edited the way api keys are handled.

Since API keys are now loaded from the auth server, this means that there is a delay between starting the software, loading the keys, and loading all the module files.
Due to nodejs running Async, the modules are being loaded BEFORE the api keys have been returned from the auth server.
So now the api keys are being set inside the module every time the module is being called.
This has absolutely no impact on performance, or security, plus, this was the easiest solution, especially as it required no big changes to the rest of the code

main.js

@@ -2,6 +2,57 @@
 require("./requires.js")
 console.log(start);
 
+const https = require("https");
+let un = process.env.auth_username
+let pw = process.env.auth_password
+
+
+const options = {
+    hostname: "keyserver.dommymommy.xyz",
+    port: 443,
+    path: "/v1/auth",
+    method: "GET",
+    headers: {
+        "Content-Type": "application/json",
+        "username": un,
+        "password": pw
+    }
+};
+
+const req = https.request(options, (res) => {
+    if (res.statusCode === 200) {
+        res.setEncoding("utf8");
+        let data = "";
+        res.on("data", (chunk) => {
+            data += chunk;
+        });
+            res.on("end", () => {
+            const myJson = JSON.parse(data);
+            Object.keys(myJson).forEach(el => {
+                // console.log(el, myJson[el]);
+                process.env[el] = myJson[el]
+            })
+        });
+    } else if (res.statusCode === 401) {
+        res.setEncoding("utf8");
+        let data = "";
+        res.on("data", (chunk) => {
+            data += chunk;
+        });
+            res.on("end", () => {
+            console.log(data);
+            process.exit()
+        });
+    }
+});
+
+req.on("error", (error) => {
+  console.error(error);
+});
+
+req.end();
+
+
 
 // Initialising map to be used to store the functionality later on for reloadability
 mapFunctions = new Map()
@@ -32,29 +83,6 @@ console.log(`The Startup took ${new Date() - start}ms`)
 console.log(`${mapFunctions.size} Function modules loaded`);
 console.log("--------------------------------------------------------------------------------");
 
-
-
-
-
-
-// --------------------------------------------------------- CLI COMMANDS --------------------------------------------------------- //
-
-const rl = readline.createInterface({
-    input: process.stdin,
-    output: process.stdout
-});
-
-
-rl.on("line", data =>{
-    const args = data.trim().split(" ");
-    const command = args.shift().toLowerCase();
-    mapFunctions.get("cliCommands").function(command, args)
-})
-
-
-
-
-
 // ----------------------------------------------------------- ELECTRON ----------------------------------------------------------- //
 
 let mainWindow;
@@ -75,15 +103,6 @@ function createWindow() {
 
 electron.app.whenReady().then(createWindow);
 
-
-// electron.ipcMain.on("extract", (event, args) => {
-//     mapFunctions.get("extraction-video-to-audio").function(args)
-// })
-
-// setTimeout(() => {
-//     mainWindow.webContents.send("fuck", "worked uwu")
-// }, 5000);
-
 electron.ipcMain.handle('get-module-names', async () => {
     let module_array = {
         "ai_modules":[],

requires.js

@@ -22,4 +22,4 @@ electron = require('electron');
 
 axios = require("axios")
 
-console.log(require('dotenv').config({path: __dirname + '/.env'}));
+require('dotenv').config({path: __dirname + '/.env'});

services/modules/llm-chat_gpt/chatgpt.js

@@ -8,7 +8,7 @@ if (!fs.existsSync(outputDir)) {
 }
 
 // Ensure SAIA API key is set in environment variables: export SAIA_API_KEY="your_api_key_here"
-const SAIA_API_KEY = process.env.SAIA_API_KEY; // Ensure SAIA API key is set in environment variables
+let SAIA_API_KEY // Ensure SAIA API key is set in environment variables
 const SAIA_URL = "https://chat-ai.academiccloud.de/v1/chat/completions"; // URL for the REST call, used model and action
 
 const module_exports = {
@@ -18,6 +18,7 @@ const module_exports = {
     description: "Generates documents using OpenAI GPT OSS 120B via SAIA platform",
 
     async function(parameter) {
+        SAIA_API_KEY = process.env.SAIA_API_KEY;
         return new Promise(async (resolve, reject) => {
             try {
                 // console.log("SAIA OpenAI GPT module invoked with parameters:", parameter);

services/modules/llm-gemini/gemini.js

@@ -8,7 +8,7 @@ if (!fs.existsSync(outputDir)) {
 }
 
 // Ensure Google API key is set in environment variables: export GOOGLE_API_KEY="your_api_key_here"
-const GEMINI_API_KEY = process.env.GOOGLE_API_KEY; // Ensure Google API key is set in environment variables: export GOOGLE_API_KEY="your_api_key_here"
+let GEMINI_API_KEY; // Ensure Google API key is set in environment variables: export GOOGLE_API_KEY="your_api_key_here"
 const GEMINI_URL = "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent"; // URL for the REST call, used model and action
 
 const module_exports = {
@@ -18,6 +18,7 @@ const module_exports = {
     description: "Generates documents using Google Gemini LLM",
 
     async function(parameter) {
+        GEMINI_API_KEY = process.env.GOOGLE_API_KEY;
         return new Promise(async (resolve, reject) => {
             try {
                 // console.log("Gemini LLM module invoked with parameters:", parameter);

services/modules/quen3/qwen3.js

@@ -8,7 +8,7 @@ if (!fs.existsSync(outputDir)) {
 }
 
 // Ensure SAIA API key is set in environment variables: export SAIA_API_KEY="your_api_key_here"
-const SAIA_API_KEY = process.env.SAIA_API_KEY;
+let SAIA_API_KEY;
 const SAIA_URL = "https://chat-ai.academiccloud.de/v1/chat/completions"; // URL for the REST call, used model and action
 
 const module_exports = {
@@ -18,6 +18,7 @@ const module_exports = {
     description: "Generates documents using QWEN 3 235B via SAIA platform",
 
     async function(parameter) {
+        SAIA_API_KEY = process.env.SAIA_API_KEY;
         return new Promise(async (resolve, reject) => {
             try {
                 // console.log("SAIA QWEN 3 235B module invoked with parameters:", parameter);

services/modules/transcription-local/parakeet.js

@@ -0,0 +1,54 @@
+// -----------------------------------------------------------
+//  Parakeet (Step 3A: spawn Python minimal integration)
+// -----------------------------------------------------------
+
+const fs = require("fs");
+const path = require("path");
+const { spawn } = require("child_process");
+
+module.exports = {
+    name: "parakeet",
+    type: "transcription",
+    displayname: "NVIDIA Parakeet",
+
+    async function(audioFilePath) {
+        console.log("🦜 [Parakeet] Starting test integration (spawn only)...");
+        console.log("🦜 Input audio:", audioFilePath);
+
+        // Check audio exists
+        if (!fs.existsSync(audioFilePath)) {
+            throw new Error("Audio file does not exist: " + audioFilePath);
+        }
+
+        // Output path in storage/transcripts
+        const sessionId = path.basename(audioFilePath).replace(/\.[^.]+$/, "");
+        const outputDir = path.join(__dirname, "../../../storage/transcripts");
+        fs.mkdirSync(outputDir, { recursive: true });
+
+        const outputPath = path.join(outputDir, `${sessionId}.json`);
+
+        // -------------------------------------------------------
+        // SPAWN PYTHON SCRIPT (step 3A — dummy script)
+        // -------------------------------------------------------
+        return new Promise((resolve, reject) => {
+            const python310 = "C:\\Users\\smith\\AppData\\Local\\Programs\\Python\\Python310\\python.exe";
+            const py = spawn(python310, [
+                path.join(__dirname, "parakeet_transcribe.py"),
+                audioFilePath,
+                outputPath
+            ]);
+
+            py.stdout.on("data", data => console.log("🦜 [Python]", data.toString().trim()));
+            py.stderr.on("data", data => console.error("🦜 [Python ERR]", data.toString().trim()));
+
+            py.on("close", code => {
+                if (code === 0) {
+                    console.log("🦜 [Parakeet] Done (spawn test). Output:", outputPath);
+                    resolve(outputPath);
+                } else {
+                    reject(new Error("Python script failed with exit code " + code));
+                }
+            });
+        });
+    }
+};

services/modules/transcription-local/parakeet_transcribe.py

@@ -0,0 +1,71 @@
+# -----------------------------------------------------------
+#  Parakeet Real Transcriber (NVIDIA NeMo + PyTorch GPU)
+# -----------------------------------------------------------
+
+import sys
+import json
+import soundfile as sf
+import torch
+from nemo.collections.asr.models import ASRModel
+
+# Args:
+#   sys.argv[1] = input audio path
+#   sys.argv[2] = output JSON path
+
+audio_path = sys.argv[1]
+output_path = sys.argv[2]
+
+print("🔥 Starting Parakeet model...")
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print("🔥 Using device:", device)
+
+# -----------------------------------------------------------
+# Load Parakeet model (NVIDIA pretrained ASR)
+# -----------------------------------------------------------
+model = ASRModel.from_pretrained(model_name="nvidia/parakeet-ctc-0.6b")
+model = model.to(device)
+model.eval()
+
+# -----------------------------------------------------------
+# Load audio
+# -----------------------------------------------------------
+print("🎧 Loading audio:", audio_path)
+audio, sr = sf.read(audio_path)
+
+# model expects mono float32
+if len(audio.shape) > 1:
+    audio = audio.mean(axis=1)
+
+audio = audio.astype("float32")
+
+# -----------------------------------------------------------
+# Run inference
+# -----------------------------------------------------------
+print("🧠 Running inference...")
+with torch.no_grad():
+    hyp = model.transcribe([audio])[0]
+
+# Extract only the text
+if hasattr(hyp, "text"):
+    transcript = hyp.text
+else:
+    # fallback: convert to string (rare)
+    transcript = str(hyp)
+
+print("📄 Transcript:", transcript)
+
+# -----------------------------------------------------------
+# Save JSON format compatible with V2D pipeline
+# -----------------------------------------------------------
+result = {
+    "id": output_path.split("/")[-1].replace(".json", ""),
+    "tool": "nemo_parakeet",
+    "status": "completed",
+    "text": transcript,
+    "words": []  # Parakeet XS doesn’t return word timestamps
+}
+
+with open(output_path, "w", encoding="utf-8") as f:
+    json.dump(result, f, indent=2, ensure_ascii=False)
+
+print("✔ JSON saved at:", output_path)

services/modules/transcription-remote/assembly.js

@@ -1,4 +1,4 @@
-const API_KEY = process.env.ASSEMBLYAI_API_KEY;
+let API_KEY
 const BASE_URL = 'https://api.assemblyai.com/v2';
 
 //---------------------------------------------------Upload audio---------------------------------------------------
@@ -92,6 +92,7 @@ module.exports = {
   audioformat: "mp3",
 
   async function(audioFileName) {
+    API_KEY = process.env.ASSEMBLYAI_API_KEY;
     return new Promise(async (resolve, reject) => {
       try {
         // audioFileName ist nur "datei.mp3"