feat: rewrite everything from scratch, use classes, decorators etc

.github/workflows/build.yml

@@ -29,15 +29,19 @@ jobs:
           uses: actions/checkout@v3
 
         - name: Install dependencies
-          run: npm install
+          # temporary fix for installing deps for api
+          run: |
+            npm install
+            cd api && npm install 
 
         - name: Build code
           run: |
-              cp ~/important/interchat-env .env
-              npm run build --if-present
+            cp ~/important/interchat-env .env
+            npm run build --if-present
+            cd api && npm run build --if-present
 
         - name: Deploy Commands
-          run: npm run deploy -- -b
+          run: npm run register:commands -- -b
 
         - name: Prune Dev-dependencies
           run: npm prune --omit=dev

.gitignore

@@ -9,8 +9,7 @@ tsconfig.tsbuildinfo
 # Configuration
 .env
 
-# Unit tests
-tests/
+# Unit test coverage
 __tests__/
 
 # Unit test coverage reports

README.md

@@ -1,6 +1,9 @@
 # InterChat
 Code for a growing discord bot which provides a fun inter-server chat!
 
+// subcommand files must have the same name as the subcommand registered on discord
+// commands and subcommands must be default exports
+
 ## Starting the bot: 
 1. Install dependencies using `npm install`
 2. Make a file called `.env` and fill it out with the appropriate contents mentioned in the env.example file.

api/.gitignore

@@ -0,0 +1,3 @@
+node_modules
+
+dist/

api/index.ts

@@ -0,0 +1,42 @@
+import express from 'express';
+import { node } from '@tensorflow/tfjs-node';
+import { load, predictionType } from 'nsfwjs';
+
+const model = await load();
+const app = express();
+const port = 3000;
+
+app.use(express.json());
+app.use(express.urlencoded({ extended: true }));
+
+const analyzeImage = async (url: string): Promise<predictionType[] | null> => {
+  const imageBuffer = await (await fetch(url)).arrayBuffer();
+
+  const imageTensor = await node.decodeImage(Buffer.from(imageBuffer), 3) as any;
+  const predictions = await model.classify(imageTensor);
+  imageTensor.dispose();
+
+  return predictions;
+};
+
+app.get('/nsfw', async (req, res) => {
+  const url = req.query.url;
+
+  if (!url || typeof url !== 'string') return res.status(400).json({ error: 'Missing url query parameter.' });
+
+  const regex =
+    /(?:(?:(?:[A-Za-z]{3,9}:(?:\/\/)?)(?:[-;:&=+$,\w]+@)?[A-Za-z0-9.-]+|(?:www.|[-;:&=+$,\w]+@)[A-Za-z0-9.-]+)(?:(?:\/[+~%/.\w-_]*)?\??(?:[-+=&;%@.\w_]*)#?(?:[\w]*))?)(?:\.jpg|\.jpeg|\.png)/;
+  if (!regex.test(url)) {
+    return res
+      .status(400)
+      .send({ error: 'Invalid url parameter. Must be a valid PNG, JPG or JPEG image URL.' });
+  }
+
+  const predictions = await analyzeImage(url);
+  if (!predictions) return res.status(500).json({ error: 'Something went wrong while analyzing the image.' });
+  return res.status(200).json(predictions);
+});
+
+app.listen(port, () => {
+  console.log(`API listening on port ${port}`);
+});