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const os = require('os')
const bytes = require('bytes')
const sharp = require('sharp')
const morgan = require('morgan')
const express = require('express')
const PDFDocument = require('pdfkit')
const axios = require("axios")
const FormData = require("form-data")
const tfjs = require('@tensorflow/tfjs-node')
const nsfwjs = require('nsfwjs')
const jpegjs = require('jpeg-js')
const fileType = require("file-type")
//const Stress = require('./lib/ddos.js');
//const { BingChat } = (await import("bing-chat")).default
const { acytoo, chatgpt_4 } = require("./lib/chatgpt.js")
const { sss_instagram, gramvio } = require("./lib/instagram.js")
const { allToJpg } = require("./lib/convertFormat.js")
const apikey = "Kyouka"
const app = express()
app.set('json spaces', 4)
app.use(morgan('dev'))
app.use(express.json({ limit: "500mb" }))
app.use(express.urlencoded({ limit: '500mb', extended: true }))
app.use((req, res, next) => {
load_model(),
next()
})
app.all('/', (req, res) => {
const status = {}
const used = process.memoryUsage()
for (let key in used) status[key] = formatSize(used[key])
const totalmem = os.totalmem()
const freemem = os.freemem()
status.memoryUsage = `${formatSize(totalmem - freemem)} / ${formatSize(totalmem)}`
res.json({
creator: "@SadTeams",
message: 'Hello World!!',
uptime: new Date(process.uptime() * 1000).toUTCString().split(' ')[4],
status
})
})
app.post('/imagetopdf', async (req, res) => {
try {
console.log(req.body)
const { images } = req.body
if (!images) return res.json({ success: false, message: 'Required an array image url' })
const buffer = await toPDF(images)
res.setHeader('Content-Disposition', `attachment; filename=${Math.random().toString(36).slice(2)}.pdf`)
res.setHeader('Content-Type', 'application/pdf')
res.setHeader('Content-Length', buffer.byteLength)
res.send(buffer)
} catch (e) {
console.log(e)
e = String(e)
res.json({ error: true, message: e === '[object Object]' ? 'Internal Server Error' : e })
}
})
async function scrapeTitle(url) {
const browser = await chromium.launch();
const page = await browser.newPage();
await page.goto(url);
const title = await page.title();
await browser.close();
return title;
}
app.get('/scrape-title', async (req, res) => {
const url = req.query.url;
if (!url) {
return res.status(400).send('URL is required');
}
try {
const title = await scrapeTitle(url);
res.send({ title });
} catch (error) {
res.status(500).send('An error occurred while scraping the title');
}
});
app.get('/fetch', async (req, res) => {
try {
if (!req.query.url) return res.json({ message: 'Required an url' })
let json = await axios.get(req.query.url)
res.json(json.data)
} catch (e) {
res.send(e)
}
})
app.post('/api/chatgpt', async (req, res) => {
try {
console.log(req.body)
const { prompt, model, status } = req.body
if (!prompt) return res.json({ success: false, message: 'Required an prompt text!' })
if (!model) return res.json({ success: false, message: 'Required an model version!' })
if (!status) return res.json({ success: false, message: 'Required an prompt text!' })
if(status !== apikey) return res.json({ success: false, message: 'Invalid status!' })
if(model == "gpt-4") {
const response = await axios.request({
method: "GET",
url: "https://aemt.me/gpt4?text=" + prompt
})
res.json({
status: "ok",
result: response.data.result
})
} else if(model == "gpt-3.5") {
const response = await acytoo(prompt, "gpt-4")
res.json({
status: "ok",
result: response
})
} else if(model == "gpt-3") {
const response = await acytoo(prompt, "gpt-3.5-turbo")
res.json({
status: "ok",
result: response
})
}
} catch (e) {
console.log(e)
e = String(e)
res.json({ error: true, message: e === '[object Object]' ? 'Internal Server Error' : e })
}
})
app.post('/api/chatgpt2', async (req, res) => {
try {
console.log(req.body)
const { data, prompt, status } = req.body
if (!data) return res.json({ success: false, message: 'Required an data text!' })
if (!prompt) return res.json({ success: false, message: 'Required an prompt text!' })
if (!status) return res.json({ success: false, message: 'Required an status text!' })
if(status !== apikey) return res.json({ success: false, message: 'Invalid status!' })
const response = await axios.request({
method: "GET",
url: `https://aemt.me/prompt/gpt?prompt=${data}&text=${prompt}`
})
res.json({
status: "ok",
result: response.data.result
})
} catch (e) {
console.log(e)
e = String(e)
res.json({ error: true, message: e === '[object Object]' ? 'Internal Server Error' : e })
}
})
app.post('/api/toanime', async (req, res) => {
try {
console.log(req.body)
const { url, status } = req.body
if (!url) return res.json({ success: false, message: 'Required an url!' })
if (!status) return res.json({ success: false, message: 'Required an status text!' })
if(status !== apikey) return res.json({ success: false, message: 'Invalid status!' })
const response = await axios.request({
method: "GET",
url: "https://aemt.me/toanime?url=" + url
})
const image = await axios.request({
method: "GET",
url: response.data.url.img_crop_single,
responseType: "arraybuffer"
})
res.setHeader('Content-Type', 'image/jpeg')
res.send(image.data)
} catch (e) {
console.log(e)
e = String(e)
res.json({ error: true, message: e === '[object Object]' ? 'Internal Server Error' : e })
}
})
app.post('/api/upscaler', async (req, res) => {
try {
console.log(req.body)
const { images, denoise, scale, format, type, status } = req.body
if (!images) return res.json({ success: false, message: 'Required an images!' })
if (!denoise) return res.json({ success: false, message: 'Required an denoise!' })
if (!scale) return res.json({ success: false, message: 'Required an images!' })
if (!format) return res.json({ success: false, message: 'Required an format size!' })
if (!type) return res.json({ success: false, message: 'Required an images!' })
if (!status) return res.json({ success: false, message: 'Required an status text!' })
if(status !== apikey) return res.json({ success: false, message: 'Invalid status!' })
if (/^(https?|http):\/\//i.test(images)) {
const data_img = await axios.request({
method: "GET",
url: images,
responseType: "arraybuffer"
})
const response = await processImage(data_img.data, denoise, scale, format, type)
const type_img = await fileType.fromBuffer(response)
res.setHeader('Content-Type', type_img.mime)
res.send(response)
} else if (images && typeof images == 'string' && isBase64(images)) {
const response = await processImage(Buffer.from(images, "base64"), denoise, scale, format, type)
const type_img = await fileType.fromBuffer(response)
res.setHeader('Content-Type', type_img.mime)
res.send(response)
} else {
res.json({
success: false, message: 'No url or base64 detected!!'
})
}
} catch (e) {
console.log(e)
e = String(e)
res.json({ error: true, message: e === '[object Object]' ? 'Internal Server Error' : e })
}
})
app.post('/api/nsfw-check', async (req, res) => {
try {
console.log(req.body)
const { images, status } = req.body
if (!images) return res.json({ success: false, message: 'Required an images!' })
if (!status) return res.json({ success: false, message: 'Required an status text!' })
if(status !== apikey) return res.json({ success: false, message: 'Invalid status!' })
if (/^(https?|http):\/\//i.test(images)) {
const data_img = await axios.request({
method: "GET",
url: images,
responseType: "arraybuffer"
})
const response = await check_nsfw(data_img.data)
res.json({
status: "ok",
result: response
})
} else if (images && typeof images == 'string' && isBase64(images)) {
const img = Buffer.from(images, "base64")
const type = await fileType.fromBuffer(img)
if (type.ext == "jpg") {
let response = await check_nsfw(img)
res.json({
status: "ok",
result: response
})
}
if (type.ext == "webp") {
let converting = await allToJpg(img)
let response = await check_nsfw(converting)
res.json({
status: "ok",
result: response
})
}
} else {
res.json({
success: false, message: 'No url or base64 detected!!'
})
}
} catch (e) {
console.log(e)
e = String(e)
res.json({ error: true, message: e === '[object Object]' ? 'Internal Server Error' : e })
}
})
app.post('/api/instagram/stalk', async (req, res) => {
try {
console.log(req.body)
const { username, status } = req.body
if (!username) return res.json({ success: false, message: 'Required an username text!' })
if (!status) return res.json({ success: false, message: 'Required an status text!' })
if(status !== apikey) return res.json({ success: false, message: 'Invalid status!' })
const response = await gramvio(username)
res.json({
status: "ok",
result: response
})
} catch (e) {
console.log(e)
e = String(e)
res.json({ error: true, message: e === '[object Object]' ? 'Internal Server Error' : e })
}
})
app.post('/api/instagram/download', async (req, res) => {
try {
console.log(req.body)
const { url, status } = req.body
if (!url) return res.json({ success: false, message: 'Required an url!' })
if (!status) return res.json({ success: false, message: 'Required an status text!' })
if(status !== apikey) return res.json({ success: false, message: 'Invalid status!' })
const response = await sss_instagram(url)
res.json({
status: "ok",
result: response
})
} catch (e) {
console.log(e)
e = String(e)
res.json({ error: true, message: e === '[object Object]' ? 'Internal Server Error' : e })
}
})
/*app.post('/tools/ddos', async (req, res) => {
try {
console.log(req.body)
const { url, interval, mount, status } = req.body
if (!url) return res.json({ success: false, message: 'Required an url!' })
if (!interval) return res.json({ success: false, message: 'Required an interval number!' })
if (!mount) return res.json({ success: false, message: 'Required an mount number!' })
if (!status) return res.json({ success: false, message: 'Required an status text!' })
if(status !== apikey) return res.json({ success: false, message: 'Invalid status!' })
const response = await Stress.start({
debug: true,
url: url,
interval: interval,
max: mount,
proxy: "./proxy.txt"
})
res.json({
status: "ok",
target: url,
interval: interval,
mount: mount,
response
})
} catch (e) {
console.log(e)
e = String(e)
res.json({ error: true, message: e === '[object Object]' ? 'Internal Server Error' : e })
}
})*/
app.post('/api/bingchat', async (req, res) => {
try {
console.log(req.body)
const { prompt, status } = req.body
if (!prompt) return res.json({ success: false, message: 'Required an prompt text!' })
if (!status) return res.json({ success: false, message: 'Required an status text!' })
if(status !== apikey) return res.json({ success: false, message: 'Invalid status!' })
const response = await axios.request({
method: "GET",
url: "https://aemt.me/bingai?text=" + prompt
})
res.json({
status: "ok",
result: response.data.result
})
} catch (e) {
console.log(e)
e = String(e)
res.json({ error: true, message: e === '[object Object]' ? 'Internal Server Error' : e })
}
})
app.post('/convert/zombie', async (req, res) => {
try {
console.log(req.body)
const { url, status } = req.body
if (!url) return res.json({ success: false, message: 'Required an url!' })
if (!status) return res.json({ success: false, message: 'Required an status text!' })
if(status !== apikey) return res.json({ success: false, message: 'Invalid status!' })
const resp = await axios.request({
method: "GET",
url: "https://aemt.me/converter/zombie?url=" + url
})
const response = await axios.request({
method: "GET",
url: resp.data.url,
contentType: "arraybuffer"
})
res.setHeader('Content-Type', 'image/jpeg')
res.send(response.data)
} catch (e) {
console.log(e)
e = String(e)
res.json({ error: true, message: e === '[object Object]' ? 'Internal Server Error' : e })
}
})
const PORT = process.env.PORT || 7860
app.listen(PORT, () => {
console.log('App running on port', PORT)
})
function formatSize(num) {
return bytes(+num || 0, { unitSeparator: ' ' })
}
function isBase64(str) {
try {
return btoa(atob(str)) === str
} catch {
return false
}
}
function toPDF(urls) {
return new Promise(async (resolve, reject) => {
try {
if (!Array.isArray(urls)) urls = [urls]
const doc = new PDFDocument({ margin: 0, size: 'A4' })
const buffers = []
for (let i = 0; i < urls.length; i++) {
const response = await fetch(urls[i], { headers: { referer: urls[i] }})
if (!response.ok) continue
const type = response.headers.get('content-type')
if (!/image/.test(type)) continue
let buffer = Buffer.from(await response.arrayBuffer())
if (/gif|webp/.test(type)) buffer = await sharp(buffer).png().toBuffer()
doc.image(buffer, 0, 0, { fit: [595.28, 841.89], align: 'center', valign: 'center' })
if (urls.length !== i + 1) doc.addPage()
}
doc.on('data', (chunk) => buffers.push(chunk))
doc.on('end', () => resolve(Buffer.concat(buffers)))
doc.on('error', reject)
doc.end()
} catch (e) {
console.log(e)
reject(e)
}
})
}
async function processImage(image, denoise, scale, format, type) {
return new Promise(async (resolve, reject) => {
try {
let type_img = await fileType.fromBuffer(image)
let random_numbers = Math.floor(Math.random() * 1000);
const formData = new FormData();
formData.append("denoise", denoise);
formData.append("scale", scale);
formData.append("format", format);
formData.append("type", type);
formData.append("file", image, {
filename:
"images_" + random_numbers.toString().padStart(3, "0") + "." + type_img.ext,
contentType: type_img.mime,
});
const response = await axios.request({
method: "POST",
url: "https://api.alcaamado.es/ns-api-waifu2x/v1/convert",
data: formData,
debug: true,
headers: {
Authority: "api.alcaamado.es",
Accept: "application/json",
Referer: "https://waifu2x.pro/",
Origin: "https://waifu2x.pro",
"User-Agent":
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
},
});
const images = await axios.request({
method: "GET",
url:
"https://api.alcaamado.es/api/v2/waifu2x/get?hash=" +
response.data.hash +
"&type=" +
format,
headers: {
Accept: "image/webp,image/apng,image/svg+xml,image/*,*/*;q=0.8",
"Content-Type": "image/jpg",
Referer: "https://waifu2x.pro/",
"User-Agent":
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
},
responseType: "arraybuffer",
});
// Mengonversi arraybuffer ke Buffer
//const buffer = Buffer.from(images.data);
resolve(images.data);
} catch (error) {
reject(error);
}
});
}
async function check_nsfw(buffer) {
let _model = await load_model()
const convert = async (img) => {
// Decoded image in UInt8 Byte array
const image = await jpegjs.decode(img, { useTArray: true })
const numChannels = 3
const numPixels = image.width * image.height
const values = new Int32Array(numPixels * numChannels)
for (let i = 0; i < numPixels; i++)
for (let c = 0; c < numChannels; ++c)
values[i * numChannels + c] = image.data[i * 4 + c]
return tfjs.tensor3d(values, [image.height, image.width, numChannels], 'int32')
}
const image = await convert(buffer)
const predictions = await _model.classify(image)
image.dispose();
const results = predictions.map(v => {
return {
class_name: v.className,
probability: v.probability,
probability_percent: (v.probability * 100).toFixed(2)
}
})
return results
}
async function load_model() {
return await nsfwjs.load()
} |