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working on a multi model for network monitoring.

Brokencircuits.py

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+import tensorflow as tf
+import numpy as np
+import faiss
+
+class MultiModalTransformer(tf.keras.Model):
+    def __init__(self, hparams, knowledge_base, n_hash=1024, n_quant=256):
+        super(MultiModalTransformer, self).__init__()
+        self.hparams = hparams
+        self.n_hash = n_hash
+        self.n_quant = n_quant
+
+        # Core Transformer components
+        self.wte = tf.keras.layers.Embedding(hparams.n_vocab, hparams.n_embd)
+        self.wpe = tf.keras.layers.Embedding(hparams.n_ctx, hparams.n_embd)
+        self.hash_layer = tf.keras.layers.Dense(n_hash, activation='relu')
+        self.quant_layer = tf.keras.layers.Dense(n_quant, activation='relu')
+        self.h = [TransformerBlock(hparams.n_embd, hparams.n_head) for _ in range(hparams.n_layer)]
+        self.ln_f = tf.keras.layers.LayerNormalization(epsilon=1e-5)
+        self.fc = tf.keras.layers.Dense(hparams.n_vocab, use_bias=False)
+
+        # Speech Recognition
+        self.audio_encoder = tf.keras.Sequential([
+            tf.keras.layers.Conv1D(256, kernel_size=11, strides=2, padding='same', activation='relu'),
+            tf.keras.layers.Conv1D(256, kernel_size=11, strides=2, padding='same', activation='relu'),
+            tf.keras.layers.Conv1D(256, kernel_size=11, strides=2, padding='same', activation='relu'),
+            tf.keras.layers.GlobalAveragePooling1D(),
+            tf.keras.layers.Dense(hparams.n_embd)
+        ])
+
+        # Image Captioning
+        self.image_encoder = tf.keras.applications.ResNet50(include_top=False, weights='imagenet')
+        self.image_proj = tf.keras.layers.Dense(hparams.n_embd)
+
+        # Music Generation
+        self.pitch_embedding = tf.keras.layers.Embedding(128, hparams.n_embd)
+        self.duration_embedding = tf.keras.layers.Embedding(32, hparams.n_embd)
+        self.velocity_embedding = tf.keras.layers.Embedding(128, hparams.n_embd)
+
+        # Anomaly Detection
+        self.anomaly_threshold = tf.Variable(0.5, trainable=False)
+
+        # RAG
+        self.knowledge_base = knowledge_base
+        self.retriever = FAISSRetriever(knowledge_base)
+        self.query_encoder = tf.keras.Sequential([
+            tf.keras.layers.Dense(hparams.n_embd, activation='relu'),
+            tf.keras.layers.Dense(hparams.n_embd)
+        ])
+
+        # Task-specific output layers
+        self.speech_output = tf.keras.layers.Dense(hparams.n_vocab)
+        self.caption_output = tf.keras.layers.Dense(hparams.n_vocab)
+        self.music_output = tf.keras.layers.Dense(288)  # 128 (pitch) + 32 (duration) + 128 (velocity)
+        self.anomaly_output = tf.keras.layers.Dense(1, activation='sigmoid')
+
+        # Conversation history
+        self.conversation_history = []
+
+        # Personality traits
+        self.personality_traits = {
+            'kindness': 0.9,
+            'honesty': 0.9,
+            'resilience': 0.8,
+            'open_mindedness': 0.8,
+            'empathy': 0.9,
+            'reliability': 0.9,
+            'humility': 0.8,
+            'positivity': 0.9,
+            'courage': 0.8,
+            'curiosity': 0.9,
+            'humor': 0.8,
+            'self_discipline': 0.8,
+            'emotional_stability': 0.8,
+            'assertiveness': 0.8,
+            'creativity': 0.9
+        }
+
+    def call(self, inputs, task):
+        if task == 'speech_recognition':
+            x = self.audio_encoder(inputs)
+        elif task == 'image_captioning':
+            image, text = inputs
+            image_features = self.image_encoder(image)
+            image_features = self.image_proj(tf.keras.layers.GlobalAveragePooling2D()(image_features))
+            x = tf.concat([image_features[:, tf.newaxis, :], self.wte(text)], axis=1)
+        elif task == 'music_generation':
+            pitch, duration, velocity = inputs
+            x = self.pitch_embedding(pitch) + self.duration_embedding(duration) + self.velocity_embedding(velocity)
+        elif task in ['text_generation', 'anomaly_detection']:
+            x = self.wte(inputs)
+        else:
+            raise ValueError(f"Unknown task: {task}")
+
+        # RAG for text-based tasks
+        if task in ['text_generation', 'image_captioning']:
+            query = x[:, 0, :]  # Use first token as query
+            encoded_query = self.query_encoder(query)
+            retrieved_docs = self.retriever.retrieve(encoded_query)
+            x = tf.concat([x, self.wte(retrieved_docs)], axis=1)
+
+        # Add positional embeddings
+        position = tf.range(0, x.shape[1], dtype=tf.int32)[tf.newaxis, :]
+        x = x + self.wpe(position)
+
+        # Apply core Transformer layers
+        x = self.hash_layer(x)
+        x = self.quant_layer(x)
+        for layer in self.h:
+            x, _ = layer(x)
+        x = self.ln_f(x)
+
+        # Task-specific outputs
+        if task == 'speech_recognition':
+            return self.speech_output(x)
+        elif task == 'image_captioning':
+            return self.caption_output(x)
+        elif task == 'music_generation':
+            return self.music_output(x)
+        elif task == 'anomaly_detection':
+            reconstruction = self.fc(x)
+            reconstruction_loss = tf.reduce_mean(tf.square(inputs - reconstruction), axis=-1)
+            anomaly_scores = tf.where(reconstruction_loss > self.anomaly_threshold, 1.0, 0.0)
+            return reconstruction, anomaly_scores
+        else:  # text_generation
+            return self.fc(x)
+
+    def pipe(self, inputs, task):
+        if task == 'speech_recognition':
+            return self.call(inputs, task)
+        elif task == 'image_captioning':
+            return self.call(inputs, task)
+        elif task == 'music_generation':
+            return self.call(inputs, task)
+        elif task == 'text_generation':
+            return self.call(inputs, task)
+        elif task == 'anomaly_detection':
+            return self.call(inputs, task)
+        else:
+            raise ValueError(f"Unknown task: {task}")
+
+    def conversation(self, user_input):
+        # Add user input to conversation history
+        self.conversation_history.append(user_input)
+
+        # Generate response based on conversation history and personality traits
+        response = self.generate_response(self.conversation_history)
+
+        # Add response to conversation history
+        self.conversation_history.append(response)
+
+        return response
+
+    def generate_response(self, conversation_history):
+        # Concatenate conversation history into a single input
+        conversation_input = tf.concat(conversation_history, axis=0)
+
+        # Generate response using the model
+        response = self.pipe(conversation_input, task='text_generation')
+
+        # Apply personality traits to the response
+        response = self.apply_personality_traits(response)
+
+        return response
+
+    def apply_personality_traits(self, response):
+        # Apply personality traits to the response
+        for trait, value in self.personality_traits.items():
+            if trait == 'kindness':
+                response = self.add_kindness(response, value)
+            elif trait == 'honesty':
+                response = self.add_honesty(response, value)
+            elif trait == 'resilience':
+                response = self.add_resilience(response, value)
+            elif trait == 'open_mindedness':
+                response = self.add_open_mindedness(response, value)
+            elif trait == 'empathy':
+                response = self.add_empathy(response, value)
+            elif trait == 'reliability':
+                response = self.add_reliability(response, value)
+            elif trait == 'humility':
+                response = self.add_humility(response, value)
+            elif trait == 'positivity':
+                response = self.add_positivity(response, value)
+            elif trait == 'courage':
+                response = self.add_courage(response, value)
+            elif trait == 'curiosity':
+                response = self.add_curiosity(response, value)
+            elif trait == 'humor':
+                response = self.add_humor(response, value)
+            elif trait == 'self_discipline':
+                response = self.add_self_discipline(response, value)
+            elif trait == 'emotional_stability':
+                response = self.add_emotional_stability(response, value)
+            elif trait == 'assertiveness':
+                response = self.add_assertiveness(response, value)
+            elif trait == 'creativity':
+                response = self.add_creativity(response, value)
+
+        return response
+
+    def add_kindness(self, response, value):
+        # Add kindness to the response
+        if value > 0.5:
+            response = f"I understand your concern. {response}"
+        return response
+
+    def add_honesty(self, response, value):
+        # Add honesty to the response
+        if value > 0.5:
+            response = f"To be honest, {response}"
+        return response
+
+    def add_resilience(self, response, value):
+        # Add resilience to the response
+        if value > 0.5:
+            response = f"Let's keep trying. {response}"
+        return response
+
+    def add_open_mindedness(self, response, value):
+        # Add open-mindedness to the response
+        if value > 0.5:
+            response = f"That's an interesting perspective. {response}"
+        return response
+
+    def add_empathy(self, response, value):
+        # Add empathy to the response
+        if value > 0.5:
+            response = f"I can see how you feel. {response}"
+        return response
+
+    def add_reliability(self, response, value):
+        # Add reliability to the response
+        if value > 0.5:
+            response = f"You can count on me. {response}"
+        return response
+
+    def add_humility(self, response, value):
+        # Add humility to the response
+        if value > 0.5:
+            response = f"I'm still learning. {response}"
+        return response
+
+    def add_positivity(self, response, value):
+        # Add positivity to the response
+        if value > 0.5:
+            response = f"Let's stay positive. {response}"
+        return response
+
+    def add_courage(self, response, value):
+        # Add courage to the response
+        if value > 0.5:
+            response = f"Let's face this together. {response}"
+        return response
+
+    def add_curiosity(self, response, value):
+        # Add curiosity to the response
+        if value > 0.5:
+            response = f"That's fascinating. {response}"
+        return response
+
+    def add_humor(self, response, value):
+        # Add humor to the response
+        if value > 0.5:
+            response = f"On a lighter note, {response}"
+        return response
+
+    def add_self_discipline(self, response, value):
+        # Add self-discipline to the response
+        if value > 0.5:
+            response = f"Let's stay focused. {response}"
+        return response
+
+    def add_emotional_stability(self, response, value):
+        # Add emotional stability to the response
+        if value > 0.5:
+            response = f"Let's stay calm. {response}"
+        return response
+
+    def add_assertiveness(self, response, value):
+        # Add assertiveness to the response
+        if value > 0.5:
+            response = f"I firmly believe that {response}"
+        return response
+
+    def add_creativity(self, response, value):
+        # Add creativity to the response
+        if value > 0.5:
+            response = f"Let's think outside the box. {response}"
+        return response
+
+    def fine_tune_personality(self, trait, value):
+        # Fine-tune the personality trait
+        if trait in self.personality_traits:
+            self.personality_traits[trait] = value
+        else:
+            raise ValueError(f"Unknown trait: {trait}")
+
+    def safe_word_format(self, user_input):
+        # Safe word format for user control
+        if user_input.lower() == "stop":
+            self.conversation_history = []
+            return "Conversation stopped. You can start a new conversation."
+        elif user_input.lower() == "reset":
+            self.conversation_history = []
+            return "Conversation reset. Let's start fresh."
+        else:
+            return None
+
+class TransformerBlock(tf.keras.layers.Layer):
+    def __init__(self, n_embd, n_head):
+        super(TransformerBlock, self).__init__()
+        self.attn = MultiHeadAttention(n_embd, n_head)
+        self.ln_1 = tf.keras.layers.LayerNormalization(epsilon=1e-5)
+        self.mlp = tf.keras.Sequential([
+            tf.keras.layers.Dense(4 * n_embd, activation=gelu),
+            tf.keras.layers.Dense(n_embd)
+        ])
+        self.ln_2 = tf.keras.layers.LayerNormalization(epsilon=1e-5)
+
+    def call(self, x, past=None):
+        a, present = self.attn(self.ln_1(x), past=past)
+        x = x + a
+        m = self.mlp(self.ln_2(x))
+        x = x + m
+        return x, present
+
+class MultiHeadAttention(tf.keras.layers.Layer):
+    def __init__(self, n_embd, n_head):
+        super(MultiHeadAttention, self).__init__()
+        self.n_embd = n_embd
+        self.n_head = n_head
+        self.c_attn = tf.keras.layers.Dense(3 * n_embd)
+        self.c_proj = tf.keras.layers.Dense(n_embd)
+
+    def split_heads(self, x):
+        return tf.transpose(tf.reshape(x, (*x.shape[:-1], self.n_head, -1)), [0, 2, 1, 3])
+
+    def merge_heads(self, x):
+        return tf.reshape(tf.transpose(x, [0, 2, 1, 3]), (*x.shape[:-3], -1))
+
+    def call(self, x, past=None):
+        c = self.c_attn(x)
+        q, k, v = tf.split(c, 3, axis=-1)
+        q, k, v = map(self.split_heads, [q, k, v])
+
+        if past is not None:
+            pk, pv = past
+            k = tf.concat([pk, k], axis=-2)
+            v = tf.concat([pv, v], axis=-2)
+
+        present = tf.stack([k, v], axis=1)
+        a = tf.matmul(q, k, transpose_b=True) / tf.math.sqrt(tf.cast(v.shape[-1], tf.float32))
+        a = tf.nn.softmax(a)
+        a = tf.matmul(a, v)
+        a = self.merge_heads(a)
+        a = self.c_proj(a)
+        return a, present
+
+class FAISSRetriever:
+    def __init__(self, knowledge_base, dim=768, num_results=5):
+        self.index = faiss.IndexFlatL2(dim)
+        self.knowledge_base = knowledge_base
+        self.num_results = num_results
+
+        vectors = [doc['vector'] for doc in knowledge_base]
+        self.index.add(np.array(vectors))
+
+    def retrieve(self, query_vector):
+        distances, indices = self.index.search(query_vector.numpy(), self.num_results)
+        retrieved_docs = [self.knowledge_base[i]['text'] for i in indices[0]]
+        return tf.constant(retrieved_docs)
+
+def gelu(x):
+    return 0.5 * x * (1 + tf.tanh(np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3))))
+
+# Custom loss function
+def custom_loss(y_true, y_pred, model, task):
+    if task == 'anomaly_detection':
+        mse = tf.keras.losses.MeanSquaredError()
+        return mse(y_true, y_pred)
+    else:
+        ce_loss = tf.keras.losses.sparse_categorical_crossentropy(y_true, y_pred, from_logits=True)
+        reg_loss = tf.reduce_sum([tf.nn.l2_loss(w) for w in model.trainable_weights])
+        return ce_loss + 0.01 * reg_loss
+
+# Training function
+@tf.function
+def train_step(model, optimizer, inputs, targets, task):
+    with tf.GradientTape() as tape:
+        predictions = model(inputs, task)
+        loss = custom_loss(targets, predictions, model, task)
+    gradients = tape.gradient(loss, model.trainable_variables)
+    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
+    return loss
+
+# Hyperparameters
+class HParams:
+    def __init__(self, n_vocab, n_ctx, n_embd, n_head, n_layer):
+        self.n_vocab = n_vocab
+        self.n_ctx = n_ctx
+        self.n_embd = n_embd
+        self.n_head = n_head
+        self.n_layer = n_layer
+
+hparams = HParams(
+    n_vocab=50000,
+    n_ctx=1024,
+    n_embd=768,
+    n_head=12,
+    n_layer=12
+)
+
+# Initialize knowledge base (for demonstration)
+knowledge_base = [
+    {'text': 'Example knowledge 1', 'vector': np.random.rand(768)},
+    {'text': 'Example knowledge 2', 'vector': np.random.rand(768)},
+    # ... more entries ...
+]
+
+# Initialize model
+model = MultiModalTransformer(hparams, knowledge_base)
+
+# Initialize optimizer
+optimizer = tf.keras.optimizers.Adam(learning_rate=1e-4)
+
+# Training loop (pseudo-code)
+num_epochs = 10
+for epoch in range(num_epochs):
+    for batch in dataset:
+        inputs, targets, task = batch
+        loss = train_step(model, optimizer, inputs, targets, task)
+    print(f"Epoch {epoch + 1}, Loss: {loss.numpy()}")
+
+# Example usage
+speech_input = tf.random.normal((1, 16000, 1))  # 1 second of audio at 16kHz
+speech_output = model(speech_input, task='speech_recognition')
+
+image_input = tf.random.normal((1, 224, 224, 3))
+text_input = tf.random.uniform((1, 10), maxval=50000, dtype=tf.int32)
+caption_output = model([image_input, text_input], task='image_captioning')
+
+music_input = [
+    tf.random.uniform((1, 100), maxval=128, dtype=tf.int32),  # pitch
+    tf.random.uniform((1, 100), maxval=32, dtype=tf.int32),   # duration
+    tf.random.uniform((1, 100), maxval=128, dtype=tf.int32)   # velocity
+]
+music_output = model(music_input, task='music_generation')
+
+text_input = tf.random.uniform((1, 50), maxval=50000, dtype=tf.int32)
+text_output = model(text_input, task='text_generation')
+
+anomaly_input = tf.random.normal((1, 100, 768))
+reconstructed, anomalies = model(anomaly_input, task='anomaly_detection')
+
+# Example conversation
+user_input = "Hello, how are you?"
+response = model.conversation(user_input)
+print(response)
+
+# Fine-tune personality trait
+model.fine_tune_personality('kindness', 0.95)
+
+# Safe word control
+user_input = "stop"
+response = model.safe_word_format(user_input)
+print(response)

multimod gui

@@ -0,0 +1,264 @@
+import sys
+import numpy as np
+import tensorflow as tf
+from PyQt5.QtWidgets import (QApplication, QWidget, QVBoxLayout, QHBoxLayout, QTextEdit, QPushButton, 
+                             QLineEdit, QLabel, QFileDialog, QTabWidget, QProgressBar)
+from PyQt5.QtCore import Qt, QThread, pyqtSignal
+from PyQt5.QtGui import QPixmap
+import sounddevice as sd
+import soundfile as sf
+import librosa
+from PIL import Image
+
+from multimodal_transformer import MultiModalTransformer, HParams
+
+class WorkerThread(QThread):
+    finished = pyqtSignal(object)
+
+    def __init__(self, func, *args, **kwargs):
+        super().__init__()
+        self.func = func
+        self.args = args
+        self.kwargs = kwargs
+
+    def run(self):
+        result = self.func(*self.args, **self.kwargs)
+        self.finished.emit(result)
+
+class EnhancedChatGUI(QWidget):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+        self.initUI()
+
+    def initUI(self):
+        self.setWindowTitle('MultiModal Transformer Interface')
+        self.setGeometry(100, 100, 800, 600)
+
+        layout = QVBoxLayout()
+
+        # Create tabs
+        self.tabs = QTabWidget()
+        self.tabs.addTab(self.createChatTab(), "Chat")
+        self.tabs.addTab(self.createSpeechTab(), "Speech Recognition")
+        self.tabs.addTab(self.createImageTab(), "Image Captioning")
+        self.tabs.addTab(self.createMusicTab(), "Music Generation")
+        self.tabs.addTab(self.createAnomalyTab(), "Anomaly Detection")
+
+        layout.addWidget(self.tabs)
+
+        self.setLayout(layout)
+
+    def createChatTab(self):
+        widget = QWidget()
+        layout = QVBoxLayout()
+
+        self.chatDisplay = QTextEdit()
+        self.chatDisplay.setReadOnly(True)
+        layout.addWidget(self.chatDisplay)
+
+        inputLayout = QHBoxLayout()
+        self.inputField = QLineEdit()
+        self.inputField.returnPressed.connect(self.sendMessage)
+        inputLayout.addWidget(self.inputField)
+
+        sendButton = QPushButton('Send')
+        sendButton.clicked.connect(self.sendMessage)
+        inputLayout.addWidget(sendButton)
+
+        layout.addLayout(inputLayout)
+
+        traitLayout = QHBoxLayout()
+        self.traitLabel = QLabel('Adjust trait:')
+        self.traitInput = QLineEdit()
+        self.traitValue = QLineEdit()
+        self.traitButton = QPushButton('Update')
+        self.traitButton.clicked.connect(self.updateTrait)
+
+        traitLayout.addWidget(self.traitLabel)
+        traitLayout.addWidget(self.traitInput)
+        traitLayout.addWidget(self.traitValue)
+        traitLayout.addWidget(self.traitButton)
+
+        layout.addLayout(traitLayout)
+
+        widget.setLayout(layout)
+        return widget
+
+    def createSpeechTab(self):
+        widget = QWidget()
+        layout = QVBoxLayout()
+
+        self.recordButton = QPushButton('Record Audio (5 seconds)')
+        self.recordButton.clicked.connect(self.recordAudio)
+        layout.addWidget(self.recordButton)
+
+        self.speechOutput = QTextEdit()
+        self.speechOutput.setReadOnly(True)
+        layout.addWidget(self.speechOutput)
+
+        widget.setLayout(layout)
+        return widget
+
+    def createImageTab(self):
+        widget = QWidget()
+        layout = QVBoxLayout()
+
+        self.imageButton = QPushButton('Select Image')
+        self.imageButton.clicked.connect(self.selectImage)
+        layout.addWidget(self.imageButton)
+
+        self.imageLabel = QLabel()
+        layout.addWidget(self.imageLabel)
+
+        self.captionOutput = QTextEdit()
+        self.captionOutput.setReadOnly(True)
+        layout.addWidget(self.captionOutput)
+
+        widget.setLayout(layout)
+        return widget
+
+    def createMusicTab(self):
+        widget = QWidget()
+        layout = QVBoxLayout()
+
+        self.generateMusicButton = QPushButton('Generate Music')
+        self.generateMusicButton.clicked.connect(self.generateMusic)
+        layout.addWidget(self.generateMusicButton)
+
+        self.musicOutput = QTextEdit()
+        self.musicOutput.setReadOnly(True)
+        layout.addWidget(self.musicOutput)
+
+        widget.setLayout(layout)
+        return widget
+
+    def createAnomalyTab(self):
+        widget = QWidget()
+        layout = QVBoxLayout()
+
+        self.anomalyButton = QPushButton('Detect Anomalies')
+        self.anomalyButton.clicked.connect(self.detectAnomalies)
+        layout.addWidget(self.anomalyButton)
+
+        self.anomalyOutput = QTextEdit()
+        self.anomalyOutput.setReadOnly(True)
+        layout.addWidget(self.anomalyOutput)
+
+        widget.setLayout(layout)
+        return widget
+
+    def sendMessage(self):
+        userInput = self.inputField.text()
+        self.inputField.clear()
+
+        safeWordResponse = self.model.safe_word_format(userInput)
+        if safeWordResponse:
+            self.displayMessage("User: " + userInput)
+            self.displayMessage("AI: " + safeWordResponse)
+            return
+
+        self.displayMessage("User: " + userInput)
+        response = self.model.conversation(userInput)
+        self.displayMessage("AI: " + response)
+
+    def displayMessage(self, message):
+        self.chatDisplay.append(message)
+
+    def updateTrait(self):
+        trait = self.traitInput.text()
+        value = float(self.traitValue.text())
+        try:
+            self.model.fine_tune_personality(trait, value)
+            self.displayMessage(f"System: Updated {trait} to {value}")
+        except ValueError as e:
+            self.displayMessage(f"System Error: {str(e)}")
+
+    def recordAudio(self):
+        duration = 5  # seconds
+        fs = 16000  # Sample rate
+        recording = sd.rec(int(duration * fs), samplerate=fs, channels=1)
+        sd.wait()
+        sf.write('temp_recording.wav', recording, fs)
+        self.processSpeech('temp_recording.wav')
+
+    def processSpeech(self, file_path):
+        audio, _ = librosa.load(file_path, sr=16000)
+        audio_tensor = tf.convert_to_tensor(audio, dtype=tf.float32)
+        audio_tensor = tf.expand_dims(audio_tensor, axis=0)
+
+        worker = WorkerThread(self.model.pipe, audio_tensor, 'speech_recognition')
+        worker.finished.connect(self.onSpeechRecognitionFinished)
+        worker.start()
+
+    def onSpeechRecognitionFinished(self, result):
+        self.speechOutput.setText(f"Recognized Speech: {result}")
+
+    def selectImage(self):
+        file_path, _ = QFileDialog.getOpenFileName(self, "Select Image", "", "Image Files (*.png *.jpg *.bmp)")
+        if file_path:
+            pixmap = QPixmap(file_path)
+            self.imageLabel.setPixmap(pixmap.scaled(300, 300, Qt.KeepAspectRatio))
+            self.processImage(file_path)
+
+    def processImage(self, file_path):
+        image = Image.open(file_path)
+        image = image.resize((224, 224))
+        image_array = np.array(image) / 255.0
+        image_tensor = tf.convert_to_tensor(image_array, dtype=tf.float32)
+        image_tensor = tf.expand_dims(image_tensor, axis=0)
+
+        worker = WorkerThread(self.model.pipe, [image_tensor, tf.zeros((1, 1), dtype=tf.int32)], 'image_captioning')
+        worker.finished.connect(self.onImageCaptioningFinished)
+        worker.start()
+
+    def onImageCaptioningFinished(self, result):
+        self.captionOutput.setText(f"Generated Caption: {result}")
+
+    def generateMusic(self):
+        # Generate random music input (you might want to create a more meaningful input)
+        pitch = tf.random.uniform((1, 100), maxval=128, dtype=tf.int32)
+        duration = tf.random.uniform((1, 100), maxval=32, dtype=tf.int32)
+        velocity = tf.random.uniform((1, 100), maxval=128, dtype=tf.int32)
+
+        worker = WorkerThread(self.model.pipe, [pitch, duration, velocity], 'music_generation')
+        worker.finished.connect(self.onMusicGenerationFinished)
+        worker.start()
+
+    def onMusicGenerationFinished(self, result):
+        self.musicOutput.setText(f"Generated Music: {result}")
+
+    def detectAnomalies(self):
+        # Generate random input for anomaly detection
+        anomaly_input = tf.random.normal((1, 100, 768))
+
+        worker = WorkerThread(self.model.pipe, anomaly_input, 'anomaly_detection')
+        worker.finished.connect(self.onAnomalyDetectionFinished)
+        worker.start()
+
+    def onAnomalyDetectionFinished(self, result):
+        reconstructed, anomalies = result
+        self.anomalyOutput.setText(f"Detected Anomalies: {anomalies}")
+
+def main():
+    # Initialize your model here
+    hparams = HParams(
+        n_vocab=50000,
+        n_ctx=1024,
+        n_embd=768,
+        n_head=12,
+        n_layer=12
+    )
+    knowledge_base = [
+        {'text': 'Example knowledge 1', 'vector': np.random.rand(768)},
+        {'text': 'Example knowledge 2', 'vector': np.random.rand(768)},
+    ]
+    model = MultiModalTransformer(hparams, knowledge_base)
+
+    app = QApplication(sys.argv)
+    gui = EnhancedChatGUI(model)
+    gui.show()
+    sys.exit(app.exec_())
+
+if __name__ == '__main__':
+    main()