Update index.js

index.js

@@ -6,6 +6,8 @@ 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 { BingChat } = (await import("bing-chat")).default
 const apikey = "@SadTeam77"
 
@@ -150,6 +152,36 @@ app.post('/api/waifu2x', 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(response)
+        } else if (images && typeof images == 'string' && isBase64(images)) {
+	   	  const response = await check_nsfw(Buffer.from(images, "base64"))
+          res.json(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/bingchat', async (req, res) => {
 	try {
 		console.log(req.body)
@@ -179,7 +211,13 @@ 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 {
@@ -291,10 +329,16 @@ async function waifu2x(image, formats) {
     })
     return images.data
 }
-function isBase64(str) {
-	try {
-		return btoa(atob(str)) === str
-	} catch {
-		return false
-	}
+async function check_nsfw(buffer) {
+  const model = await nsfwjs.load() // To load a local model, nsfw.load('file://./path/to/model/')
+  // Image must be in tf.tensor3d format
+  // you can convert image to tf.tensor3d with tf.node.decodeImage(Uint8Array,channels)
+  const image = await tfjs.node.decodeImage(buffer,3)
+  const predictions = await model.classify(image)
+  image.dispose() // Tensor memory must be managed explicitly (it is not sufficient to let a tf.Tensor go out of scope for its memory to be released).
+  return prediction.map(v => {
+    class_name: v.className,
+    probability: v.probability,
+    probability_percent: (v.probability * 100).toFixed(2)
+  })
 }