Update index.js

index.js

@@ -7,13 +7,9 @@ const PDFDocument = require('pdfkit')
 const axios = require("axios")
 const FormData = require("form-data")
 const ytdl = require('ytdl-core')
-const tfjs = require('@tensorflow/tfjs-node')
-const Upscaler = require('upscaler/node')
-const nsfwjs = require('nsfwjs')
-const jpegjs = require('jpeg-js')
 const Jimp = require('jimp')
 const fileType = require("file-type")
-const Waifu2X = require('@ibaraki-douji/waifu2x')
+
 //const Stress = require('./lib/ddos.js');
 //const { BingChat } = (await import("bing-chat")).default
 const { acytoo, chatgpt_4 } = require("./lib/chatgpt.js")
@@ -191,74 +187,7 @@ app.post('/api/upscaler', async (req, res) => {
 		res.json({ error: true, message: e === '[object Object]' ? 'Internal Server Error' : e })
 	}
 })
-app.post('/api/upscaler2', 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 upscaleImage(data_img.data)
-          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 upscaleImage(Buffer.from(images, "base64"))
-          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/upscaler3', 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 upscaleImageV2(data_img.data)
-          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 upscaleImageV2(Buffer.from(images, "base64"))
-          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/img2img', async (req, res) => {
 	try {
 		console.log(req.body)
@@ -329,54 +258,7 @@ app.post('/api/toanime2', async (req, res) => {
 		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)
@@ -561,53 +443,6 @@ function toPDF(urls) {
 	})
 }
 
-async function upscaleImage(imageBuffer) {
-  return new Promise(async (resolve, reject) => {
-      try {
-        const upscaler = new Upscaler();
-        const imageTensor = tfjs.tensor3d(new Uint8Array(imageBuffer), [imageBuffer.height, imageBuffer.width, 3]);
-
-        const upscaledTensor = await upscaler.upscale(imageTensor, {
-          scale: 4, // upscale by a factor of 4
-          sharpen: true, // apply sharpening
-          denoise: true, // apply denoising
-          contrast: 1.5, // increase contrast by 50%
-          colorEnhance: true, // enhance colors
-        });
-
-        const upscaledImageBuffer = await tfjs.tensor3dToBuffer(upscaledTensor);
-
-        // dispose the tensors!
-        imageTensor.dispose();
-        upscaledTensor.dispose();
-        resolve(upscaledImageBuffer);
-      } catch (error) {
-        reject(error);
-      }
-    });
-}
-
-async function upscaleImageV2(imageBuffer) {
-  return new Promise(async (resolve, reject) => {
-    try {
-      const upscale = await Waifu2X.upscale(imageBuffer, null, {
-        noise: 0,
-        scale: 2,
-        gpu: -1,
-        ramLimit: 500,
-        outputAsBuffer: true
-      });
-
-      upscale.finishedPromise().then(() => {
-        const outputBuffer = upscale.endBuffer;
-        resolve(outputBuffer); // ArrayBuffer
-      });
-    } catch(e) {
-      reject(e.message);
-    }
-  });
-}
-
 async function processImageUpscaler(images, denoise, format, type) {
     return new Promise(async (resolve, reject) => {
       try {
@@ -725,38 +560,4 @@ async function processImageAnime(inputBuffer) {
         console.error(err);
         throw err;
     }
-}
-
-
-
-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()
 }