diff --git a/BirdNET_GLOBAL_6K_V2.4_Model_TFJS/app.py b/BirdNET_GLOBAL_6K_V2.4_Model_TFJS/app.py
deleted file mode 100755
index 4a502788..00000000
--- a/BirdNET_GLOBAL_6K_V2.4_Model_TFJS/app.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from flask import Flask, render_template
-
-app = Flask(__name__)
-
-@app.route('/')
-def index():
-    return render_template('index.html')
-
-if __name__ == "__main__":
-    app.run(debug=False)
\ No newline at end of file
diff --git a/BirdNET_GLOBAL_6K_V2.4_Model_TFJS/static/main.js b/BirdNET_GLOBAL_6K_V2.4_Model_TFJS/static/main.js
deleted file mode 100644
index 86e7dff8..00000000
--- a/BirdNET_GLOBAL_6K_V2.4_Model_TFJS/static/main.js
+++ /dev/null
@@ -1,181 +0,0 @@
-// Simple status function to print messages to the page
-function setStatus(status, new_line = true) {
-    document.getElementById('status').innerHTML += status;
-    if (new_line) {
-        document.getElementById('status').innerHTML += '<br>';
-    }
-    console.log(status);
-}
-
-// Define custom layer for computing mel spectrograms
-class MelSpecLayerSimple extends tf.layers.Layer {
-    constructor(config) {
-        super(config);
-
-        // Initialize parameters
-        this.sampleRate = config.sampleRate;
-        this.specShape = config.specShape;
-        this.frameStep = config.frameStep;
-        this.frameLength = config.frameLength;
-        this.fmin = config.fmin;
-        this.fmax = config.fmax;
-        this.melFilterbank = tf.tensor2d(config.melFilterbank);
-    }
-
-    build(inputShape) {
-        // Initialize trainable weights, for example:
-        this.magScale = this.addWeight(
-            'magnitude_scaling',
-            [],
-            'float32',
-            tf.initializers.constant({ value: 1.23 })
-        );
-
-        super.build(inputShape);
-    }
-
-    // Compute the output shape of the layer
-    computeOutputShape(inputShape) {
-        return [inputShape[0], this.specShape[0], this.specShape[1], 1];
-    }
-
-    // Define the layer's forward pass
-    call(input) {
-        return tf.tidy(() => {
-
-            // inputs is a tensor representing the input data
-            input = input[0].squeeze()
-
-            // Normalize values between -1 and 1
-            input = tf.sub(input, tf.min(input, -1, true));
-            input = tf.div(input, tf.max(input, -1, true).add(0.000001));
-            input = tf.sub(input, 0.5);
-            input = tf.mul(input, 2.0);
-
-            // Perform STFT
-            let spec = tf.signal.stft(
-                input,
-                this.frameLength,
-                this.frameStep,
-                this.frameLength,
-                tf.signal.hannWindow,
-            );
-
-            // Cast from complex to float
-            spec = tf.cast(spec, 'float32');
-
-            // Apply mel filter bank
-            spec = tf.matMul(spec, this.melFilterbank);
-
-            // Convert to power spectrogram
-            spec = spec.pow(2.0);
-
-            // Apply nonlinearity
-            spec = spec.pow(tf.div(1.0, tf.add(1.0, tf.exp(this.magScale.read()))));
-
-            // Flip the spectrogram
-            spec = tf.reverse(spec, -1);
-
-            // Swap axes to fit input shape
-            spec = tf.transpose(spec)
-
-            // Adding the channel dimension
-            spec = spec.expandDims(-1);
-
-            // Adding batch dimension
-            spec = spec.expandDims(0);
-
-            return spec;
-        });
-    }
-
-    // Optionally, include the `className` method to provide a machine-readable name for the layer
-    static get className() {
-        return 'MelSpecLayerSimple';
-    }
-}
-
-// Register the custom layer with TensorFlow.js
-tf.serialization.registerClass(MelSpecLayerSimple);
-
-// Main function
-async function run() {
-    await tf.ready();  // Make sure TensorFlow.js is fully loaded
-
-    // Load model with custom layer (this can take a while the first time)
-    setStatus('Loading model...', false);
-    const model = await tf.loadLayersModel('static/model/model.json', {custom_objects: {'MelSpecLayerSimple': MelSpecLayerSimple}});
-    setStatus('Done!')
-
-    // Load labels
-    setStatus('Loading labels...', false);
-    const label_data = await fetch('static/model/labels.json');
-    const labels = await label_data.json();
-    setStatus('Done!')
-
-    // Load the audio file
-    setStatus('Loading audio file...', false);
-    const response = await fetch('static/sample.wav');
-    const arrayBuffer = await response.arrayBuffer();
-
-    // Decode the audio data
-    const audioCtx = new (window.AudioContext || window.webkitAudioContext)();
-    const audioBuffer = await new Promise((resolve, reject) => {
-        audioCtx.decodeAudioData(arrayBuffer, resolve, reject);
-    });
-
-    // Read audio data
-    const audioData = audioBuffer.getChannelData(0); // Get data for the first channel
-
-    // TODO: Resample and split audio data into chunks of 3 seconds
-    // to match the input shape of the model (1, 144000)
-    // For now, let's use a sample that is already 3 seconds long and sampled at 48 kHz
-
-    // Create a tensor from the audio data
-    // Only works for batch size 1 for now
-    let input = tf.tensor(audioData).reshape([1, 144000]);
-    setStatus('Done!')
-
-    // Run the prediction
-    setStatus('Running prediction...', false);
-    const prediction = model.predict(input);
-    setStatus('Done!')
-
-    // Print top 3 probabilities and labels
-    setStatus('<b>Results:</b>');
-    const probs = await prediction.data();
-    const probs_sorted = probs.slice().sort().reverse();
-    for (let i = 0; i < 3; i++) {
-        const index = probs.indexOf(probs_sorted[i]);
-        setStatus(labels[index] + ': ' + probs_sorted[i]);
-    }
-
-    // Load metadata model
-    setStatus('<br>Loading metadata model...', false);
-    const metadata_model = await tf.loadGraphModel('static/model/mdata/model.json');
-    setStatus('Done!')
-
-    // Dummy location and week
-    const lat = 52.5;
-    const lon = 13.4;
-    const week = 42;
-    let mdata_input = tf.tensor([lat, lon, week]).expandDims(0);
-
-    // Run the prediction
-    setStatus('Running mdata prediction...', false);
-    const mdata_prediction = metadata_model.predict(mdata_input);
-    setStatus('Done!')
-
-    // Print top 10 probabilities and labels (labels are the same as for the audio model)
-    setStatus('<b>Most common species @ (' + lat + '/' + lon + ') in week ' + week + ':</b>');
-    const mdata_probs = await mdata_prediction.data();
-    const mdata_probs_sorted = mdata_probs.slice().sort().reverse();
-    for (let i = 0; i < 10; i++) {
-        const index = mdata_probs.indexOf(mdata_probs_sorted[i]);
-        setStatus(labels[index] + ': ' + mdata_probs_sorted[i]);
-    }
-
-}
-
-// Run the function above after the page is fully loaded
-window.addEventListener('load', run);
diff --git a/BirdNET_GLOBAL_6K_V2.4_Model_TFJS/static/sample.wav b/BirdNET_GLOBAL_6K_V2.4_Model_TFJS/static/sample.wav
deleted file mode 100755
index 002cbb01..00000000
Binary files a/BirdNET_GLOBAL_6K_V2.4_Model_TFJS/static/sample.wav and /dev/null differ
diff --git a/BirdNET_GLOBAL_6K_V2.4_Model_TFJS/templates/index.html b/BirdNET_GLOBAL_6K_V2.4_Model_TFJS/templates/index.html
deleted file mode 100644
index 3c125413..00000000
--- a/BirdNET_GLOBAL_6K_V2.4_Model_TFJS/templates/index.html
+++ /dev/null
@@ -1,16 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-    <title>BirdNET TFJS Example</title>
-</head>
-<body>
-    <h1>BirdNET TFJS Example</h1>
-
-    <p id="status"></p>
-    
-    <!-- Load TensorFlow.js -->
-    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
-    <!-- Load your custom script -->
-    <script type="text/javascript" src="static/main.js"></script>
-</body>
-</html>
\ No newline at end of file
diff --git a/js/worker.js b/js/worker.js
index fc8f8f0e..a21fb509 100644
--- a/js/worker.js
+++ b/js/worker.js
@@ -298,38 +298,32 @@ break;
         case "get-valid-species": {getValidSpecies();
 break;
 }
-        case "get-locations": {getLocations({
-    db: STATE.db,
-    file: args.file
-});
+        case "get-locations": { getLocations({ db: STATE.db, file: args.file });
 break;
 }
-        case "get-valid-files-list": {await getFiles(args.files);
+        case "get-valid-files-list": { await getFiles(args.files);
 break;
 }
-        case "insert-manual-record": {const count = await onInsertManualRecord(args);
+        case "insert-manual-record": { await onInsertManualRecord(args);
 break;
 }
-        case "load-model": {SEEN_LABELS = false;
-SEEN_MODEL_READY = false;
-UI.postMessage({
-    event: "spawning"
-});
-if (args.model === "v3") {
-    BATCH_SIZE = 1;
-    sampleRate = 48_000;
-}  else {
-    BATCH_SIZE = parseInt(args.batchSize);
-    sampleRate = 24_000;
-}
-setAudioContext(sampleRate);
-memoryDB = undefined;
-BACKEND = args.backend;
-STATE.update({
-    model: args.model
-});
-predictWorkers.length && terminateWorkers();
-spawnWorkers(args.model, args.list, BATCH_SIZE, args.threads);
+        case "load-model": {
+            SEEN_LABELS = false;
+            SEEN_MODEL_READY = false;
+            UI.postMessage({
+                event: "spawning"
+            });
+            sampleRate = args.model === "v2.4" ? 48_000 : 24_000;
+
+            BATCH_SIZE = parseInt(args.batchSize);
+            setAudioContext(sampleRate);
+            memoryDB = undefined;
+            BACKEND = args.backend;
+            STATE.update({
+                model: args.model
+            });
+            predictWorkers.length && terminateWorkers();
+            spawnWorkers(args.model, args.list, BATCH_SIZE, args.threads);
 break;
 }
         case "post": {await uploadOpus(args);
@@ -339,7 +333,7 @@ break;
 break;
 }
         case "save": {console.log("file save requested");
-await saveAudio(args.file, args.start, args.end, args.filename, args.metadata);
+            await saveAudio(args.file, args.start, args.end, args.filename, args.metadata);
 break;
 }
         case "save2db": {await onSave2DiskDB(args);
@@ -354,23 +348,23 @@ break;
         case "update-file-start": {await onUpdateFileStart(args);
 break;
 }
-        case "update-list": {UI.postMessage({
-    event: "show-spinner"
-});
-SEEN_LIST_UPDATE = false;
-predictWorkers.forEach((worker) => worker.postMessage({
-    message: "list",
-    list: args.list,
-    lat: STATE.lat,
-    lon: STATE.lon,
-    week: -1,
-    threshold: STATE.speciesThreshold
-}));
+        case "update-list": {
+            UI.postMessage({ event: "show-spinner" });
+                SEEN_LIST_UPDATE = false;
+                predictWorkers.forEach((worker) => worker.postMessage({
+                    message: "list",
+                    list: args.list,
+                    lat: STATE.lat,
+                    lon: STATE.lon,
+                    week: -1,
+                    threshold: STATE.speciesThreshold
+                }));
 break;
 }
-        case "update-state": {TEMP = args.temp || TEMP;
-appPath = args.path || appPath;
-STATE.update(args);
+        case "update-state": {
+            TEMP = args.temp || TEMP;
+            appPath = args.path || appPath;
+            STATE.update(args);
 break;
 }
         default: {UI.postMessage("Worker communication lines open");
@@ -1218,7 +1212,7 @@ const getPredictBuffers = async ({
                 worker = workerInstance;
             }
             // Create array with 0's (short segment of silence that will trigger the finalChunk flag
-            const myArray = new Float32Array(new Array(chunkLength).fill(0));
+            const myArray = new Float32Array(Array.from({length: chunkLength}).fill(0));
             feedChunksToModel(myArray, chunkStart, file, end, worker);
             readStream.resume();
         }
@@ -2518,8 +2512,8 @@ const getChartTotals = ({
 const getRate = (species) => {
 
     return new Promise(function (resolve, reject) {
-        const calls = new Array(52).fill(0);
-        const total = new Array(52).fill(0);
+        const calls = Array.from({length: 52}).fill(0);
+        const total = Array.from({length: 52}).fill(0);
         // Add Location filter
         const locationFilter = filterLocation();
 
@@ -2734,7 +2728,7 @@ async function onChartRequest(args) {
                 const count = rows[i].count;
                 // stack years
                 if (!(year in results)) {
-                    results[year] = new Array(dataPoints).fill(0);
+                    results[year] = Array.from({length: dataPoints}).fill(0);
                 }
                 if (aggregation === 'Week') {
                     results[year][parseInt(week) - 1] = count;
diff --git a/package.json b/package.json
index 583b0d0a..3d4d0d6c 100644
--- a/package.json
+++ b/package.json
@@ -35,6 +35,8 @@
       "!*fixed_roll*${/*}",
       "!Help/example.mp3",
       "!venv${/*}",
+      "!test${/*}",
+      "!custom_tfjs${/*}",
       "!*git*${/*}",
       "!package-lock.json",
       "!poetry.lock*",