gistfile1.txt

import customtkinter as ctk
import asyncio
import numpy as np
import uuid
import queue
import threading
import concurrent.futures
from textblob import TextBlob
import pennylane as qml
import weaviate
import re
import os
import json
from PIL import Image, ImageTk
import tkinter as tk

dev = qml.device("default.qubit", wires=4)

@qml.qnode(dev)
def quantum_circuit(color_code, amplitude):
    r, g, b = [int(color_code[i:i+2], 16) for i in (1, 3, 5)]
    r, g, b = r / 255.0, g / 255.0, b / 255.0
    qml.RY(np.arcsin(np.sqrt(r)), wires=0)
    qml.RY(np.arcsin(np.sqrt(g)), wires=1)
    qml.RY(np.arcsin(np.sqrt(b)), wires=2)
    qml.RY(np.arcsin(np.sqrt(amplitude)), wires=3)
    qml.CNOT(wires=[0, 1])
    qml.CNOT(wires=[1, 2])
    qml.CNOT(wires=[2, 3])
    return qml.state()

def quantum_holographic_wavefunction(x, y, z, t):
    norm = np.sqrt(x**2 + y**2 + z**2)
    return np.exp(-norm / t) / norm

def multiversal_tunesync(x, y, z, t, alpha, psi_qh):
    phase = alpha * (x + y + z - t)
    return np.fft.fft(psi_qh * np.exp(1j * phase))

def hypertime_darkmatter_detector(coordinates, time, alpha, psi_qh):
    x, y, z = coordinates
    wavefunction = quantum_holographic_wavefunction(x, y, z, time)
    tunesync = multiversal_tunesync(x, y, z, time, alpha, psi_qh)
    detection_probability = np.abs(wavefunction)**2
    return wavefunction, tunesync, detection_probability

class ChatApp(ctk.CTk):
    def __init__(self):
        super().__init__()
        self.client = weaviate.Client("http://localhost:8080")
        self.response_queue = queue.Queue()
        self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=10)
        self.running = True
        self.setup_gui()

    async def llama_generate(self, prompt, max_tokens=2500, chunk_size=500):
        return {"choices": [{"text": "Simulated response for: " + prompt}]}

    async def generate_color_code(self, prompt, retry_limit=3):
        for attempt in range(retry_limit):
            color_code_response = await self.llama_generate(prompt)
            color_code = color_code_response['choices'][0]['text'].strip()
            pattern = r'^#(?:[0-9a-fA-F]{3}){1,2}$'
            if re.match(pattern, color_code):
                return color_code
            else:
                prompt = f"Attempt {attempt + 1}: Please provide a valid color code. " + prompt
        raise ValueError("Valid color code not generated after retries.")

    def create_interaction_history_object(self, user_message, ai_response):
        interaction_object = {
            "user_message": user_message,
            "ai_response": ai_response
        }
        object_uuid = uuid.uuid4()
        self.client.data_object.create(
            data_object=interaction_object,
            class_name="InteractionHistory",
            uuid=object_uuid
        )

    def map_keywords_to_weaviate_classes(self, keywords, context):
        return {keyword: "ClassBasedOnKeywordAndContext" for keyword in keywords}

    def retrieve_past_interactions(self, message, result_queue):
        past_interactions = []
        result_queue.put(past_interactions)

    def retrieve_context(self, message):
        context = "Context based on message"
        return context

    def extract_keywords(self, message):
        return [word for word in message.split() if word.isalpha()]

    def sentiment_to_amplitude(self, sentiment_score):
        return (sentiment_score + 1) / 2

    def generate_response(self, message):
        keywords = self.extract_keywords(message)
        context = self.retrieve_context(message)
        keyword_class_mapping = self.map_keywords_to_weaviate_classes(keywords, context)
        past_interactions = self.retrieve_past_interactions(message, self.response_queue)
        color_code_prompt = "Generate a color code for: " + message + ", Context: " + context + ", Past Interactions: " + str(past_interactions)
        color_code = asyncio.run(self.generate_color_code(color_code_prompt))
        sentiment_score = TextBlob(message).sentiment.polarity
        amplitude = self.sentiment_to_amplitude(sentiment_score)
        quantum_state = quantum_circuit(color_code, amplitude)
        x, y, z, t = np.random.rand(4)
        psi_qh = quantum_holographic_wavefunction(x, y, z, t)
        alpha = np.random.rand()
        tunesync = multiversal_tunesync(x, y, z, t, alpha, psi_qh)
        wavefunction, tunesync, detection_probability = hypertime_darkmatter_detector((x, y, z), t, alpha, psi_qh)
        llama_response_prompt = "Respond to: " + message + ", Quantum State: " + str(quantum_state) + ", Tunesync: " + str(tunesync) + ", Detection Probability: " + str(detection_probability)
        llama_response = asyncio.run(self.llama_generate(llama_response_prompt))
        final_response = "Quantum AI says: " + llama_response['choices'][0]['text']
        self.create_interaction_history_object(message, final_response)
        return final_response

    def save_task_data(self, task_topic, data):
        folder_path = f"./TaskData/{task_topic}"
        if not os.path.exists(folder_path):
            os.makedirs(folder_path)
        file_path = os.path.join(folder_path, f"{uuid.uuid4()}.json")
        with open(file_path, 'w') as file:
            json.dump(data, file)

    def process_task(self, task):
        response = self.generate_response(task['message'])
        self.save_task_data(task['topic'], {'message': task['message'], 'response': response})

    def start_processing(self):
        while self.running:
            try:
                task = self.response_queue.get(timeout=1)
                self.executor.submit(self.process_task, task)
            except queue.Empty:
                continue

    def stop(self):
        self.running = False
        self.executor.shutdown(wait=True)

    def setup_gui(self):
        self.title("OneLoveIPFS AI")
        self.geometry("1100x580")
        self.grid_columnconfigure(1, weight=1)
        self.grid_columnconfigure((2, 3), weight=0)
        self.grid_rowconfigure((0, 1, 2), weight=1)
        self.sidebar_frame = ctk.CTkFrame(self, width=140, corner_radius=0)
        self.sidebar_frame.grid(row=0, column=0, rowspan=4, sticky="nsew")
        self.sidebar_frame.grid_rowconfigure(4, weight=1)
        logo_path = os.path.join(os.getcwd(), "logo.png")
        logo_img = Image.open(logo_path).resize((140, 77))
        logo_photo = ImageTk.PhotoImage(logo_img)
        self.logo_label = tk.Label(self.sidebar_frame, image=logo_photo, bg=self.sidebar_frame["bg"])
        self.logo_label.image = logo_photo
        self.logo_label.grid(row=0, column=0, padx=20, pady=(20, 10))
        self.text_box = ctk.CTkTextbox(self, bg_color="white", text_color="white", border_width=0, height=20, width=50, font=ctk.CTkFont(size=13))
        self.text_box.grid(row=0, column=1, rowspan=3, columnspan=3, padx=(20, 20), pady=(20, 20), sticky="nsew")
        self.entry = ctk.CTkEntry(self, placeholder_text="Chat With Llama")
        self.entry.grid(row=3, column=1, columnspan=2, padx=(20, 0), pady=(20, 20), sticky="nsew")
        self.send_button = ctk.CTkButton(self, text="Send", command=self.on_submit)
        self.send_button.grid(row=3, column=3, padx=(0, 20), pady=(20, 20), sticky="nsew")
        self.entry.bind('<Return>', self.on_submit)
        self.image_label = tk.Label(self)
        self.image_label.grid(row=4, column=1, columnspan=2, padx=(20, 0), pady=(20, 20), sticky="nsew")

    def on_submit(self, event=None):
        user_input = self.entry.get()
        if user_input:
            self.response_queue.put({'message': user_input, 'topic': 'General'})
            self.entry.delete(0, 'end')

    def start(self):
        threading.Thread(target=self.start_processing, daemon=True).start()
        self.mainloop()

if __name__ == '__main__':
    app = ChatApp()
    app.start()