LAM.py

# lam.py
import sched
import time
import logging
from datetime import datetime, timedelta
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoProcessor, AutoModelForSpeechSeq2Seq, AutoModelForSequenceClassification
import soundfile as sf
import sounddevice as sd
import pyttsx3
import torch
import torch_directml
import numpy as np
from bindsnet.network import Network
from bindsnet.network.nodes import Input
from bindsnet.network.monitors import Monitor
from bindsnet.network.topology import Connection
from phe import paillier
from sklearn.linear_model import LinearRegression

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Initialize DirectML device
dml = torch_directml.device()

# Initialize Text-to-Speech (TTS) engine
tts_engine = pyttsx3.init()

# Load the tokenizer and model for text generation using DirectML
text_tokenizer = AutoTokenizer.from_pretrained("bigscience/bloomz")
text_model = AutoModelForCausalLM.from_pretrained("bigscience/bloomz").to(dml)

# Load the processor and model for ASR using DirectML
speech_processor = AutoProcessor.from_pretrained("openai/whisper-large-v3")
speech_model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v3").to(dml)

# Load the emotion recognition model using DirectML
emotion_tokenizer = AutoTokenizer.from_pretrained("arpanghoshal/EmoRoBERTa")
emotion_model = AutoModelForSequenceClassification.from_pretrained("arpanghoshal/EmoRoBERTa").to(dml)

# Initialize homomorphic encryption
public_key, private_key = paillier.generate_paillier_keypair()

# Machine learning model for predictive analytics
predictive_model = LinearRegression()

class IzhikevichNodes(torch.nn.Module):
    def __init__(self, n):
        super().__init__()
        self.n = n
        self.v = -65.0 * torch.ones(n, device=dml)
        self.u = self.v * 0.2
        self.a = 0.02
        self.b = 0.2
        self.c = -65.0
        self.d = 8.0

    def forward(self, x):
        v = self.v
        u = self.u
        a = self.a
        b = self.b
        c = self.c
        d = self.d

        fired = v >= 30.0
        self.v[fired] = c
        self.u[fired] += d
        self.v += 0.04 * v**2 + 5 * v + 140 - u + x
        self.u += a * (b * v - u)

        return fired.float()

class LAM:
    def __init__(self, name):
        self.name = name
        self.tasks = []
        self.task_dependencies = {}
        self.network = self.create_bindsnet_model()
        self.scheduler = sched.scheduler(time.time, time.sleep)
        self.context = {"location": None, "time": None, "user_preferences": {}}

    def create_bindsnet_model(self):
        # Create a BindsNET network
        network = Network()

        # Define input and Izhikevich neuron layers
        input_layer = Input(n=100)
        izhikevich_layer = IzhikevichNodes(n=100)

        # Connect input to Izhikevich neurons
        connection = Connection(source=input_layer, target=izhikevich_layer, w=0.05 * torch.randn(input_layer.n, izhikevich_layer.n, device=dml))

        # Add layers and connections to the network
        network.add_layer(input_layer, name="Input")
        network.add_layer(izhikevich_layer, name="Izhikevich")
        network.add_connection(connection, source="Input", target="Izhikevich")

        # Add a monitor to record spikes from the Izhikevich layer
        monitor = Monitor(obj=izhikevich_layer, state_vars=["s"], time=100)
        network.add_monitor(monitor, name="Izhikevich")

        return network

    def simulate_bindsnet_model(self, input_value):
        try:
            # Reset the network
            self.network.reset_state_variables()

            # Create input spikes (binary)
            spikes = torch.bernoulli(input_value * torch.ones(100, device=dml)).byte()

            # Run the network
            self.network.run(inputs={"Input": spikes}, time=100)

            # Get spike recordings
            izhikevich_spikes = self.network.monitors["Izhikevich"].get("s")

            return izhikevich_spikes
        except Exception as e:
            logger.error(f"Error in simulate_bindsnet_model: {e}")
            return None

    def add_task(self, task, priority=1, due_time=None, dependencies=[]):
        try:
            encrypted_task = paillier.EncryptedNumber(public_key, public_key.encrypt(task))
            self.tasks.append({"task": encrypted_task, "priority": priority, "due_time": due_time, "dependencies": dependencies})
            self.tasks.sort(key=lambda x: (x["priority"], x["due_time"] if x["due_time"] else datetime.max))
            for dep in dependencies:
                self.task_dependencies[dep] = self.task_dependencies.get(dep, []) + [task]
            print(f"Task '{task}' added with priority {priority}.")
            if due_time:
                delay = (due_time - datetime.now()).total_seconds()
                if delay > 0:
                    self.scheduler.enter(delay, 1, self.notify, argument=(task,))
                    self.scheduler.run(blocking=False)
        except Exception as e:
            logger.error(f"Error in add_task: {e}")

    def show_tasks(self):
        try:
            print(f"Tasks for {self.name}:")
            for idx, task in enumerate(self.tasks, start=1):
                decrypted_task = private_key.decrypt(task["task"])
                due_time_str = task["due_time"].strftime('%Y-%m-%d %H:%M:%S') if task["due_time"] else "No due time"
                print(f"{idx}. {decrypted_task} (Priority: {task['priority']}, Due: {due_time_str})")
        except Exception as e:
            logger.error(f"Error in show_tasks: {e}")

    def remove_task(self, task_number):
        try:
            if 0 < task_number <= len(self.tasks):
                removed_task = self.tasks.pop(task_number - 1)
                decrypted_task = private_key.decrypt(removed_task["task"])
                print(f"Task '{decrypted_task}' removed.")
            else:
                print("Invalid task number.")
        except Exception as e:
            logger.error(f"Error in remove_task: {e}")

    def set_reminder(self, task_number, reminder_time):
        try:
            if 0 < task_number <= len(self.tasks):
                task = self.tasks[task_number - 1]["task"]
                decrypted_task = private_key.decrypt(task)
                self.tasks[task_number - 1]["due_time"] = reminder_time
                delay = (reminder_time - datetime.now()).total_seconds()
                if delay > 0:
                    self.scheduler.enter(delay, 1, self.notify, argument=(decrypted_task,))
                    self.scheduler.run(blocking=False)
                print(f"Reminder set for task '{decrypted_task}' at {reminder_time}.")
            else:
                print("Invalid task number.")
        except Exception as e:
            logger.error(f"Error in set_reminder: {e}")

    def notify(self, task):
        print(f"Reminder: Task '{task}' is due!")
        self.speak(f"Reminder: Task '{task}' is due!")

    def generate_response(self, prompt):
        try:
            inputs = text_tokenizer(prompt, return_tensors="pt").to(dml)
            outputs = text_model.generate(inputs.input_ids, max_length=50)
            response = text_tokenizer.decode(outputs[0], skip_special_tokens=True)
            return response
        except Exception as e:
            logger.error(f"Error in generate_response: {e}")
            return "Sorry, I couldn't generate a response at the moment."

    def transcribe_audio(self, audio_path):
        try:
            speech_array, _ = sf.read(audio_path)
            inputs = speech_processor(speech_array, sampling_rate=16000, return_tensors="pt").to(dml)
            generated_ids = speech_model.generate(inputs.input_ids)
            transcription = speech_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
            return transcription
        except Exception as e:
            logger.error(f"Error in transcribe_audio: {e}")
            return "Sorry, I couldn't transcribe the audio at the moment."

    def record_and_transcribe_audio(self, duration=5, fs=16000):
        try:
            print("Recording...")
            audio = sd.rec(int(duration * fs), samplerate=fs, channels=1, dtype='float32')
            sd.wait()
            print("Recording complete.")
            audio_path = "temp_audio.wav"
            sf.write(audio_path, audio, fs)
            return self.transcribe_audio(audio_path)
        except Exception as e:
            logger.error(f"Error in record_and_transcribe_audio: {e}")
            return "Sorry, I couldn't transcribe the audio at the moment."

    def recognize_emotion(self, text):
        try:
            inputs = emotion_tokenizer(text, return_tensors="pt").to(dml)
            outputs = emotion_model(**inputs)
            scores = outputs.logits[0].detach().cpu().numpy()
            emotions = ['anger', 'disgust', 'fear', 'joy', 'neutral', 'sadness', 'surprise']
            emotion = emotions[np.argmax(scores)]
            return emotion
        except Exception as e:
            logger.error(f"Error in recognize_emotion: {e}")
            return "Emotion recognition failed."

    def process_task_with_bindsnet(self, task):
        try:
            input_value = torch.tensor([len(task)]).float().to(dml) / 10  # Normalize input
            spikes = self.simulate_bindsnet_model(input_value)
            if spikes is not None:
                spike_count = spikes.sum().item()
                return spike_count
            else:
                return 0
        except Exception as e:
            logger.error(f"Error in process_task_with_bindsnet: {e}")
            return 0

    def record_process_emotion_and_respond(self, duration=5, fs=16000):
        try:
            transcription = self.record_and_transcribe_audio(duration, fs)
            print(f"Transcribed text: {transcription}")
            emotion = self.recognize_emotion(transcription)
            print(f"Recognized emotion: {emotion}")
            response = self.generate_response(f"The detected emotion is {emotion}. How can I help you with that?")
            self.speak(response)
        except Exception as e:
            logger.error(f"Error in record_process_emotion_and_respond: {e}")
            self.speak("Sorry, I couldn't process the emotion at the moment.")

    def analyze_task_priority_and_urgency(self):
        try:
            for task in self.tasks:
                decrypted_task = private_key.decrypt(task["task"])
                priority = task["priority"]
                due_time = task["due_time"]
                urgency = (due_time - datetime.now()).total_seconds() if due_time else float('inf')
                # Example logic for priority and urgency analysis
                if urgency < 3600:  # Task due within an hour
                    task["priority"] = max(priority, 1)  # Increase priority if urgent
                elif urgency < 86400:  # Task due within a day
                    task["priority"] = max(priority, 2)
                else:
                    task["priority"] = max(priority, 3)
            self.tasks.sort(key=lambda x: (x["priority"], x["due_time"] if x["due_time"] else datetime.max))
        except Exception as e:
            logger.error(f"Error in analyze_task_priority_and_urgency: {e}")

    def predictive_analytics(self, historical_data):
        try:
            # Example historical data: list of (task_length, time_taken)
            X = np.array([data[0] for data in historical_data]).reshape(-1, 1)
            y = np.array([data[1] for data in historical_data])
            predictive_model.fit(X, y)
            predicted_time = predictive_model.predict(np.array([[len(self.tasks[-1]["task"])]])).item()
            print(f"Predicted time for the new task: {predicted_time} seconds")
        except Exception as e:
            logger.error(f"Error in predictive_analytics: {e}")

    def update_context(self, location=None, time=None, user_preferences=None):
        if location:
            self.context["location"] = location
        if time:
            self.context["time"] = time
        if user_preferences:
            self.context["user_preferences"] = user_preferences

    def speak(self, text):
        tts_engine.say(text)
        tts_engine.runAndWait()