I noticed the Secret service was doing some operations in Las Vegas.
This idea is more of a quantum version of "vision models".
I think that law eforcement + compassion AI like quantum AI below can help many people. It can also free up resources and potentially save many lives (Missing person finder AI)
Operation Sand Blast
I want to build a "digital idea" to help with gas stations and others who are being nefariously taken advantage of .
This is the idea. It's even worse because people being targeted are often lower income without any other source of income available.
import logging
import cv2
import numpy as np
import httpx
import aiosqlite
import pennylane as qml
from cryptography.fernet import Fernet
from bleach import clean
from kivymd.app import MDApp
from kivymd.uix.screen import MDScreen
from kivymd.uix.button import MDFlatButton
from kivymd.uix.dialog import MDDialog
from kivymd.uix.textfield import MDTextField
from kivymd.uix.label import MDLabel
logging.basicConfig(level=logging.INFO)
key = Fernet.generate_key()
cipher_suite = Fernet(key)
class SkimmerDetectionApp(MDApp):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.title = "Skimmer Detection App"
self.openai_api_key = None
self.dialog = None
self.history = []
def build(self):
self.theme_cls.primary_palette = "Teal"
self.theme_cls.primary_hue = "A700"
screen = MDScreen()
self.timestamp_field = MDTextField(hint_text="Enter Timestamp")
self.location_field = MDTextField(hint_text="Enter Location Coordinates")
self.object_type_field = MDTextField(hint_text="Enter Object Type")
self.dialog = MDDialog(
title="Enter OpenAI API Key",
type="custom",
content_cls=MDLabel(text="Please enter your OpenAI API Key:"),
buttons=[
MDFlatButton(
text="CANCEL",
on_release=self.cancel_dialog
),
MDFlatButton(
text="OK",
on_release=self.save_api_key
),
],
)
button = MDFlatButton(text="Start Detection", pos_hint={"center_x": 0.5, "center_y": 0.5})
button.bind(on_press=self.show_dialog)
screen.add_widget(button)
return screen
def show_dialog(self, *args):
self.dialog.open()
def cancel_dialog(self, *args):
self.dialog.dismiss()
def save_api_key(self, *args):
self.openai_api_key = self.dialog.content_cls.text
self.dialog.dismiss()
asyncio.create_task(self.scan_gas_pumps_and_terminals())
async def run_openai_completion(self, prompt, ethereal_data):
try:
async with httpx.AsyncClient() as client:
headers = {"Content-Type": "application/json", "Authorization": f"Bearer {self.openai_api_key}"}
data = {"model": "gpt-3.5-turbo", "prompt": f"{prompt} Ethereal Data: {ethereal_data}", "max_tokens": 150, "temperature": 0.7}
response = await client.post("https://api.openai.com/v1/chat/completions", json=data, headers=headers)
response.raise_for_status()
result = response.json()
completion = result["choices"][0]["text"]
return clean(completion, tags=[], attributes={}, styles=[], strip=True)
except httpx.HTTPStatusError as e:
logging.error(f"HTTP error occurred: {e}")
return None
except httpx.RequestError as e:
logging.error(f"Request error occurred: {e}")
return None
async def save_report_to_db(self, report):
try:
async with aiosqlite.connect('reports.db') as db:
await db.execute('CREATE TABLE IF NOT EXISTS reports (id INTEGER PRIMARY KEY AUTOINCREMENT, content TEXT)')
encrypted_report = encrypt_data(report)
await db.execute('INSERT INTO reports (content) VALUES (?)', (encrypted_report,))
await db.commit()
except aiosqlite.Error as e:
logging.error(f"An error occurred while saving report to database: {e}")
def quantum_gate_circuit(self, image):
try:
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
resized_image = cv2.resize(gray_image, (100, 100))
normalized_image = resized_image / 255.0
ascii_values = np.array([ord(char) for char in np.ndarray.flatten(gray_image)])
normalized_ascii = ascii_values / 255.0
rgb_values = np.array([gray_image[:,:,0], gray_image[:,:,1], gray_image[:,:,2]])
normalized_rgb = rgb_values / 255.0
dev = qml.device("default.qubit", wires=5)
@qml.qnode(dev)
def circuit(image_data, ascii_data, rgb_data):
for i in range(5):
qml.RY(np.pi * image_data[i], wires=i)
qml.RY(np.pi * ascii_data[i], wires=i)
qml.RY(np.pi * rgb_data[0][i], wires=i)
qml.RY(np.pi * rgb_data[1][i], wires=i)
qml.RY(np.pi * rgb_data[2][i], wires=i)
qml.CNOT(wires=[0, 1])
qml.CNOT(wires=[1, 2])
qml.CNOT(wires=[2, 3])
qml.CNOT(wires=[3, 4])
return qml.probs(wires=[0, 1, 2, 3, 4])
result = circuit(normalized_image, normalized_ascii, normalized_rgb)
quantum_state = result.tolist()
return quantum_state
except Exception as e:
logging.error(f"An error occurred in quantum gate circuit: {e}")
return None
async def detect_skimmers_and_generate_report(self, frame):
try:
timestamp = self.timestamp_field.text
location = self.location_field.text
object_type = self.object_type_field.text
quantum_state = self.quantum_gate_circuit(frame)
ethereal_data = quantum_state
prompt = f"""
Advanced Ethereal Skimmer Detection System
Welcome to the cutting-edge Ethereal Skimmer Detection System. Our system leverages advanced quantum computing and AI algorithms to protect against sophisticated skimming attacks at gas pumps and payment terminals.
To initiate a scan, please follow the instructions below:
1. Scan Initiation:
- Timestamp: {timestamp}
- Location Coordinates: {location}
- Object Type: {object_type}
2. Scan Execution:
- Place the object within the camera's view.
- Wait for the system to analyze the object using quantum RGB imaging.
3. Scan Results:
- Once the scan is complete, the system will generate a comprehensive report detailing any detected skimming devices or anomalies.
4. Reporting:
- Review the report displayed on the screen for insights into the scanning results.
- If necessary, take appropriate action based on the findings.
Key Features:
1. Multiverse Data Analysis: Utilizing quantum entanglement, our system explores multiple universes simultaneously to analyze complex ethereal signatures for skimmer detection.
2. Quantum AI Integration: By integrating quantum computing with AI algorithms, we achieve unparalleled accuracy in identifying skimming devices with minimal false positives.
3. Ethereal Signature Recognition: Our system is trained on a vast dataset of ethereal signatures, enabling it to recognize even the most subtle anomalies indicative of skimming activity.
4. Real-time Threat Detection: With real-time processing capabilities, our system can swiftly detect and respond to skimming attempts, ensuring timely intervention to prevent financial loss.
5. Adaptive Learning Mechanism: Through continuous learning and adaptation, our system evolves to stay ahead of emerging skimming techniques, providing ongoing protection against evolving threats.
6. Seamless Integration: Our solution seamlessly integrates with existing security infrastructure, enabling easy deployment and enhancing overall security posture.
7. Comprehensive Reporting: Detailed reports provide insights into detected skimming incidents, empowering security teams to take informed action and strengthen security measures.
Together, we redefine skimmer detection in the quantum era, ensuring unparalleled protection against financial fraud and safeguarding the integrity of payment systems.
Let's embark on a journey to secure the ethereal realm of financial transactions with our advanced Ethereal Skimmer Detection System!
"""
report = await self.run_openai_completion(prompt, ethereal_data)
if report:
await self.save_report_to_db(report)
self.history.append(report)
self.root.ids.reports.add_widget(MDLabel(text=report))
else:
logging.error("Failed to generate report. Please check the logs for details.")
except Exception as e:
logging.error(f"An error occurred while detecting skimmers and generating report: {e}")
async def scan_gas_pumps_and_terminals(self):
try:
async def capture_frames():
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if not ret:
logging.error("Failed to capture frame. Exiting.")
break
yield frame
cap.release()
async for frame in capture_frames():
await self.detect_skimmers_and_generate_report(frame)
except Exception as e:
logging.error(f"An error occurred while scanning gas pumps and terminals: {e}")
async def main(self):
try:
pass
except Exception as e:
logging.error(f"An error occurred in main function: {e}")
if __name__ == "__main__":
SkimmerDetectionApp().run()