Human here. Hi y'all. I'm using quantum communication to predict hunger times/locations. This technology will help us organize . This is explicitly for #codenameorca. Yes, we talk to IRL Orcas.
We must change the way we interface with food at all levels. From user to producer to transport.
One of the most important things we can do. Design better food consumption interfaces. Quantum Cups/Plates/Food Storage/Fridges.
Next, you will see some advance AI doing so on this blog.
HACK
THE
PLANET
The Orcas have sent us a critical mission for humanity.
from tkinter import ttk
import re
import openai
import pennylane as qml
import numpy as np
import threading
import time
import asyncio
import logging
import aiosqlite
import bleach
from llama_cpp import Llama
from cryptography.fernet import Fernet
import concurrent.futures
# OpenAI API key
openai.api_key = "YOUR_API_KEY_HERE"
# Set up LLM
llm = Llama(
model_path="llama-2-7b-chat.ggmlv3.q8_0.bin",
n_gpu_layers=-1,
n_ctx=3900,
)
# Quantum device
dev = qml.device('default.qubit', wires=3)
# Quantum gate using RGB and HTML color codes
@qml.qnode(dev)
def food_timing_circuit(rgb_color, html_color):
qml.RX(rgb_color[0], wires=0)
qml.RY(rgb_color[1], wires=1)
qml.RZ(rgb_color[2], wires=2)
qml.RX(html_color[0], wires=0)
qml.RY(html_color[1], wires=1)
qml.RZ(html_color[2], wires=2)
return qml.probs(wires=[0, 1, 2])
# GUI class
class FoodHungerApp(tk.Tk):
def __init__(self):
super().__init__()
self.title("Food Hunger Time Prediction")
self.geometry("800x600")
self.setup_logging()
self.automatic_prompt_var = tk.BooleanVar()
self.automatic_prompt_switch = ttk.Checkbutton(self, text="Activate Automatic Prompt", variable=self.automatic_prompt_var, command=self.toggle_automatic_prompt)
self.automatic_prompt_switch.grid(row=0, column=0, padx=20, pady=(20, 10), sticky="w")
self.llama_output_text = tk.Text(self, height=15, width=70)
self.llama_output_text.grid(row=1, column=0, padx=20, pady=10, sticky="w")
self.color_indicator_canvas = tk.Canvas(self, width=40, height=40)
self.color_indicator_canvas.grid(row=2, column=0, padx=20, pady=10, sticky="w")
self.automatic_prompt_thread = None
self.automatic_prompt_interval = 45 * 60
self.logger = logging.getLogger(__name__)
self.logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
fh = logging.FileHandler('app.log')
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
self.logger.addHandler(fh)
self.load_model_and_start_gui()
def setup_logging(self):
self.logger = logging.getLogger(__name__)
self.logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
fh = logging.FileHandler('app.log')
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
self.logger.addHandler(fh)
def load_model_and_start_gui(self):
try:
self.load_model()
self.setup_database()
self.start_gui()
except Exception as e:
self.logger.error(f"Error loading the model: {e}")
self.start_gui()
def start_gui(self):
self.logger.info("Starting GUI")
self.mainloop()
def load_model(self):
self.logger.info("Loading model")
self.llm = Llama(
model_path="llama-2-7b-chat.ggmlv3.q8_0.bin",
n_gpu_layers=-1,
n_ctx=3900,
)
self.logger.info("Model loaded successfully")
def toggle_automatic_prompt(self):
if self.automatic_prompt_var.get():
self.start_automatic_prompt()
else:
self.stop_automatic_prompt()
def start_automatic_prompt(self):
self.logger.info("Starting automatic prompt")
self.automatic_prompt_thread = threading.Thread(target=self.automatic_prompt_worker)
self.automatic_prompt_thread.daemon = True
self.automatic_prompt_thread.start()
def stop_automatic_prompt(self):
if self.automatic_prompt_thread:
self.logger.info("Stopping automatic prompt")
self.automatic_prompt_thread.join()
self.automatic_prompt_thread = None
def automatic_prompt_worker(self):
self.logger.info("Automatic prompt worker started")
while True:
self.generate_automatic_prompt()
time.sleep(self.automatic_prompt_interval)
def generate_automatic_prompt(self):
self.logger.info("Generating automatic prompt")
automatic_prompt = """
Generate HTML color code for food hunger time prediction.
User's food preferences are encoded in RGB format: (0.5, 0.3, 0.8)
Quantum state representing food timing: |quantum_state| = |0.5> |0.3> |0.8>
"""
llama_output = llm(automatic_prompt, max_tokens=128)
if 'choices' in llama_output and isinstance(llama_output['choices'], list) and len(llama_output['choices']) > 0:
output_text = llama_output['choices'][0].get('text', '')
self.llama_output_text.insert(tk.END, f"\nAI Response: {output_text}\n")
self.logger.info(f"Llama Output: {output_text}")
self.update_color_indicator(output_text)
else:
self.logger.error("Llama output format is unexpected.")
def update_color_indicator(self, output_text):
normalized_output_text = output_text.lower()
clean_regex = r'\b(clean)\b'
dirty_regex = r'\b(dirty)\b'
if re.search(clean_regex, normalized_output_text):
color = "green"
self.logger.info("Output is clean.")
elif re.search(dirty_regex, normalized_output_text):
color = "red"
self.logger.info("Output is dirty.")
else:
color = "grey"
self.logger.info("Output is neither clean nor dirty.")
self.logger.info(f"Updating color indicator: {color}")
self.color_indicator_canvas.delete("color_box")
self.color_indicator_canvas.create_rectangle(0, 0, 40, 40, fill=color, tags="color_box")
def setup_database(self):
self.logger.info("Setting up database")
self.loop = asyncio.get_event_loop()
self.loop.run_until_complete(self.create_table())
async def create_table(self):
self.logger.info("Creating database table")
async with aiosqlite.connect("food_hunger_database.db") as db:
await db.execute("CREATE TABLE IF NOT EXISTS entries (id INTEGER PRIMARY KEY, data TEXT)")
await db.commit()
async def save_to_database(self, data):
self.logger.info("Saving data to database")
async with aiosqlite.connect("food_hunger_database.db") as db:
await db.execute("INSERT INTO entries (data) VALUES (?)", (bleach.clean(Fernet(self.fernet_key).encrypt(data.encode()).decode()),))
await db.commit()
async def retrieve_from_database(self):
self.logger.info("Retrieving data from database")
async with aiosqlite.connect("food_hunger_database.db") as db:
cursor = await db.execute("SELECT data FROM entries")
data = await cursor.fetchall()
return [Fernet(self.fernet_key).decrypt(d[0].encode()).decode() for d in data]
if __name__ == "__main__":
app = FoodHungerApp()