +1,19 @@
|
||||
import { defineConfig } from "tsup";
|
||||
|
||||
export default defineConfig({
|
||||
entry: ["src/index.ts"],
|
||||
format: ["esm"],
|
||||
dts: true,
|
||||
clean: true,
|
||||
// Bundle face-api and tensorflow inline (they're pure JS).
|
||||
// Keep sharp and canvas external (they have native C++ bindings).
|
||||
noExternal: [
|
||||
"@vladmandic/face-api",
|
||||
"@tensorflow/tfjs",
|
||||
"@tensorflow/tfjs-backend-wasm"
|
||||
],
|
||||
external: [
|
||||
"sharp",
|
||||
"canvas"
|
||||
],
|
||||
});
|
||||
@@ -176,6 +176,7 @@ Return JSON: { "facts": [ { "statement": "...", "source": "Organization Name Onl
|
||||
*/
|
||||
async fetchRealSocialPosts(
|
||||
topic: string,
|
||||
customSources?: string[],
|
||||
retries = 1,
|
||||
): Promise<SocialPost[]> {
|
||||
console.log(
|
||||
@@ -220,7 +221,7 @@ Return a JSON object with a single string field "query". Example: {"query": "cor
|
||||
|
||||
if (!videos || videos.length === 0) {
|
||||
console.warn(`⚠️ [Serper] No videos found for query: "${queryStr}"`);
|
||||
if (retries > 0) return this.fetchRealSocialPosts(topic, retries - 1);
|
||||
if (retries > 0) return this.fetchRealSocialPosts(topic, customSources, retries - 1);
|
||||
return [];
|
||||
}
|
||||
|
||||
@@ -237,11 +238,16 @@ Return a JSON object with a single string field "query". Example: {"query": "cor
|
||||
|
||||
if (ytVideos.length === 0) {
|
||||
console.warn(`⚠️ [Serper] No YouTube videos in search results.`);
|
||||
if (retries > 0) return this.fetchRealSocialPosts(topic, retries - 1);
|
||||
if (retries > 0) return this.fetchRealSocialPosts(topic, customSources, retries - 1);
|
||||
return [];
|
||||
}
|
||||
|
||||
// Step 3: Ask the LLM to evaluate the relevance of the found videos
|
||||
|
||||
const sourceExamples = customSources && customSources.length > 0
|
||||
? `Specifically prioritize content from: ${customSources.join(", ")}.`
|
||||
: `(e.g., Google Developers, Vercel, Theo - t3.gg, Fireship, Syntax, ByteByteGo, IBM Technology, McKinsey, Gartner, Deloitte).`;
|
||||
|
||||
const evalPrompt = `You are a strict technical evaluator. You must select the MOST RELEVANT educational tech video from the list below based on this core article context: "${topic.slice(0, 800)}..."
|
||||
|
||||
Videos:
|
||||
@@ -249,7 +255,7 @@ ${ytVideos.map((v, i) => `[ID: ${i}] Title: "${v.title}" | Channel: "${v.channel
|
||||
|
||||
RULES:
|
||||
1. The video MUST be highly relevant to the EXACT technical topic of the context.
|
||||
2. The channel SHOULD be a high-quality tech, development, or professional B2B channel (e.g., Google Developers, Vercel, Theo - t3.gg, Fireship, Syntax, ByteByteGo, IBM Technology, McKinsey, Gartner, Deloitte). AVOID gaming, generic vlogs, clickbait, off-topic podcasts, or unrelated topics.
|
||||
2. The channel SHOULD be a high-quality tech, development, or professional B2B channel ${sourceExamples} AVOID gaming, generic vlogs, clickbait, off-topic podcasts, or unrelated topics.
|
||||
3. If none of the videos are strictly relevant to the core technical or business subject (e.g. they are just casually mentioning the word), YOU MUST RETURN -1. Be extremely critical. Do not just pick the "best of the worst".
|
||||
4. If one is highly relevant, return its ID number.
|
||||
|
||||
@@ -273,7 +279,7 @@ Return ONLY a JSON object: {"bestVideoId": number}`;
|
||||
|
||||
if (bestIdx < 0 || bestIdx >= ytVideos.length) {
|
||||
console.warn(`⚠️ [Serper] LLM rejected all videos as irrelevant.`);
|
||||
if (retries > 0) return this.fetchRealSocialPosts(topic, retries - 1);
|
||||
if (retries > 0) return this.fetchRealSocialPosts(topic, customSources, retries - 1);
|
||||
return [];
|
||||
}
|
||||
|
||||
@@ -342,7 +348,7 @@ CRITICAL: Do NOT provide more than 2 trendsKeywords. Keep it extremely focused.`
|
||||
try {
|
||||
let parsed = JSON.parse(
|
||||
response.choices[0].message.content ||
|
||||
'{"trendsKeywords": [], "dcVariables": []}',
|
||||
'{"trendsKeywords": [], "dcVariables": []}',
|
||||
);
|
||||
if (Array.isArray(parsed)) {
|
||||
parsed = parsed[0] || { trendsKeywords: [], dcVariables: [] };
|
||||
|
||||
@@ -13,6 +13,8 @@ import {
|
||||
Footer,
|
||||
FoldingMarks,
|
||||
DocumentTitle,
|
||||
COLORS,
|
||||
FONT_SIZES,
|
||||
} from "./pdf/SharedUI.js";
|
||||
import { SimpleLayout } from "./pdf/SimpleLayout.js";
|
||||
|
||||
@@ -29,23 +31,23 @@ const localStyles = PDFStyleSheet.create({
|
||||
marginBottom: 6,
|
||||
},
|
||||
monoNumber: {
|
||||
fontSize: 7,
|
||||
fontSize: FONT_SIZES.TINY,
|
||||
fontWeight: "bold",
|
||||
color: "#94a3b8",
|
||||
color: COLORS.TEXT_LIGHT,
|
||||
letterSpacing: 2,
|
||||
width: 25,
|
||||
},
|
||||
sectionTitle: {
|
||||
fontSize: 9,
|
||||
fontSize: FONT_SIZES.LABEL,
|
||||
fontWeight: "bold",
|
||||
color: "#000000",
|
||||
color: COLORS.CHARCOAL,
|
||||
textTransform: "uppercase",
|
||||
letterSpacing: 0.5,
|
||||
},
|
||||
officialText: {
|
||||
fontSize: 8,
|
||||
fontSize: FONT_SIZES.BODY,
|
||||
lineHeight: 1.5,
|
||||
color: "#334155",
|
||||
color: COLORS.TEXT_MAIN,
|
||||
textAlign: "justify",
|
||||
paddingLeft: 25,
|
||||
},
|
||||
@@ -100,7 +102,7 @@ export const AgbsPDF = ({
|
||||
};
|
||||
|
||||
const content = (
|
||||
<>
|
||||
<PDFView>
|
||||
<DocumentTitle
|
||||
title="Allgemeine Geschäftsbedingungen"
|
||||
subLines={[`Stand: ${date}`]}
|
||||
@@ -142,7 +144,7 @@ export const AgbsPDF = ({
|
||||
|
||||
<AGBSection index="05" title="Abnahme">
|
||||
Die Leistung gilt als abgenommen, wenn der Auftraggeber sie produktiv
|
||||
nutzt oder innerhalb von 7 Tagen nach Bereitstellung keine
|
||||
nutzt oder innerhalb von 30 Tagen nach Bereitstellung keine
|
||||
wesentlichen Mängel angezeigt werden. Optische Abweichungen,
|
||||
Geschmacksfragen oder subjektive Einschätzungen stellen keine Mängel
|
||||
dar.
|
||||
@@ -206,7 +208,7 @@ export const AgbsPDF = ({
|
||||
bleibt die Wirksamkeit der übrigen Regelungen unberührt.
|
||||
</AGBSection>
|
||||
</PDFView>
|
||||
</>
|
||||
</PDFView>
|
||||
);
|
||||
|
||||
if (mode === "full") {
|
||||
@@ -214,9 +216,8 @@ export const AgbsPDF = ({
|
||||
<SimpleLayout
|
||||
companyData={companyData}
|
||||
bankData={bankData}
|
||||
headerIcon={headerIcon}
|
||||
footerLogo={footerLogo}
|
||||
icon={headerIcon}
|
||||
pageNumber="10"
|
||||
showPageNumber={false}
|
||||
>
|
||||
{content}
|
||||
@@ -232,7 +233,7 @@ export const AgbsPDF = ({
|
||||
<Footer
|
||||
logo={footerLogo}
|
||||
companyData={companyData}
|
||||
bankData={bankData}
|
||||
_bankData={bankData}
|
||||
showDetails={false}
|
||||
showPageNumber={false}
|
||||
/>
|
||||
|
||||
@@ -46,7 +46,7 @@ export const CombinedQuotePDF = ({
|
||||
|
||||
const layoutProps = {
|
||||
date,
|
||||
icon: estimationProps.headerIcon,
|
||||
headerIcon: estimationProps.headerIcon,
|
||||
footerLogo: estimationProps.footerLogo,
|
||||
companyData,
|
||||
bankData,
|
||||
@@ -71,7 +71,7 @@ export const CombinedQuotePDF = ({
|
||||
footerLogo={estimationProps.footerLogo}
|
||||
/>
|
||||
)}
|
||||
<SimpleLayout {...layoutProps} pageNumber="END" showPageNumber={false}>
|
||||
<SimpleLayout {...layoutProps} showPageNumber={false}>
|
||||
<ClosingModule />
|
||||
</SimpleLayout>
|
||||
</PDFDocument>
|
||||
|
||||
@@ -50,7 +50,7 @@ export const EstimationPDF = ({
|
||||
const commonProps = {
|
||||
state,
|
||||
date,
|
||||
icon: headerIcon,
|
||||
headerIcon,
|
||||
footerLogo,
|
||||
companyData,
|
||||
};
|
||||
@@ -64,17 +64,17 @@ export const EstimationPDF = ({
|
||||
<FrontPageModule state={state} headerIcon={headerIcon} date={date} />
|
||||
</PDFPage>
|
||||
|
||||
<SimpleLayout {...commonProps} pageNumber={getPageNum()}>
|
||||
<SimpleLayout {...commonProps}>
|
||||
<BriefingModule state={state} />
|
||||
</SimpleLayout>
|
||||
|
||||
{state.sitemap && state.sitemap.length > 0 && (
|
||||
<SimpleLayout {...commonProps} pageNumber={getPageNum()}>
|
||||
<SimpleLayout {...commonProps}>
|
||||
<SitemapModule state={state} />
|
||||
</SimpleLayout>
|
||||
)}
|
||||
|
||||
<SimpleLayout {...commonProps} pageNumber={getPageNum()}>
|
||||
<SimpleLayout {...commonProps}>
|
||||
<EstimationModule
|
||||
state={state}
|
||||
positions={positions}
|
||||
@@ -83,11 +83,11 @@ export const EstimationPDF = ({
|
||||
/>
|
||||
</SimpleLayout>
|
||||
|
||||
<SimpleLayout {...commonProps} pageNumber={getPageNum()}>
|
||||
<SimpleLayout {...commonProps}>
|
||||
<TransparenzModule pricing={pricing} />
|
||||
</SimpleLayout>
|
||||
|
||||
<SimpleLayout {...commonProps} pageNumber={getPageNum()}>
|
||||
<SimpleLayout {...commonProps}>
|
||||
<ClosingModule />
|
||||
</SimpleLayout>
|
||||
</PDFDocument>
|
||||
|
||||
@@ -8,58 +8,48 @@ const simpleStyles = StyleSheet.create({
|
||||
industrialPage: {
|
||||
padding: 30,
|
||||
paddingTop: 20,
|
||||
backgroundColor: '#ffffff',
|
||||
flexDirection: 'column',
|
||||
backgroundColor: '#FFFFFF',
|
||||
fontFamily: 'Outfit',
|
||||
},
|
||||
industrialNumber: {
|
||||
fontSize: 60,
|
||||
fontWeight: 'bold',
|
||||
color: '#f1f5f9',
|
||||
position: 'absolute',
|
||||
top: -10,
|
||||
right: 0,
|
||||
zIndex: -1,
|
||||
},
|
||||
industrialSection: {
|
||||
marginTop: 16,
|
||||
paddingTop: 12,
|
||||
flexDirection: 'row',
|
||||
position: 'relative',
|
||||
contentView: {
|
||||
flex: 1,
|
||||
marginTop: 20,
|
||||
},
|
||||
});
|
||||
|
||||
interface SimpleLayoutProps {
|
||||
children: React.ReactNode;
|
||||
pageNumber?: string;
|
||||
icon?: string;
|
||||
headerIcon?: string;
|
||||
footerLogo?: string;
|
||||
companyData: any;
|
||||
bankData?: any;
|
||||
showDetails?: boolean;
|
||||
showPageNumber?: boolean;
|
||||
children: React.ReactNode;
|
||||
}
|
||||
|
||||
export const SimpleLayout = ({
|
||||
children,
|
||||
pageNumber,
|
||||
icon,
|
||||
export const SimpleLayout: React.FC<SimpleLayoutProps> = ({
|
||||
headerIcon,
|
||||
footerLogo,
|
||||
companyData,
|
||||
bankData,
|
||||
showPageNumber = true
|
||||
}: SimpleLayoutProps) => {
|
||||
showDetails = false,
|
||||
showPageNumber = true,
|
||||
children,
|
||||
}) => {
|
||||
return (
|
||||
<PDFPage size="A4" style={[pdfStyles.page, simpleStyles.industrialPage]}>
|
||||
<Header icon={icon} showAddress={false} />
|
||||
{pageNumber && <PDFText style={simpleStyles.industrialNumber}>{pageNumber}</PDFText>}
|
||||
<PDFView style={simpleStyles.industrialSection}>
|
||||
<PDFView style={{ width: '100%' }}>
|
||||
{children}
|
||||
</PDFView>
|
||||
<PDFPage size="A4" style={simpleStyles.industrialPage}>
|
||||
<Header icon={headerIcon} sender={companyData.name} showAddress={false} />
|
||||
|
||||
<PDFView style={simpleStyles.contentView}>
|
||||
{children}
|
||||
</PDFView>
|
||||
|
||||
<Footer
|
||||
logo={footerLogo}
|
||||
companyData={companyData}
|
||||
bankData={bankData}
|
||||
showDetails={false}
|
||||
_bankData={bankData}
|
||||
showDetails={showDetails}
|
||||
showPageNumber={showPageNumber}
|
||||
/>
|
||||
</PDFPage>
|
||||
|
||||
53
packages/pdf-library/src/generate-agbs.ts
Normal file
53
packages/pdf-library/src/generate-agbs.ts
Normal file
@@ -0,0 +1,53 @@
|
||||
import { renderToFile, Document as PDFDocument, Font } from "@react-pdf/renderer";
|
||||
import { createElement } from "react";
|
||||
import { AgbsPDF } from "./components/AgbsPDF.js";
|
||||
import path from "path";
|
||||
import fs from "fs";
|
||||
import { fileURLToPath } from "url";
|
||||
|
||||
const __filename = fileURLToPath(import.meta.url);
|
||||
const __dirname = path.dirname(__filename);
|
||||
|
||||
// Standard Font Registration
|
||||
Font.register({
|
||||
family: 'Outfit',
|
||||
fonts: [
|
||||
{ src: 'Helvetica' },
|
||||
{ src: 'Helvetica-Bold', fontWeight: 'bold' },
|
||||
],
|
||||
});
|
||||
|
||||
Font.register({
|
||||
family: 'Helvetica',
|
||||
fonts: [
|
||||
{ src: 'Helvetica' },
|
||||
{ src: 'Helvetica-Bold', fontWeight: 'bold' },
|
||||
],
|
||||
});
|
||||
|
||||
async function generate() {
|
||||
const outDir = path.join(__dirname, "../../../out");
|
||||
if (!fs.existsSync(outDir)) {
|
||||
fs.mkdirSync(outDir, { recursive: true });
|
||||
}
|
||||
|
||||
const outputPath = path.resolve(outDir, "AGB_Mintel.pdf");
|
||||
|
||||
console.log("Generating High-Fidelity AGB PDF...");
|
||||
|
||||
const headerIcon = "/Users/marcmintel/Projects/mintel.me/apps/web/src/assets/logo/Icon-White-Transparent.png";
|
||||
const footerLogo = "/Users/marcmintel/Projects/mintel.me/apps/web/src/assets/logo/Logo-Black-Transparent.png";
|
||||
|
||||
// WRAP IN DOCUMENT - MANDATORY FOR standalone rendering
|
||||
const document = createElement(PDFDocument, {
|
||||
title: "Allgemeine Geschäftsbedingungen - Marc Mintel",
|
||||
author: "Marc Mintel",
|
||||
},
|
||||
createElement(AgbsPDF, { mode: "full", headerIcon, footerLogo })
|
||||
);
|
||||
|
||||
await renderToFile(document, outputPath);
|
||||
console.log(`Generated: ${outputPath}`);
|
||||
}
|
||||
|
||||
generate().catch(console.error);
|
||||
@@ -56,8 +56,8 @@ export class ThumbnailGenerator {
|
||||
}
|
||||
}
|
||||
|
||||
// Default to the requested nano-banana-pro model unless explicitly provided
|
||||
const model = options?.model || "google/nano-banana-pro";
|
||||
// Default to the requested flux-1.1-pro model unless explicitly provided
|
||||
const model = options?.model || "black-forest-labs/flux-1.1-pro";
|
||||
|
||||
const output = await this.replicate.run(model as `${string}/${string}`, {
|
||||
input: inputPayload,
|
||||
|
||||
Reference in New Issue
Block a user