├── requirements.txt ├── docs ├── pi-stabilizer.jpeg ├── getting_started.md ├── roadmap.md ├── design_decisions.md └── architecture.md ├── .github └── ISSUE_TEMPLATE │ ├── custom.md │ ├── feature_request.md │ └── bug_report.md ├── .gitignore ├── Dockerfile ├── docker-compose.yml ├── security └── audits │ └── penetration_testing │ ├── vulnerability_db.json │ └── vulnerability_disclosure.py ├── monitoring ├── grafana │ ├── grafana_dashboard.py │ └── grafana_server.py └── prometheus │ ├── prometheus_config.py │ └── prometheus_server.py ├── tests ├── integration_tests │ ├── test_grafana_server.py │ └── test_prometheus_server.py └── unit_tests │ ├── test_grafana_dashboard.py │ └── test_prometheus_config.py ├── utils ├── constants.py └── helpers.py ├── infrastructure └── cloud │ ├── aws │ └── aws_cloud.py python │ ├── azure │ └── azure_cloud.py │ └── gcp │ └── gcp_cloud.py ├── LICENCE.md ├── README.md ├── src ├── data │ └── econometrics │ │ ├── data_visualization.py │ │ └── forecasting.py ├── governance │ └── smart_contracts │ │ └── voting_mechanisms.py └── ai-engine │ └── models │ ├── prophet.py │ ├── arima.py │ └── lstm.py ├── wallet ├── homomorphic_encryption.py ├── secure_multiparty_computation.py ├── zero_knowledge_proofs.py └── wallet_app.py ├── exchange └── dex.py ├── integration └── pi_network │ └── pi_coin_creation.py ├── index.html ├── payment_systems └── payment_gateway.py └── LICENSE /requirements.txt: -------------------------------------------------------------------------------- 1 | requests 2 | prometheus-client 3 | grafana-api 4 | python-json-logger 5 | -------------------------------------------------------------------------------- /docs/pi-stabilizer.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KOSASIH/pi-stabilizer/HEAD/docs/pi-stabilizer.jpeg -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/custom.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Custom issue template 3 | about: Describe this issue template's purpose here. 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | 11 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Docker files 2 | Dockerfile 3 | docker-compose.yml 4 | 5 | # Python files 6 | __pycache__/ 7 | *.pyc 8 | 9 | # Config files 10 | config/ 11 | 12 | # Data files 13 | data/ 14 | 15 | # Logs 16 | logs/ 17 | 18 | # Virtual environment 19 | .venv/ 20 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | # Use an official Python image as a base 2 | FROM python:3.9-slim 3 | 4 | # Set the working directory to /app 5 | WORKDIR /app 6 | 7 | # Copy the requirements file 8 | COPY requirements.txt . 9 | 10 | # Install the dependencies 11 | RUN pip install -r requirements.txt 12 | 13 | # Copy the application code 14 | COPY . . 15 | 16 | # Expose the port for Prometheus and Grafana 17 | EXPOSE 9090 3000 18 | 19 | # Run the command to start the application 20 | CMD ["python", "main.py"] 21 | -------------------------------------------------------------------------------- /docker-compose.yml: -------------------------------------------------------------------------------- 1 | version: '3' 2 | 3 | services: 4 | prometheus: 5 | build: . 6 | ports: 7 | - "9090:9090" 8 | depends_on: 9 | - pi-stabilizer 10 | environment: 11 | - PROMETHEUS_CONFIG_FILE=/app/config/prometheus.yml 12 | 13 | grafana: 14 | image: grafana/grafana:latest 15 | ports: 16 | - "3000:3000" 17 | depends_on: 18 | - prometheus 19 | environment: 20 | - GF_SECURITY_ADMIN_PASSWORD=password 21 | 22 | pi-stabilizer: 23 | build: . 24 | environment: 25 | - PI_STABILIZER_CONFIG_FILE=/app/config/pi-stabilizer.yml 26 | -------------------------------------------------------------------------------- /security/audits/penetration_testing/vulnerability_db.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "name": "SQL Injection", 4 | "description": "SQL injection vulnerability in login form", 5 | "severity": "High", 6 | "type": "sql_injection" 7 | }, 8 | { 9 | "name": "Cross-Site Scripting", 10 | "description": "XSS vulnerability in user input field", 11 | "severity": "Medium", 12 | "type": "cross_site_scripting" 13 | }, 14 | { 15 | "name": "Cross-Site Request Forgery", 16 | "description": "CSRF vulnerability in payment form", 17 | "severity": "Low", 18 | "type": "cross_site_request_forgery" 19 | } 20 | ] 21 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/feature_request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Feature request 3 | about: Suggest an idea for this project 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Is your feature request related to a problem? Please describe.** 11 | A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] 12 | 13 | **Describe the solution you'd like** 14 | A clear and concise description of what you want to happen. 15 | 16 | **Describe alternatives you've considered** 17 | A clear and concise description of any alternative solutions or features you've considered. 18 | 19 | **Additional context** 20 | Add any other context or screenshots about the feature request here. 21 | -------------------------------------------------------------------------------- /monitoring/grafana/grafana_dashboard.py: -------------------------------------------------------------------------------- 1 | import json 2 | 3 | class GrafanaDashboard: 4 | def __init__(self, dashboard_file): 5 | self.dashboard_file = dashboard_file 6 | self.dashboard = self.load_dashboard() 7 | 8 | def load_dashboard(self): 9 | with open(self.dashboard_file, 'r') as f: 10 | dashboard = json.load(f) 11 | return dashboard 12 | 13 | def get_panels(self): 14 | return self.dashboard.get('panels', []) 15 | 16 | def get_template_variables(self): 17 | return self.dashboard.get('templating', {}).get('list', []) 18 | 19 | def get_annotations(self): 20 | return self.dashboard.get('annotations', {}).get('list', []) 21 | -------------------------------------------------------------------------------- /tests/integration_tests/test_grafana_server.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | from monitoring.grafana.grafana_server import GrafanaServer 3 | 4 | class TestGrafanaServer(unittest.TestCase): 5 | def setUp(self): 6 | self.dashboard_file = 'path/to/dashboard.json' 7 | self.port = 8081 8 | self.server = GrafanaServer(self.dashboard_file, self.port) 9 | 10 | def test_start_server(self): 11 | self.server.start_server() 12 | self.assertTrue(self.server.httpd.is_running()) 13 | 14 | def test_render_dashboard(self): 15 | self.server.render_dashboard() 16 | # Verify that dashboard is rendered correctly 17 | 18 | if __name__ == '__main__': 19 | unittest.main() 20 | -------------------------------------------------------------------------------- /tests/integration_tests/test_prometheus_server.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | from monitoring.prometheus.prometheus_server import PrometheusServer 3 | 4 | class TestPrometheusServer(unittest.TestCase): 5 | def setUp(self): 6 | self.config_file = 'path/to/config.json' 7 | self.port = 8080 8 | self.server = PrometheusServer(self.config_file, self.port) 9 | 10 | def test_start_server(self): 11 | self.server.start_server() 12 | self.assertTrue(self.server.httpd.is_running()) 13 | 14 | def test_scrape_metrics(self): 15 | self.server.scrape_metrics() 16 | # Verify that metrics are scraped correctly 17 | 18 | if __name__ == '__main__': 19 | unittest.main() 20 | -------------------------------------------------------------------------------- /monitoring/prometheus/prometheus_config.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | 4 | class PrometheusConfig: 5 | def __init__(self, config_file): 6 | self.config_file = config_file 7 | self.config = self.load_config() 8 | 9 | def load_config(self): 10 | with open(self.config_file, 'r') as f: 11 | config = json.load(f) 12 | return config 13 | 14 | def get_scrape_configs(self): 15 | return self.config.get('scrape_configs', []) 16 | 17 | def get_alertmanager_config(self): 18 | return self.config.get('alertmanager', {}) 19 | 20 | def get_rule_files(self): 21 | return self.config.get('rule_files', []) 22 | 23 | def get_global_config(self): 24 | return self.config.get('global', {}) 25 | -------------------------------------------------------------------------------- /utils/constants.py: -------------------------------------------------------------------------------- 1 | """ 2 | Constants used throughout the application 3 | """ 4 | 5 | # HTTP status codes 6 | HTTP_OK = 200 7 | HTTP_BAD_REQUEST = 400 8 | HTTP_UNAUTHORIZED = 401 9 | HTTP_FORBIDDEN = 403 10 | HTTP_NOT_FOUND = 404 11 | HTTP_INTERNAL_SERVER_ERROR = 500 12 | 13 | # Prometheus constants 14 | PROMETHEUS_DEFAULT_PORT = 9090 15 | PROMETHEUS_DEFAULT_SCRAPE_INTERVAL = 10 # seconds 16 | 17 | # Grafana constants 18 | GRAFANA_DEFAULT_PORT = 3000 19 | GRAFANA_DEFAULT_DASHBOARD_WIDTH = 12 20 | GRAFANA_DEFAULT_DASHBOARD_HEIGHT = 6 21 | 22 | # Logging constants 23 | LOG_LEVEL_DEBUG = 'DEBUG' 24 | LOG_LEVEL_INFO = 'INFO' 25 | LOG_LEVEL_WARNING = 'WARNING' 26 | LOG_LEVEL_ERROR = 'ERROR' 27 | LOG_LEVEL_CRITICAL = 'CRITICAL' 28 | 29 | # Other constants 30 | DEFAULT_TIMEOUT = 30 # seconds 31 | MAX_RETRIES = 3 32 | -------------------------------------------------------------------------------- /tests/unit_tests/test_grafana_dashboard.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | from monitoring.grafana.grafana_dashboard import GrafanaDashboard 3 | 4 | class TestGrafanaDashboard(unittest.TestCase): 5 | def setUp(self): 6 | self.dashboard_file = 'path/to/dashboard.json' 7 | self.dashboard = GrafanaDashboard(self.dashboard_file) 8 | 9 | def test_load_dashboard(self): 10 | self.assertIsNotNone(self.dashboard.dashboard) 11 | 12 | def test_get_panels(self): 13 | panels = self.dashboard.get_panels() 14 | self.assertIsInstance(panels, list) 15 | 16 | def test_get_template_variables(self): 17 | template_variables = self.dashboard.get_template_variables() 18 | self.assertIsInstance(template_variables, list) 19 | 20 | def test_get_annotations(self): 21 | annotations = self.dashboard.get_annotations() 22 | self.assertIsInstance(annotations, list) 23 | 24 | if __name__ == '__main__': 25 | unittest.main() 26 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report 3 | about: Create a report to help us improve 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | **Describe the bug** 11 | A clear and concise description of what the bug is. 12 | 13 | **To Reproduce** 14 | Steps to reproduce the behavior: 15 | 1. Go to '...' 16 | 2. Click on '....' 17 | 3. Scroll down to '....' 18 | 4. See error 19 | 20 | **Expected behavior** 21 | A clear and concise description of what you expected to happen. 22 | 23 | **Screenshots** 24 | If applicable, add screenshots to help explain your problem. 25 | 26 | **Desktop (please complete the following information):** 27 | - OS: [e.g. iOS] 28 | - Browser [e.g. chrome, safari] 29 | - Version [e.g. 22] 30 | 31 | **Smartphone (please complete the following information):** 32 | - Device: [e.g. iPhone6] 33 | - OS: [e.g. iOS8.1] 34 | - Browser [e.g. stock browser, safari] 35 | - Version [e.g. 22] 36 | 37 | **Additional context** 38 | Add any other context about the problem here. 39 | -------------------------------------------------------------------------------- /docs/getting_started.md: -------------------------------------------------------------------------------- 1 | Getting Started 2 | --------------- 3 | 4 | This guide provides a step-by-step introduction to getting started with the Pi Stabilizer system. 5 | 6 | ### Prerequisites 7 | 8 | * Raspberry Pi device 9 | * Docker installed on the Raspberry Pi device 10 | * Internet connection 11 | 12 | ### Step 1: Clone the Repository 13 | 14 | * Clone the Pi Stabilizer repository: `git clone https://github.com/KOSASIH/pi-stabilizer.git` 15 | 16 | ### Step 2: Build the Docker Image 17 | 18 | * Build the Docker image: `docker-compose build` 19 | 20 | ### Step 3: Start the Application 21 | 22 | * Start the application: `docker-compose up` 23 | 24 | ### Step 4: Access the Grafana Dashboard 25 | 26 | * Access the Grafana dashboard: `http://localhost:3000` 27 | 28 | ### Step 5: Access the Prometheus Exporter 29 | 30 | * Access the Prometheus exporter: `http://localhost:9090` 31 | 32 | ### Troubleshooting 33 | 34 | * Check the Docker container logs for errors: `docker-compose logs` 35 | * Check the system logs for errors: `sudo journalctl -u pi-stabilizer` 36 | -------------------------------------------------------------------------------- /monitoring/prometheus/prometheus_server.py: -------------------------------------------------------------------------------- 1 | import http.server 2 | import socketserver 3 | 4 | class PrometheusServer: 5 | def __init__(self, config_file, port): 6 | self.config_file = config_file 7 | self.port = port 8 | self.config = PrometheusConfig(config_file) 9 | 10 | def start_server(self): 11 | Handler = http.server.SimpleHTTPRequestHandler 12 | with socketserver.TCPServer(("", self.port), Handler) as httpd: 13 | print(f"Prometheus server started on port {self.port}") 14 | httpd.serve_forever() 15 | 16 | def scrape_metrics(self): 17 | # Scrape metrics from targets 18 | for scrape_config in self.config.get_scrape_configs(): 19 | target = scrape_config['targets'][0] 20 | print(f"Scraping metrics from {target}") 21 | # Simulated metric scraping 22 | metrics = {'metric1': 10, 'metric2': 20} 23 | print(f"Metrics: {metrics}") 24 | 25 | def run(self): 26 | self.start_server() 27 | self.scrape_metrics() 28 | -------------------------------------------------------------------------------- /monitoring/grafana/grafana_server.py: -------------------------------------------------------------------------------- 1 | import http.server 2 | import socketserver 3 | 4 | class GrafanaServer: 5 | def __init__(self, dashboard_file, port): 6 | self.dashboard_file = dashboard_file 7 | self.port = port 8 | self.dashboard = GrafanaDashboard(dashboard_file) 9 | 10 | def start_server(self): 11 | Handler = http.server.SimpleHTTPRequestHandler 12 | with socketserver.TCPServer(("", self.port), Handler) as httpd: 13 | print(f"Grafana server started on port {self.port}") 14 | httpd.serve_forever() 15 | 16 | def render_dashboard(self): 17 | # Render the dashboard 18 | panels = self.dashboard.get_panels() 19 | template_variables = self.dashboard.get_template_variables() 20 | annotations = self.dashboard.get_annotations() 21 | print(f"Rendering dashboard with {len(panels)} panels, {len(template_variables)} template variables, and {len(annotations)} annotations") 22 | 23 | def run(self): 24 | self.start_server() 25 | self.render_dashboard() 26 | -------------------------------------------------------------------------------- /tests/unit_tests/test_prometheus_config.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | from monitoring.prometheus.prometheus_config import PrometheusConfig 3 | 4 | class TestPrometheusConfig(unittest.TestCase): 5 | def setUp(self): 6 | self.config_file = 'path/to/config.json' 7 | self.config = PrometheusConfig(self.config_file) 8 | 9 | def test_load_config(self): 10 | self.assertIsNotNone(self.config.config) 11 | 12 | def test_get_scrape_configs(self): 13 | scrape_configs = self.config.get_scrape_configs() 14 | self.assertIsInstance(scrape_configs, list) 15 | 16 | def test_get_alertmanager_config(self): 17 | alertmanager_config = self.config.get_alertmanager_config() 18 | self.assertIsInstance(alertmanager_config, dict) 19 | 20 | def test_get_rule_files(self): 21 | rule_files = self.config.get_rule_files() 22 | self.assertIsInstance(rule_files, list) 23 | 24 | def test_get_global_config(self): 25 | global_config = self.config.get_global_config() 26 | self.assertIsInstance(global_config, dict) 27 | 28 | if __name__ == '__main__': 29 | unittest.main() 30 | -------------------------------------------------------------------------------- /docs/roadmap.md: -------------------------------------------------------------------------------- 1 | Roadmap 2 | -------- 3 | 4 | This document outlines the roadmap for the Pi Stabilizer system. 5 | 6 | ### Short-term Goals (Next 3 Months) 7 | 8 | * Improve the stabilization algorithm to reduce latency and increase accuracy 9 | * Add support for additional Raspberry Pi devices 10 | * Improve the user interface and user experience of the Grafana dashboard 11 | 12 | ### Mid-term Goals (Next 6-12 Months) 13 | 14 | * Integrate with additional monitoring systems (e.g. Nagios, Zabbix) 15 | * Develop a mobile app for remote monitoring and control 16 | * Implement automated testing and continuous integration/continuous deployment (CI/CD) pipelines 17 | 18 | ### Long-term Goals (Next 1-2 Years) 19 | 20 | * Develop a cloud-based version of the Pi Stabilizer system 21 | * Integrate with machine learning algorithms for predictive maintenance 22 | * Expand support to other single-board computers (e.g. Asus Tinker Board, Pine64) 23 | 24 | ### Future Development 25 | 26 | * Explore the use of edge computing and IoT technologies to further improve the system's performance and scalability 27 | * Develop a community-driven plugin ecosystem for the Pi Stabilizer system 28 | * Investigate the use of blockchain technology for secure and transparent data storage and sharing 29 | -------------------------------------------------------------------------------- /infrastructure/cloud/aws/aws_cloud.py python: -------------------------------------------------------------------------------- 1 | import boto3 2 | 3 | class AWSCloud: 4 | def __init__(self, access_key_id, secret_access_key, region_name): 5 | self.ec2 = boto3.client('ec2', aws_access_key_id=access_key_id, aws_secret_access_key=secret_access_key, region_name=region_name) 6 | self.s3 = boto3.client('s3', aws_access_key_id=access_key_id, aws_secret_access_key=secret_access_key, region_name=region_name) 7 | 8 | def create_instance(self, instance_type, image_id, security_group_ids): 9 | # Create a new EC2 instance 10 | response = self.ec2.run_instances( 11 | ImageId=image_id, 12 | InstanceType=instance_type, 13 | SecurityGroupIds=security_group_ids, 14 | MaxCount=1, 15 | MinCount=1 16 | ) 17 | instance_id = response['Instances'][0]['InstanceId'] 18 | return instance_id 19 | 20 | def create_bucket(self, bucket_name): 21 | # Create a new S3 bucket 22 | response = self.s3.create_bucket(Bucket=bucket_name) 23 | return response['Bucket'] 24 | 25 | def upload_file(self, bucket_name, file_name, file_content): 26 | # Upload a file to S3 27 | response = self.s3.put_object(Body=file_content, Bucket=bucket_name, Key=file_name) 28 | return response 29 | -------------------------------------------------------------------------------- /docs/design_decisions.md: -------------------------------------------------------------------------------- 1 | Design Decisions 2 | ---------------- 3 | 4 | This document outlines the design decisions made during the development of the Pi Stabilizer system. 5 | 6 | ### Why Python? 7 | 8 | * Python is a popular and widely-used language that is well-suited for rapid prototyping and development. 9 | * It has a large community and a rich ecosystem of libraries and tools. 10 | * It is easy to learn and use, making it a great choice for developers of all skill levels. 11 | 12 | ### Why Prometheus and Grafana? 13 | 14 | * Prometheus is a popular and widely-used monitoring system that provides a scalable and flexible way to collect and store metrics. 15 | * Grafana is a popular and widely-used visualization tool that provides a customizable and easy-to-use way to display metrics. 16 | * Both Prometheus and Grafana have large communities and a rich ecosystem of plugins and integrations. 17 | 18 | ### Why Docker? 19 | 20 | * Docker provides a lightweight and portable way to package and deploy applications. 21 | * It allows for easy management and orchestration of containers. 22 | * It provides a consistent and reliable way to deploy applications across different environments. 23 | 24 | ### Why a Customizable Dashboard? 25 | 26 | * A customizable dashboard allows users to easily tailor the system to their specific needs and use cases. 27 | * It provides a flexible and adaptable way to display metrics and data. 28 | * It allows users to easily add or remove features and functionality as needed. 29 | -------------------------------------------------------------------------------- /infrastructure/cloud/azure/azure_cloud.py: -------------------------------------------------------------------------------- 1 | from azure.identity import DefaultAzureCredential 2 | from azure.mgmt.compute import ComputeManagementClient 3 | from azure.storage.blob import BlobServiceClient 4 | 5 | class AzureCloud: 6 | def __init__(self, subscription_id, resource_group, location): 7 | self.credential = DefaultAzureCredential() 8 | self.compute_client = ComputeManagementClient(self.credential, subscription_id) 9 | self.blob_service_client = BlobServiceClient(f"https://{resource_group}.blob.core.windows.net", self.credential) 10 | 11 | def create_instance(self, instance_name, vm_size, image_reference): 12 | # Create a new Azure instance 13 | body = { 14 | 'location': location, 15 | 'vm_size': vm_size, 16 | 'image_reference': image_reference 17 | } 18 | response = self.compute_client.virtual_machines.create_or_update(resource_group, instance_name, body) 19 | instance_id = response.id 20 | return instance_id 21 | 22 | def create_container(self, container_name): 23 | # Create a new Azure blob container 24 | container_client = self.blob_service_client.create_container(container_name) 25 | return container_client 26 | 27 | def upload_file(self, container_name, file_name, file_content): 28 | # Upload a file to Azure blob container 29 | blob_client = self.blob_service_client.get_blob_client(container_name, file_name) 30 | response = blob_client.upload_data(file_content, overwrite=True) 31 | return response 32 | -------------------------------------------------------------------------------- /docs/architecture.md: -------------------------------------------------------------------------------- 1 | Architecture 2 | ------------ 3 | 4 | The Pi Stabilizer system consists of several components that work together to provide real-time monitoring and stabilization of Raspberry Pi devices. The architecture is designed to be scalable, flexible, and easy to maintain. 5 | 6 | ### Components 7 | 8 | * **Pi Stabilizer Application**: A Python application that runs on the Raspberry Pi device and is responsible for stabilizing the device. It uses advanced algorithms to monitor and adjust the device's performance in real-time. 9 | * **Prometheus Exporter**: A Prometheus exporter that exposes metrics about the Pi device's stability. It is used to collect data about the device's performance and make it available for monitoring and analysis. 10 | * **Grafana Dashboard**: A customizable Grafana dashboard that provides a visual representation of the Pi device's stability metrics. It allows users to easily monitor and analyze the device's performance. 11 | * **Docker Container**: A Docker container that packages the Pi Stabilizer application, Prometheus exporter, and Grafana dashboard. It provides a lightweight and portable way to deploy the system. 12 | 13 | ### Data Flow 14 | 15 | 1. The Pi Stabilizer application collects data about the Raspberry Pi device's performance and sends it to the Prometheus exporter. 16 | 2. The Prometheus exporter exposes the data as metrics, which are then scraped by Prometheus. 17 | 3. Prometheus stores the metrics in its database, making them available for querying and analysis. 18 | 4. The Grafana dashboard queries Prometheus for the metrics and displays them in a visual format. 19 | 5. Users can access the Grafana dashboard to monitor and analyze the Pi device's performance. 20 | 21 | ### Benefits 22 | 23 | * Real-time monitoring of Pi stability 24 | * Advanced stabilization algorithm for optimal performance 25 | * Customizable Grafana dashboard for easy analysis 26 | * Scalable and flexible architecture for easy maintenance and updates 27 | -------------------------------------------------------------------------------- /utils/helpers.py: -------------------------------------------------------------------------------- 1 | """ 2 | Helper functions used throughout the application 3 | """ 4 | 5 | import requests 6 | import json 7 | import logging 8 | from typing import List, Dict, Any 9 | 10 | def make_request(url: str, method: str = 'GET', data: Dict[str, Any] = None, headers: Dict[str, str] = None) -> requests.Response: 11 | """ 12 | Make an HTTP request to the given URL 13 | """ 14 | try: 15 | response = requests.request(method, url, data=data, headers=headers) 16 | response.raise_for_status() 17 | return response 18 | except requests.RequestException as e: 19 | logging.error(f"Error making request to {url}: {e}") 20 | return None 21 | 22 | def parse_json(response: requests.Response) -> Dict[str, Any]: 23 | """ 24 | Parse the JSON response from an HTTP request 25 | """ 26 | try: 27 | return response.json() 28 | except json.JSONDecodeError as e: 29 | logging.error(f"Error parsing JSON response: {e}") 30 | return {} 31 | 32 | def get_logger(name: str) -> logging.Logger: 33 | """ 34 | Get a logger with the given name 35 | """ 36 | logger = logging.getLogger(name) 37 | logger.setLevel(logging.INFO) 38 | formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') 39 | handler = logging.StreamHandler() 40 | handler.setFormatter(formatter) 41 | logger.addHandler(handler) 42 | return logger 43 | 44 | def retry(func: callable, max_retries: int = MAX_RETRIES, timeout: int = DEFAULT_TIMEOUT) -> Any: 45 | """ 46 | Retry a function with exponential backoff 47 | """ 48 | retries = 0 49 | while retries < max_retries: 50 | try: 51 | return func() 52 | except Exception as e: 53 | logging.error(f"Error calling {func.__name__}: {e}") 54 | retries += 1 55 | time.sleep(timeout * (2 ** retries)) 56 | raise Exception(f"Failed to call {func.__name__} after {max_retries} retries") 57 | -------------------------------------------------------------------------------- /infrastructure/cloud/gcp/gcp_cloud.py: -------------------------------------------------------------------------------- 1 | from google.oauth2 import service_account 2 | from googleapiclient.discovery import build 3 | 4 | class GCPCloud: 5 | def __init__(self, credentials_file, project_id, zone): 6 | self.credentials = service_account.Credentials.from_service_account_file(credentials_file) 7 | self.compute = build('compute', 'v1', credentials=self.credentials) 8 | self.storage = build('storage', 'v1', credentials=self.credentials) 9 | 10 | def create_instance(self, instance_name, machine_type, image_project, image_family): 11 | # Create a new GCP instance 12 | body = { 13 | 'name': instance_name, 14 | 'machineType': f'zones/{zone}/machineTypes/{machine_type}', 15 | 'disks': [{ 16 | 'initializeParams': { 17 | 'diskSizeGb': '10' 18 | } 19 | }], 20 | 'networkInterfaces': [{ 21 | 'network': f'global/networks/default' 22 | }], 23 | 'bootDisk': { 24 | 'initializeParams': { 25 | 'diskSizeGb': '10' 26 | } 27 | } 28 | } 29 | response = self.compute.instances().insert(project=project_id, zone=zone, body=body).execute() 30 | instance_id = response['targetLink'] 31 | return instance_id 32 | 33 | def create_bucket(self, bucket_name): 34 | # Create a new GCP bucket 35 | body = { 36 | 'name': bucket_name 37 | } 38 | response = self.storage.buckets().insert(project=project_id, body=body).execute() 39 | bucket_name = response['name'] 40 | return bucket_name 41 | 42 | def upload_file(self, bucket_name, file_name, file_content): 43 | # Upload a file to GCP bucket 44 | body = { 45 | 'name': file_name 46 | } 47 | response = self.storage.objects().insert(bucket=bucket_name, body=body, media_body=file_content).execute() 48 | return response 49 | -------------------------------------------------------------------------------- /security/audits/penetration_testing/vulnerability_disclosure.py: -------------------------------------------------------------------------------- 1 | import requests 2 | import json 3 | from bs4 import BeautifulSoup 4 | 5 | class VulnerabilityDisclosure: 6 | def __init__(self, target_url, vulnerability_db): 7 | self.target_url = target_url 8 | self.vulnerability_db = vulnerability_db 9 | self.vulnerabilities = self.load_vulnerabilities() 10 | 11 | def load_vulnerabilities(self): 12 | with open(self.vulnerability_db, 'r') as f: 13 | vulnerabilities = json.load(f) 14 | return vulnerabilities 15 | 16 | def scan_target(self): 17 | # Scan the target URL for vulnerabilities 18 | response = requests.get(self.target_url) 19 | soup = BeautifulSoup(response.content, 'html.parser') 20 | findings = [] 21 | for vulnerability in self.vulnerabilities: 22 | if self.check_vulnerability(soup, vulnerability): 23 | findings.append(vulnerability) 24 | return findings 25 | 26 | def check_vulnerability(self, soup, vulnerability): 27 | # Check if the target is vulnerable to the given vulnerability 28 | if vulnerability['type'] == 'sql_injection': 29 | # Simulated SQL injection check 30 | if soup.find('input', {'name': 'username'}) and soup.find('input', {'name': 'password'}): 31 | return True 32 | elif vulnerability['type'] == 'cross_site_scripting': 33 | # Simulated XSS check 34 | if soup.find('script', {'src': 'https://example.com/malicious_script.js'}): 35 | return True 36 | return False 37 | 38 | def report_vulnerabilities(self, findings): 39 | # Generate a report of the found vulnerabilities 40 | report = [] 41 | for finding in findings: 42 | report.append({ 43 | 'vulnerability': finding['name'], 44 | 'description': finding['description'], 45 | 'severity': finding['severity'] 46 | }) 47 | return report 48 | 49 | def run(self): 50 | findings = self.scan_target() 51 | report = self.report_vulnerabilities(findings) 52 | print(f"Vulnerability report for {self.target_url}:") 53 | for entry in report: 54 | print(f" {entry['vulnerability']}: {entry['description']} (Severity: {entry['severity']})") 55 | -------------------------------------------------------------------------------- /LICENCE.md: -------------------------------------------------------------------------------- 1 | PiOS License 2 | 3 | Copyright (C) 2024 KOSASIH 4 | 5 | Permission is hereby granted by the application software developer (“Software Developer”), free 6 | of charge, to any person obtaining a copy of this application, software and associated 7 | documentation files (the “Software”), which was developed by the Software Developer for use on 8 | Pi Network, whereby the purpose of this license is to permit the development of derivative works 9 | based on the Software, including the right to use, copy, modify, merge, publish, distribute, 10 | sub-license, and/or sell copies of such derivative works and any Software components incorporated 11 | therein, and to permit persons to whom such derivative works are furnished to do so, in each case, 12 | solely to develop, use and market applications for the official Pi Network. For purposes of this 13 | license, Pi Network shall mean any application, software, or other present or future platform 14 | developed, owned or managed by Pi Community Company, and its parents, affiliates or subsidiaries, 15 | for which the Software was developed, or on which the Software continues to operate. However, 16 | you are prohibited from using any portion of the Software or any derivative works thereof in any 17 | manner (a) which infringes on any Pi Network intellectual property rights, (b) to hack any of Pi 18 | Network’s systems or processes or (c) to develop any product or service which is competitive with 19 | the Pi Network. 20 | 21 | The above copyright notice and this permission notice shall be included in all copies or 22 | substantial portions of the Software. 23 | 24 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, 25 | INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE 26 | AND NON-INFRINGEMENT. IN NO EVENT SHALL THE AUTHORS, PUBLISHERS, OR COPYRIGHT HOLDERS OF THIS 27 | SOFTWARE BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY OR CONSEQUENTIAL 28 | DAMAGES (INCLUDING, BUT NOT LIMITED TO BUSINESS INTERRUPTION, LOSS OF USE, DATA OR PROFITS) 29 | HOWEVER CAUSED AND UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR 30 | TORT (INCLUDING NEGLIGENCE) ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE 31 | OR OTHER DEALINGS IN THE SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. 32 | 33 | Pi, Pi Network and the Pi logo are trademarks of the Pi Community Company. 34 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![Static Badge](https://img.shields.io/badge/%E2%9A%99-PiStabilizer-blue) 2 | 3 |

PiStabilizer by KOSASIH is licensed under Creative Commons Attribution 4.0 International

4 | 5 | # pi-stabilizer 6 | The main repository for the PiStabilizer project, containing the core codebase and documentation. 7 | 8 | Pi Stabilizer 9 | ============= 10 | 11 | The Pi Stabilizer is a high-tech system for stabilizing Raspberry Pi devices. This repository contains the source code for the system, which includes a Prometheus exporter, a Grafana dashboard, and a Python application for stabilizing the Pi. 12 | 13 | Features 14 | -------- 15 | 16 | * Real-time monitoring of Pi stability using Prometheus 17 | * Customizable Grafana dashboard for visualizing stability metrics 18 | * Advanced stabilization algorithm for maintaining optimal Pi performance 19 | * Dockerized deployment for easy setup and management 20 | 21 | Getting Started 22 | --------------- 23 | 24 | 1. Clone the repository: `git clone https://github.com/KOSASIH/pi-stabilizer.git` 25 | 2. Build the Docker image: `docker-compose build` 26 | 3. Start the application: `docker-compose up` 27 | 4. Access the Grafana dashboard: `http://localhost:3000` 28 | 5. Access the Prometheus exporter: `http://localhost:9090` 29 | 30 | Configuration 31 | ------------- 32 | 33 | The system can be configured using environment variables and configuration files. See the `config` directory for examples of configuration files. 34 | 35 | Contributing 36 | ------------ 37 | 38 | Contributions are welcome! Please submit pull requests to the `main` branch. 39 | 40 | License 41 | ------- 42 | 43 | The Pi Stabilizer is licensed under the Apache 2.0 License. See `LICENSE` for details. 44 | -------------------------------------------------------------------------------- /src/data/econometrics/data_visualization.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | import seaborn as sns 5 | from statsmodels.graphics.tsaplots import plot_acf, plot_pacf 6 | from statsmodels.tsa.seasonal import seasonal_decompose 7 | from statsmodels.tsa.stattools import adfuller 8 | 9 | class DataVisualization: 10 | def __init__(self, data): 11 | self.data = data 12 | 13 | def plot_time_series(self): 14 | plt.figure(figsize=(12, 6)) 15 | plt.plot(self.data) 16 | plt.title("Time Series Plot") 17 | plt.xlabel("Time") 18 | plt.ylabel("Value") 19 | plt.show() 20 | 21 | def plot_autocorrelation(self): 22 | plot_acf(self.data) 23 | plt.title("Autocorrelation Plot") 24 | plt.show() 25 | 26 | def plot_partial_autocorrelation(self): 27 | plot_pacf(self.data) 28 | plt.title("Partial Autocorrelation Plot") 29 | plt.show() 30 | 31 | def plot_seasonal_decomposition(self): 32 | decomposition = seasonal_decompose(self.data, model='additive') 33 | trend = decomposition.trend 34 | seasonal = decomposition.seasonal 35 | residual = decomposition.resid 36 | plt.figure(figsize=(12, 6)) 37 | plt.subplot(411) 38 | plt.plot(self.data, label='Original') 39 | plt.legend(loc='best') 40 | plt.subplot(412) 41 | plt.plot(trend, label='Trend') 42 | plt.legend(loc='best') 43 | plt.subplot(413) 44 | plt.plot(seasonal, label='Seasonality') 45 | plt.legend(loc='best') 46 | plt.subplot(414) 47 | plt.plot(residual, label='Residuals') 48 | plt.legend(loc='best') 49 | plt.tight_layout() 50 | plt.show() 51 | 52 | def plot_distribution(self): 53 | sns.distplot(self.data) 54 | plt.title("Distribution Plot") 55 | plt.show() 56 | 57 | def test_stationarity(self): 58 | result = adfuller(self.data) 59 | print("ADF Statistic: %f" % result[0]) 60 | print("p-value: %f" % result[1]) 61 | if result[1] < 0.05: 62 | print("Reject null hypothesis: The time series is likely stationary") 63 | else: 64 | print("Fail to reject null hypothesis: The time series is likely non-stationary") 65 | 66 | def visualize(self): 67 | self.plot_time_series() 68 | self.plot_autocorrelation() 69 | self.plot_partial_autocorrelation() 70 | self.plot_seasonal_decomposition() 71 | self.plot_distribution() 72 | self.test_stationarity() 73 | -------------------------------------------------------------------------------- /src/governance/smart_contracts/voting_mechanisms.py: -------------------------------------------------------------------------------- 1 | import hashlib 2 | from ecdsa import SigningKey, VerifyingKey 3 | from ecdsa.util import sigdecode_der 4 | 5 | class VotingMechanism: 6 | def __init__(self, contract_address, voting_power_distribution): 7 | self.contract_address = contract_address 8 | self.voting_power_distribution = voting_power_distribution 9 | self.votes_cast = {} 10 | 11 | def generate_voting_key(self, voter_id): 12 | private_key = SigningKey.from_secret_exponent(voter_id, curve=ecdsa.SECP256k1) 13 | public_key = private_key.verifying_key 14 | return private_key, public_key 15 | 16 | def cast_vote(self, voter_id, vote, private_key): 17 | vote_hash = hashlib.sha256(vote.encode()).hexdigest() 18 | signature = private_key.sign(vote_hash.encode()) 19 | self.votes_cast[voter_id] = {"vote": vote, "signature": signature} 20 | 21 | def verify_vote(self, voter_id, vote, public_key): 22 | vote_hash = hashlib.sha256(vote.encode()).hexdigest() 23 | signature = self.votes_cast[voter_id]["signature"] 24 | try: 25 | public_key.verify(signature, vote_hash.encode()) 26 | return True 27 | except: 28 | return False 29 | 30 | def tally_votes(self): 31 | vote_counts = {} 32 | for vote in self.votes_cast.values(): 33 | if vote["vote"] not in vote_counts: 34 | vote_counts[vote["vote"]] = 0 35 | vote_counts[vote["vote"]] += self.voting_power_distribution[vote["voter_id"]] 36 | return vote_counts 37 | 38 | def determine_winner(self): 39 | vote_counts = self.tally_votes() 40 | winner = max(vote_counts, key=vote_counts.get) 41 | return winner 42 | 43 | def execute_contract(self, winner): 44 | # Execute the smart contract based on the winner 45 | pass 46 | 47 | class PluralityVoting(VotingMechanism): 48 | def __init__(self, contract_address, voting_power_distribution): 49 | super().__init__(contract_address, voting_power_distribution) 50 | 51 | class InstantRunoffVoting(VotingMechanism): 52 | def __init__(self, contract_address, voting_power_distribution): 53 | super().__init__(contract_address, voting_power_distribution) 54 | 55 | def tally_votes(self): 56 | # Implement instant runoff voting logic 57 | pass 58 | 59 | class SingleTransferableVote(VotingMechanism): 60 | def __init__(self, contract_address, voting_power_distribution): 61 | super().__init__(contract_address, voting_power_distribution) 62 | 63 | def tally_votes(self): 64 | # Implement single transferable vote logic 65 | pass 66 | -------------------------------------------------------------------------------- /src/data/econometrics/forecasting.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | from statsmodels.tsa.arima_model import ARIMA 4 | from statsmodels.tsa.statespace.sarimax import SARIMAX 5 | from sklearn.metrics import mean_squared_error 6 | from sklearn.model_selection import TimeSeriesSplit 7 | 8 | class Forecasting: 9 | def __init__(self, data, order, seasonal_order): 10 | self.data = data 11 | self.order = order 12 | self.seasonal_order = seasonal_order 13 | 14 | def fit_arima(self): 15 | self.model = ARIMA(self.data, order=self.order) 16 | self.model_fit = self.model.fit(disp=0) 17 | 18 | def fit_sarimax(self): 19 | self.model = SARIMAX(self.data, order=self.order, seasonal_order=self.seasonal_order) 20 | self.model_fit = self.model.fit(disp=0) 21 | 22 | def forecast(self, steps): 23 | forecast = self.model_fit.forecast(steps=steps) 24 | return forecast 25 | 26 | def evaluate(self, test_data): 27 | predictions = self.model_fit.predict(start=len(self.data) - len(test_data), end=len(self.data) - 1) 28 | mse = mean_squared_error(test_data, predictions) 29 | rmse = np.sqrt(mse) 30 | return rmse 31 | 32 | def walk_forward_validation(self, test_size=0.2): 33 | tscv = TimeSeriesSplit(n_splits=5) 34 | scores = [] 35 | for train_index, test_index in tscv.split(self.data): 36 | X_train, X_test = self.data[train_index], self.data[test_index] 37 | self.fit_arima() 38 | score = self.evaluate(X_test) 39 | scores.append(score) 40 | return scores 41 | 42 | def hyperparameter_tuning(self): 43 | def objective(params): 44 | order = params["order"] 45 | seasonal_order = params["seasonal_order"] 46 | self.order = order 47 | self.seasonal_order = seasonal_order 48 | self.fit_sarimax() 49 | score = self.walk_forward_validation() 50 | return score 51 | 52 | space = { 53 | "order": hp.quniform("order", 1, 5, 1), 54 | "seasonal_order": hp.quniform("seasonal_order", 1, 5, 1) 55 | } 56 | trials = Trials() 57 | best = fmin(objective, space, algo=tpe.suggest, max_evals=50, trials=trials) 58 | return best 59 | 60 | def plot_forecast(self, steps): 61 | forecast = self.forecast(steps) 62 | plt.plot(forecast) 63 | plt.title("Forecast") 64 | plt.xlabel("Time") 65 | plt.ylabel("Value") 66 | plt.show() 67 | 68 | def plot_residuals(self): 69 | residuals = self.model_fit.resid 70 | plt.plot(residuals) 71 | plt.title("Residuals") 72 | plt.xlabel("Time") 73 | plt.ylabel("Value") 74 | plt.show() 75 | 76 | def plot_diagnostics(self): 77 | self.model_fit.plot_diagnostics() 78 | plt.show() 79 | -------------------------------------------------------------------------------- /wallet/homomorphic_encryption.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | from cryptography.hazmat.primitives import serialization 4 | from cryptography.hazmat.primitives.asymmetric import ec 5 | from cryptography.hazmat.primitives import hashes 6 | from cryptography.hazmat.backends import default_backend 7 | 8 | class HomomorphicEncryption: 9 | def __init__(self): 10 | self.ec_curve = ec.SECP256R1() 11 | self.ec_group = self.ec_curve.curve 12 | 13 | def generate_keypair(self): 14 | # Generate a keypair for homomorphic encryption 15 | private_key = ec.generate_private_key(self.ec_curve, default_backend()) 16 | public_key = private_key.public_key() 17 | return private_key, public_key 18 | 19 | def encrypt(self, private_key, plaintext): 20 | # Encrypt the plaintext using the private key 21 | ciphertext = self._encrypt(private_key, plaintext) 22 | return ciphertext 23 | 24 | def _encrypt(self, private_key, plaintext): 25 | # Use the Elliptic Curve Diffie-Hellman key exchange to encrypt the plaintext 26 | shared_secret = private_key.exchange(ec.ECDH(), private_key.public_key()) 27 | ciphertext = self._encrypt_with_shared_secret(shared_secret, plaintext) 28 | return ciphertext 29 | 30 | def _encrypt_with_shared_secret(self, shared_secret, plaintext): 31 | # Use the shared secret to encrypt the plaintext 32 | iv = os.urandom(16) 33 | cipher = self._create_cipher(shared_secret, iv) 34 | ciphertext = cipher.encrypt(plaintext) 35 | return iv + ciphertext 36 | 37 | def _create_cipher(self, shared_secret, iv): 38 | # Create a cipher object using the shared secret and IV 39 | cipher = Cipher(algorithms.AES(shared_secret), modes.CBC(iv), backend=default_backend()) 40 | return cipher 41 | 42 | def decrypt(self, private_key, ciphertext): 43 | # Decrypt the ciphertext using the private key 44 | plaintext = self._decrypt(private_key, ciphertext) 45 | return plaintext 46 | 47 | def _decrypt(self, private_key, ciphertext): 48 | # Use the Elliptic Curve Diffie-Hellman key exchange to decrypt the ciphertext 49 | shared_secret = private_key.exchange(ec.ECDH(), private_key.public_key()) 50 | plaintext = self._decrypt_with_shared_secret(shared_secret, ciphertext) 51 | return plaintext 52 | 53 | def _decrypt_with_shared_secret(self, shared_secret, ciphertext): 54 | # Use the shared secret to decrypt the ciphertext 55 | iv = ciphertext[:16] 56 | cipher = self._create_cipher(shared_secret, iv) 57 | plaintext = cipher.decrypt(ciphertext[16:]) 58 | return plaintext 59 | 60 | def compute_encrypted_value(self, encrypted_value1, encrypted_value2): 61 | # Compute the encrypted value using homomorphic addition 62 | return self._compute_encrypted_value(encrypted_value1, encrypted_value2) 63 | 64 | def _compute_encrypted_value(self, encrypted_value1, encrypted_value2): 65 | # Use the homomorphic property to add the encrypted values 66 | return encrypted_value1 + encrypted_value2 67 | -------------------------------------------------------------------------------- /wallet/secure_multiparty_computation.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | from cryptography.hazmat.primitives import serialization 4 | from cryptography.hazmat.primitives.asymmetric import ec 5 | from cryptography.hazmat.primitives import hashes 6 | from cryptography.hazmat.backends import default_backend 7 | from wallet.homomorphic_encryption import HomomorphicEncryption 8 | from wallet.zero_knowledge_proofs import ZeroKnowledgeProofs 9 | 10 | class SecureMultipartyComputation: 11 | def __init__(self): 12 | self.homomorphic_encryption = HomomorphicEncryption() 13 | self.zero_knowledge_proofs = ZeroKnowledgeProofs() 14 | 15 | def compute_balance(self, encrypted_balance_query): 16 | # Use homomorphic encryption to compute the balance 17 | encrypted_balance = self.homomorphic_encryption.compute_encrypted_value(encrypted_balance_query) 18 | # Use zero-knowledge proofs to verify the balance 19 | proof = self.zero_knowledge_proofs.generate_proof(encrypted_balance) 20 | return proof 21 | 22 | def compute_transaction(self, private_key, recipient, amount): 23 | # Use homomorphic encryption to encrypt the transaction 24 | encrypted_transaction = self.homomorphic_encryption.encrypt(private_key, recipient, amount) 25 | # Use zero-knowledge proofs to verify the transaction 26 | proof = self.zero_knowledge_proofs.generate_proof(encrypted_transaction) 27 | return proof 28 | 29 | def verify_transaction(self, decrypted_transaction): 30 | # Use zero-knowledge proofs to verify the transaction 31 | proof = self.zero_knowledge_proofs.generate_proof(decrypted_transaction) 32 | return proof 33 | 34 | def generate_shares(self, secret, num_shares, threshold): 35 | # Use Shamir's Secret Sharing to generate shares 36 | shares = [] 37 | for i in range(num_shares): 38 | share = self._generate_share(secret, i, num_shares, threshold) 39 | shares.append(share) 40 | return shares 41 | 42 | def _generate_share(self, secret, i, num_shares, threshold): 43 | # Use polynomial interpolation to generate a share 44 | coefficients = self._generate_coefficients(secret, threshold) 45 | share = 0 46 | for j in range(threshold): 47 | share += coefficients[j] * (i ** j) 48 | return share 49 | 50 | def _generate_coefficients(self, secret, threshold): 51 | # Use random coefficients for the polynomial 52 | coefficients = [] 53 | for i in range(threshold): 54 | coefficient = os.urandom(32) 55 | coefficients.append(coefficient) 56 | return coefficients 57 | 58 | def reconstruct_secret(self, shares, threshold): 59 | # Use Lagrange interpolation to reconstruct the secret 60 | secret = 0 61 | for i in range(threshold): 62 | secret += shares[i] * self._lagrange_basis(i, shares, threshold) 63 | return secret 64 | 65 | def _lagrange_basis(self, i, shares, threshold): 66 | # Compute the Lagrange basis polynomial 67 | basis = 1 68 | for j in range(threshold): 69 | if i != j: 70 | basis *= (shares[i] - shares[j]) / (i - j) 71 | return basis 72 | -------------------------------------------------------------------------------- /wallet/zero_knowledge_proofs.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | from cryptography.hazmat.primitives import serialization 4 | from cryptography.hazmat.primitives.asymmetric import ec 5 | from cryptography.hazmat.primitives import hashes 6 | from cryptography.hazmat.backends import default_backend 7 | 8 | class ZeroKnowledgeProofs: 9 | def __init__(self): 10 | self.ec_curve = ec.SECP256R1() 11 | self.ec_group = self.ec_curve.curve 12 | 13 | def generate_proof(self, statement): 14 | # Generate a zero-knowledge proof for the statement 15 | proof = self._generate_proof(statement) 16 | return proof 17 | 18 | def _generate_proof(self, statement): 19 | # Use the Fiat-Shamir heuristic to generate a zero-knowledge proof 20 | challenge = self._generate_challenge(statement) 21 | response = self._generate_response(challenge, statement) 22 | proof = self._create_proof(challenge, response) 23 | return proof 24 | 25 | def _generate_challenge(self, statement): 26 | # Generate a random challenge for the proof 27 | challenge = os.urandom(32) 28 | return challenge 29 | 30 | def _generate_response(self, challenge, statement): 31 | # Generate a response to the challenge using the statement 32 | response = self._compute_response(challenge, statement) 33 | return response 34 | 35 | def _compute_response(self, challenge, statement): 36 | # Use the Elliptic Curve Digital Signature Algorithm (ECDSA) to compute the response 37 | private_key = self._generate_private_key() 38 | signature = private_key.sign(challenge, ec.ECDSA(self.ec_curve), default_backend()) 39 | response = signature.to_bytes((signature.bit_length() + 7) // 8, 'big') 40 | return response 41 | 42 | def _generate_private_key(self): 43 | # Generate a private key for the proof 44 | private_key = ec.generate_private_key(self.ec_curve, default_backend()) 45 | return private_key 46 | 47 | def _create_proof(self, challenge, response): 48 | # Create a proof object containing the challenge and response 49 | proof = { 50 | 'challenge': challenge.hex(), 51 | 'response': response.hex() 52 | } 53 | return proof 54 | 55 | def verify_proof(self, proof, statement): 56 | # Verify the zero-knowledge proof 57 | valid = self._verify_proof(proof, statement) 58 | return valid 59 | 60 | def _verify_proof(self, proof, statement): 61 | # Use the Fiat-Shamir heuristic to verify the proof 62 | challenge = bytes.fromhex(proof['challenge']) 63 | response = bytes.fromhex(proof['response']) 64 | valid = self._verify_response(challenge, response, statement) 65 | return valid 66 | 67 | def _verify_response(self, challenge, response, statement): 68 | # Use the Elliptic Curve Digital Signature Algorithm (ECDSA) to verify the response 69 | public_key = self._generate_public_key() 70 | signature = ec.ECDSA(self.ec_curve).verify(response, challenge, public_key, default_backend()) 71 | return signature 72 | 73 | def _generate_public_key(self): 74 | # Generate a public key for the proof 75 | private_key = self._generate_private_key() 76 | public_key = private_key.public_key() 77 | return public_key 78 | -------------------------------------------------------------------------------- /exchange/dex.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | from cryptography.hazmat.primitives import serialization 4 | from cryptography.hazmat.primitives.asymmetric import ec 5 | from cryptography.hazmat.primitives import hashes 6 | from cryptography.hazmat.backends import default_backend 7 | from wallet.homomorphic_encryption import HomomorphicEncryption 8 | from wallet.zero_knowledge_proofs import ZeroKnowledgeProofs 9 | 10 | class DEX: 11 | def __init__(self): 12 | self.homomorphic_encryption = HomomorphicEncryption() 13 | self.zero_knowledge_proofs = ZeroKnowledgeProofs() 14 | self.order_book = {} 15 | 16 | def create_order(self, user_id, asset_id, amount, price): 17 | # Create a new order for the user 18 | order = { 19 | 'user_id': user_id, 20 | 'asset_id': asset_id, 21 | 'amount': amount, 22 | 'price': price 23 | } 24 | self.order_book[asset_id] = self.order_book.get(asset_id, []) 25 | self.order_book[asset_id].append(order) 26 | return order 27 | 28 | def execute_trade(self, buyer_id, seller_id, asset_id, amount, price): 29 | # Execute a trade between the buyer and seller 30 | buyer_order = self.get_order(buyer_id, asset_id, amount, price) 31 | seller_order = self.get_order(seller_id, asset_id, -amount, price) 32 | if buyer_order and seller_order: 33 | self.execute_trade_internal(buyer_order, seller_order) 34 | return True 35 | return False 36 | 37 | def get_order(self, user_id, asset_id, amount, price): 38 | # Get an order from the order book 39 | for order in self.order_book.get(asset_id, []): 40 | if order['user_id'] == user_id and order['amount'] == amount and order['price'] == price: 41 | return order 42 | return None 43 | 44 | def execute_trade_internal(self, buyer_order, seller_order): 45 | # Execute the trade internally 46 | buyer_private_key = self.get_private_key(buyer_order['user_id']) 47 | seller_private_key = self.get_private_key(seller_order['user_id']) 48 | encrypted_amount = self.homomorphic_encryption.encrypt(buyer_private_key, str(buyer_order['amount'])) 49 | proof = self.zero_knowledge_proofs.generate_proof(encrypted_amount) 50 | self.verify_proof(proof, encrypted_amount) 51 | self.transfer_assets(buyer_order['user_id'], seller_order['user_id'], buyer_order['asset_id'], buyer_order['amount']) 52 | 53 | def get_private_key(self, user_id): 54 | # Get the private key for the user 55 | # This is a simulated implementation and not secure 56 | # In a real-world implementation, you would need to use secure and tested libraries and protocols for key management 57 | private_key = ec.generate_private_key(ec.SECP256R1(), default_backend()) 58 | return private_key 59 | 60 | def transfer_assets(self, from_user_id, to_user_id, asset_id, amount): 61 | # Transfer assets from one user to another 62 | # This is a simulated implementation and not secure 63 | # In a real-world implementation, you would need to use secure and tested libraries and protocols for asset management 64 | print(f"Transferred {amount} {asset_id} from {from_user_id} to {to_user_id}") 65 | 66 | def verify_proof(self, proof, statement): 67 | # Verify the zero-knowledge proof 68 | valid = self.zero_knowledge_proofs.verify_proof(proof, statement) 69 | return valid 70 | -------------------------------------------------------------------------------- /integration/pi_network/pi_coin_creation.py: -------------------------------------------------------------------------------- 1 | import hashlib 2 | import ecdsa 3 | import binascii 4 | from ecdsa.util import sigdecode_der 5 | 6 | class PiCoin: 7 | def __init__(self, name, symbol, total_supply): 8 | self.name = name 9 | self.symbol = symbol 10 | self.total_supply = total_supply 11 | self.blockchain = [] 12 | 13 | def generate_genesis_block(self): 14 | genesis_block = { 15 | "index": 0, 16 | "previous_hash": "0" * 64, 17 | "transactions": [], 18 | "timestamp": int(time.time()), 19 | "nonce": 0 20 | } 21 | self.blockchain.append(genesis_block) 22 | 23 | def create_transaction(self, sender, recipient, amount): 24 | transaction = { 25 | "sender": sender, 26 | "recipient": recipient, 27 | "amount": amount, 28 | "timestamp": int(time.time()) 29 | } 30 | return transaction 31 | 32 | def add_transaction(self, transaction): 33 | self.blockchain[-1]["transactions"].append(transaction) 34 | 35 | def mine_block(self, miner): 36 | previous_hash = self.blockchain[-1]["previous_hash"] 37 | nonce = 0 38 | while True: 39 | block_hash = self.calculate_block_hash(nonce, previous_hash) 40 | if self.validate_proof(block_hash): 41 | break 42 | nonce += 1 43 | new_block = { 44 | "index": len(self.blockchain), 45 | "previous_hash": previous_hash, 46 | "transactions": self.blockchain[-1]["transactions"], 47 | "timestamp": int(time.time()), 48 | "nonce": nonce 49 | } 50 | self.blockchain.append(new_block) 51 | self.reward_miner(miner) 52 | 53 | def calculate_block_hash(self, nonce, previous_hash): 54 | block_string = str(nonce) + previous_hash + str(self.blockchain[-1]["transactions"]) 55 | return hashlib.sha256(block_string.encode()).hexdigest() 56 | 57 | def validate_proof(self, block_hash): 58 | return block_hash[:4] == "0000" 59 | 60 | def reward_miner(self, miner): 61 | reward_transaction = self.create_transaction("PiCoin", miner, 10) 62 | self.add_transaction(reward_transaction) 63 | 64 | def get_balance(self, address): 65 | balance = 0 66 | for block in self.blockchain: 67 | for transaction in block["transactions"]: 68 | if transaction["recipient"] == address: 69 | balance += transaction["amount"] 70 | elif transaction["sender"] == address: 71 | balance -= transaction["amount"] 72 | return balance 73 | 74 | class PiWallet: 75 | def __init__(self): 76 | self.private_key = ecdsa.SigningKey.from_secret_exponent(123, curve=ecdsa.SECP256k1) 77 | self.public_key = self.private_key.verifying_key 78 | 79 | def generate_address(self): 80 | return binascii.hexlify(self.public_key.to_string()).decode() 81 | 82 | def sign_transaction(self, transaction): 83 | transaction_hash = hashlib.sha256(str(transaction).encode()).hexdigest() 84 | signature = self.private_key.sign(transaction_hash.encode()) 85 | return signature 86 | 87 | def verify_transaction(self, transaction, signature, public_key): 88 | transaction_hash = hashlib.sha256(str(transaction).encode()).hexdigest() 89 | try: 90 | public_key.verify(signature, transaction_hash.encode()) 91 | return True 92 | except: 93 | return False 94 | -------------------------------------------------------------------------------- /src/ai-engine/models/prophet.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | from fbprophet import Prophet 4 | from sklearn.metrics import mean_squared_error 5 | from sklearn.model_selection import TimeSeriesSplit 6 | from hyperopt import hp, fmin, tpe, Trials 7 | import matplotlib.pyplot as plt 8 | 9 | class ProphetModel: 10 | def __init__(self, data, seasonality_mode='additive', growth='linear'): 11 | self.data = data 12 | self.seasonality_mode = seasonality_mode 13 | self.growth = growth 14 | self.model = None 15 | self.model_fit = None 16 | self.params = None 17 | 18 | def fit(self): 19 | self.model = Prophet(seasonality_mode=self.seasonality_mode, growth=self.growth) 20 | self.model.fit(self.data) 21 | 22 | def _auto_seasonality_mode(self): 23 | def objective(params): 24 | seasonality_mode = params["seasonality_mode"] 25 | model = Prophet(seasonality_mode=seasonality_mode, growth=self.growth) 26 | model.fit(self.data) 27 | return model.rsquare() 28 | 29 | space = { 30 | "seasonality_mode": hp.choice("seasonality_mode", ['additive', 'multiplicative']) 31 | } 32 | trials = Trials() 33 | best = fmin(objective, space, algo=tpe.suggest, max_evals=10, trials=trials) 34 | return best["seasonality_mode"] 35 | 36 | def _auto_growth(self): 37 | def objective(params): 38 | growth = params["growth"] 39 | model = Prophet(seasonality_mode=self.seasonality_mode, growth=growth) 40 | model.fit(self.data) 41 | return model.rsquare() 42 | 43 | space = { 44 | "growth": hp.choice("growth", ['linear', 'logistic']) 45 | } 46 | trials = Trials() 47 | best = fmin(objective, space, algo=tpe.suggest, max_evals=10, trials=trials) 48 | return best["growth"] 49 | 50 | def forecast(self, steps): 51 | future = self.model.make_future_dataframe(periods=steps) 52 | forecast = self.model.predict(future) 53 | return forecast 54 | 55 | def evaluate(self, test_data): 56 | predictions = self.model.predict(test_data) 57 | mse = mean_squared_error(test_data['y'], predictions['yhat']) 58 | rmse = np.sqrt(mse) 59 | return rmse 60 | 61 | def plot_forecast(self, steps): 62 | forecast = self.forecast(steps) 63 | self.model.plot(forecast) 64 | plt.title("Forecast") 65 | plt.xlabel("Time") 66 | plt.ylabel("Value") 67 | plt.show() 68 | 69 | def walk_forward_validation(self, test_size=0.2): 70 | tscv = TimeSeriesSplit(n_splits=5) 71 | scores = [] 72 | for train_index, test_index in tscv.split(self.data): 73 | X_train, X_test = self.data[train_index], self.data[test_index] 74 | self.fit() 75 | score = self.evaluate(X_test) 76 | scores.append(score) 77 | return scores 78 | 79 | def hyperparameter_tuning(self): 80 | def objective(params): 81 | self.seasonality_mode = params["seasonality_mode"] 82 | self.growth = params["growth"] 83 | self.fit() 84 | score = self.walk_forward_validation() 85 | return score 86 | 87 | space = { 88 | "seasonality_mode": hp.choice("seasonality_mode", ['additive', 'multiplicative']), 89 | "growth": hp.choice("growth", ['linear', 'logistic']) 90 | } 91 | trials = Trials() 92 | best = fmin(objective, space, algo=tpe.suggest, max_evals=50, trials=trials) 93 | return best 94 | -------------------------------------------------------------------------------- /src/ai-engine/models/arima.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | from statsmodels.tsa.arima.model import ARIMA 4 | from statsmodels.tsa.statespace.sarimax import SARIMAX 5 | from sklearn.metrics import mean_squared_error 6 | from sklearn.model_selection import TimeSeriesSplit 7 | from hyperopt import hp, fmin, tpe, Trials 8 | import matplotlib.pyplot as plt 9 | 10 | class ARIMAModel: 11 | def __init__(self, data, order=None, seasonal_order=None): 12 | self.data = data 13 | self.order = order 14 | self.seasonal_order = seasonal_order 15 | self.model = None 16 | self.model_fit = None 17 | self.params = None 18 | 19 | def fit(self): 20 | if self.order is None: 21 | self.order = self._auto_arima() 22 | if self.seasonal_order is None: 23 | self.seasonal_order = self._auto_seasonal_arima() 24 | self.model = SARIMAX(self.data, order=self.order, seasonal_order=self.seasonal_order) 25 | self.model_fit = self.model.fit() 26 | 27 | def _auto_arima(self): 28 | def objective(params): 29 | order = params["order"] 30 | model = ARIMA(self.data, order=order) 31 | model_fit = model.fit() 32 | return model_fit.aic 33 | 34 | space = { 35 | "order": hp.quniform("order", 0, 5, 1) 36 | } 37 | trials = Trials() 38 | best = fmin(objective, space, algo=tpe.suggest, max_evals=50, trials=trials) 39 | return tuple(best["order"]) 40 | 41 | def _auto_seasonal_arima(self): 42 | def objective(params): 43 | seasonal_order = params["seasonal_order"] 44 | model = SARIMAX(self.data, order=self.order, seasonal_order=seasonal_order) 45 | model_fit = model.fit() 46 | return model_fit.aic 47 | 48 | space = { 49 | "seasonal_order": hp.quniform("seasonal_order", 0, 2, 1) 50 | } 51 | trials = Trials() 52 | best = fmin(objective, space, algo=tpe.suggest, max_evals=50, trials=trials) 53 | return tuple(best["seasonal_order"]) 54 | 55 | def forecast(self, steps): 56 | return self.model_fit.forecast(steps=steps) 57 | 58 | def evaluate(self, test_data): 59 | predictions = self.model_fit.predict(start=len(self.data), end=len(self.data)+len(test_data)-1) 60 | mse = mean_squared_error(test_data, predictions) 61 | rmse = np.sqrt(mse) 62 | return rmse 63 | 64 | def plot_residuals(self): 65 | residuals = self.model_fit.resid 66 | residuals.plot() 67 | plt.title("Residuals") 68 | plt.xlabel("Time") 69 | plt.ylabel("Residual") 70 | plt.show() 71 | 72 | def plot_forecast(self, steps): 73 | forecast = self.forecast(steps) 74 | plt.plot(self.data) 75 | plt.plot(forecast) 76 | plt.title("Forecast") 77 | plt.xlabel("Time") 78 | plt.ylabel("Value") 79 | plt.show() 80 | 81 | def walk_forward_validation(self, test_size=0.2): 82 | tscv = TimeSeriesSplit(n_splits=5) 83 | scores = [] 84 | for train_index, test_index in tscv.split(self.data): 85 | X_train, X_test = self.data[train_index], self.data[test_index] 86 | self.fit() 87 | score = self.evaluate(X_test) 88 | scores.append(score) 89 | return scores 90 | 91 | def hyperparameter_tuning(self): 92 | def objective(params): 93 | self.order = params["order"] 94 | self.seasonal_order = params["seasonal_order"] 95 | self.fit() 96 | score = self.walk_forward_validation() 97 | return score 98 | 99 | space = { 100 | "order": hp.quniform("order", 0, 5, 1), 101 | "seasonal_order": hp.quniform("seasonal_order", 0, 2, 1) 102 | } 103 | trials = Trials() 104 | best = fmin(objective, space, algo=tpe.suggest, max_evals=50, trials=trials) 105 | return best 106 | -------------------------------------------------------------------------------- /index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | PiStabilizer 4 | 5 | 6 | 7 | 87 | 88 | 89 |
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PiStabilizer

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Decentralized, AI-powered stablecoin platform for Pi Coin

94 | Get Started 95 |
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Features

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100 | 101 |

AI-Powered Stability

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Utilizes machine learning algorithms to maintain a stable exchange rate of $314.159 for Pi Coin.

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105 | 106 |

Seamless Integration

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Integrates with the Pi Network to automatically create and distribute Pi Coin as a stable store of value.

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Advanced Econometric Models

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Employs advanced econometric models to mitigate price volatility and ensure a reliable medium of exchange.

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© 2024 PiStabilizer. All rights reserved.

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118 | 119 | 120 | -------------------------------------------------------------------------------- /payment_systems/payment_gateway.py: -------------------------------------------------------------------------------- 1 | import hashlib 2 | import ecdsa 3 | from ecdsa.util import sigdecode_der 4 | import binascii 5 | from cryptography.hazmat.primitives import serialization 6 | from cryptography.hazmat.primitives.asymmetric import padding 7 | from cryptography.hazmat.primitives import hashes 8 | from cryptography.hazmat.backends import default_backend 9 | 10 | class PaymentGateway: 11 | def __init__(self, pi_coin): 12 | self.pi_coin = pi_coin 13 | self.payment_requests = {} 14 | 15 | def create_payment_request(self, sender, recipient, amount): 16 | payment_request = { 17 | "sender": sender, 18 | "recipient": recipient, 19 | "amount": amount, 20 | "timestamp": int(time.time()) 21 | } 22 | self.payment_requests[payment_request["timestamp"]] = payment_request 23 | return payment_request 24 | 25 | def process_payment(self, payment_request, private_key): 26 | transaction = self.pi_coin.create_transaction(payment_request["sender"], payment_request["recipient"], payment_request["amount"]) 27 | signature = self.sign_transaction(transaction, private_key) 28 | self.pi_coin.add_transaction(transaction) 29 | self.pi_coin.mine_block("PiCoin Miner") 30 | return signature 31 | 32 | def sign_transaction(self, transaction, private_key): 33 | transaction_hash = hashlib.sha256(str(transaction).encode()).hexdigest() 34 | private_key_pem = private_key.to_pem() 35 | private_key_obj = serialization.load_pem_private_key(private_key_pem, password=None, backend=default_backend()) 36 | signature = private_key_obj.sign( 37 | transaction_hash.encode(), 38 | padding.PSS( 39 | mgf=padding.MGF1(algorithm=hashes.SHA256()), 40 | salt_length=padding.PSS.MAX_LENGTH 41 | ), 42 | hashes.SHA256() 43 | ) 44 | return signature 45 | 46 | def verify_payment(self, payment_request, signature, public_key): 47 | transaction = self.pi_coin.create_transaction(payment_request["sender"], payment_request["recipient"], payment_request["amount"]) 48 | transaction_hash = hashlib.sha256(str(transaction).encode()).hexdigest() 49 | public_key_pem = public_key.to_pem() 50 | public_key_obj = serialization.load_pem_public_key(public_key_pem, backend=default_backend()) 51 | try: 52 | public_key_obj.verify( 53 | signature, 54 | transaction_hash.encode(), 55 | padding.PSS( 56 | mgf=padding.MGF1(algorithm=hashes.SHA256()), 57 | salt_length=padding.PSS.MAX_LENGTH 58 | ), 59 | hashes.SHA256() 60 | ) 61 | return True 62 | except: 63 | return False 64 | 65 | class PaymentProcessor: 66 | def __init__(self, payment_gateway): 67 | self.payment_gateway = payment_gateway 68 | 69 | def process_payment_request(self, payment_request, private_key): 70 | payment_request_signature = self.payment_gateway.process_payment(payment_request, private_key) 71 | return payment_request_signature 72 | 73 | def verify_payment_request(self, payment_request, signature, public_key): 74 | return self.payment_gateway.verify_payment(payment_request, signature, public_key) 75 | 76 | class Merchant: 77 | def __init__(self, payment_processor, public_key): 78 | self.payment_processor = payment_processor 79 | self.public_key = public_key 80 | 81 | def receive_payment(self, payment_request, signature): 82 | if self.payment_processor.verify_payment_request(payment_request, signature, self.public_key): 83 | print("Payment received successfully!") 84 | else: 85 | print("Payment verification failed!") 86 | 87 | class Customer: 88 | def __init__(self, payment_processor, private_key): 89 | self.payment_processor = payment_processor 90 | self.private_key = private_key 91 | 92 | def make_payment(self, payment_request): 93 | payment_request_signature = self.payment_processor.process_payment_request(payment_request, self.private_key) 94 | return payment_request_signature 95 | -------------------------------------------------------------------------------- /wallet/wallet_app.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | from cryptography.hazmat.primitives import serialization 4 | from cryptography.hazmat.primitives.asymmetric import rsa, ec 5 | from cryptography.hazmat.primitives import hashes 6 | from cryptography.hazmat.backends import default_backend 7 | from wallet.wallet_core import WalletCore 8 | from wallet.secure_multiparty_computation import SecureMultipartyComputation 9 | from wallet.homomorphic_encryption import HomomorphicEncryption 10 | from wallet.zero_knowledge_proofs import ZeroKnowledgeProofs 11 | 12 | class WalletApp: 13 | def __init__(self, wallet_core): 14 | self.wallet_core = wallet_core 15 | self.secure_multiparty_computation = SecureMultipartyComputation() 16 | self.homomorphic_encryption = HomomorphicEncryption() 17 | self.zero_knowledge_proofs = ZeroKnowledgeProofs() 18 | 19 | def create_wallet(self, password): 20 | private_key = rsa.generate_private_key( 21 | public_exponent=65537, 22 | key_size=2048, 23 | backend=default_backend() 24 | ) 25 | private_key_pem = private_key.private_bytes( 26 | encoding=serialization.Encoding.PEM, 27 | format=serialization.PrivateFormat.PKCS8, 28 | encryption_algorithm=serialization.BestAvailableEncryption(password.encode()) 29 | ) 30 | public_key_pem = private_key.public_key().public_bytes( 31 | encoding=serialization.Encoding.OpenSSH, 32 | format=serialization.PublicFormat.OpenSSH 33 | ) 34 | wallet_data = { 35 | "private_key": private_key_pem.decode(), 36 | "public_key": public_key_pem.decode() 37 | } 38 | with open("wallet.json", "w") as f: 39 | json.dump(wallet_data, f) 40 | return wallet_data 41 | 42 | def load_wallet(self, password): 43 | with open("wallet.json", "r") as f: 44 | wallet_data = json.load(f) 45 | private_key_pem = wallet_data["private_key"].encode() 46 | private_key = serialization.load_pem_private_key(private_key_pem, password.encode(), backend=default_backend()) 47 | public_key_pem = wallet_data["public_key"].encode() 48 | public_key = serialization.load_ssh_public_key(public_key_pem, backend=default_backend()) 49 | return private_key, public_key 50 | 51 | def get_balance(self, public_key): 52 | # Use homomorphic encryption to encrypt the balance query 53 | encrypted_balance_query = self.homomorphic_encryption.encrypt(public_key, "get_balance") 54 | # Use secure multi-party computation to compute the balance 55 | balance = self.secure_multiparty_computation.compute_balance(encrypted_balance_query) 56 | # Use zero-knowledge proofs to verify the balance 57 | proof = self.zero_knowledge_proofs.generate_proof(balance) 58 | return proof 59 | 60 | def send_transaction(self, private_key, recipient, amount): 61 | # Use secure multi-party computation to compute the transaction 62 | transaction = self.secure_multiparty_computation.compute_transaction(private_key, recipient, amount) 63 | # Use homomorphic encryption to encrypt the transaction 64 | encrypted_transaction = self.homomorphic_encryption.encrypt(transaction) 65 | # Use zero-knowledge proofs to verify the transaction 66 | proof = self.zero_knowledge_proofs.generate_proof(encrypted_transaction) 67 | return proof 68 | 69 | def receive_transaction(self, public_key, transaction): 70 | # Use homomorphic encryption to decrypt the transaction 71 | decrypted_transaction = self.homomorphic_encryption.decrypt(public_key, transaction) 72 | # Use secure multi-party computation to verify the transaction 73 | verified_transaction = self.secure_multiparty_computation.verify_transaction(decrypted_transaction) 74 | # Use zero-knowledge proofs to verify the transaction 75 | proof = self.zero_knowledge_proofs.generate_proof(verified_transaction) 76 | return proof 77 | 78 | def add_allah_features(self): 79 | # Add Allah features, such as prayer reminders and Quranic verses 80 | print("Allah features added!") 81 | 82 | def main(): 83 | wallet_core = WalletCore() 84 | wallet_app = WalletApp(wallet_core) 85 | while True: 86 | print("1. Create Wallet") 87 | print("2. Load Wallet") 88 | print("3. Get Balance") 89 | print("4. Send Transaction") 90 | print("5. Receive Transaction") 91 | print("6. Add Allah Features") 92 | print("7. Exit") 93 | choice = input("Enter your choice: ") 94 | if choice == "1": 95 | password = input("Enter password: ") 96 | wallet_app.create_wallet(password) 97 | elif choice == "2": 98 | password = input("Enter password: ") 99 | private_key, public_key = wallet_app.load_wallet(password) 100 | print("Private Key:", private_key) 101 | print("Public Key:", public_key) 102 | elif choice == "3": 103 | public_key = input("Enter public key: ") 104 | balance = wallet_app.get_balance(public_key) 105 | print("Balance:", balance) 106 | elif choice == "4": 107 | private_key = input("Enter private key: ") 108 | recipient = input("Enter recipient: ") 109 | amount = int(input("Enter amount: ")) 110 | transaction = wallet_app.send_transaction(private_key, recipient, amount) 111 | print("Transaction:", transaction) 112 | elif choice == "5": 113 | public_key = input("Enter public key: ") 114 | transaction = input("Enter transaction: ") 115 | verified_transaction = wallet_app.receive_transaction(public_key, transaction) 116 | print("Verified Transaction:", verified_transaction) 117 | elif choice == "6": 118 | wallet_app.add_allah_features() 119 | elif choice == "7": 120 | break 121 | else: 122 | print("Invalid choice. Please try again.") 123 | 124 | if __name__ == "__main__": 125 | main() 126 | -------------------------------------------------------------------------------- /src/ai-engine/models/lstm.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | from sklearn.preprocessing import MinMaxScaler 4 | from keras.models import Sequential 5 | from keras.layers import LSTM, Dense 6 | from sklearn.metrics import mean_squared_error 7 | from sklearn.model_selection import TimeSeriesSplit 8 | from hyperopt import hp, fmin, tpe, Trials 9 | import matplotlib.pyplot as plt 10 | 11 | class LSTMModel: 12 | def __init__(self, data, n_features, n_steps, n_epochs, batch_size, optimizer): 13 | self.data = data 14 | self.n_features = n_features 15 | self.n_steps = n_steps 16 | self.n_epochs = n_epochs 17 | self.batch_size = batch_size 18 | self.optimizer = optimizer 19 | self.model = None 20 | self.model_fit = None 21 | self.params = None 22 | 23 | def fit(self): 24 | self.model = Sequential() 25 | self.model.add(LSTM(50, input_shape=(self.n_steps, self.n_features))) 26 | self.model.add(Dense(1)) 27 | self.model.compile(loss='mean_squared_error', optimizer=self.optimizer) 28 | self.model_fit = self.model.fit(self.data, epochs=self.n_epochs, batch_size=self.batch_size, verbose=0) 29 | 30 | def _auto_n_features(self): 31 | def objective(params): 32 | n_features = params["n_features"] 33 | model = LSTMModel(self.data[:, :n_features], n_features, self.n_steps, self.n_epochs, self.batch_size, self.optimizer) 34 | model.fit() 35 | return model.model_fit.history['loss'][-1] 36 | 37 | space = { 38 | "n_features": hp.quniform("n_features", 1, self.data.shape[1], 1) 39 | } 40 | trials = Trials() 41 | best = fmin(objective, space, algo=tpe.suggest, max_evals=10, trials=trials) 42 | return int(best["n_features"]) 43 | 44 | def _auto_n_steps(self): 45 | def objective(params): 46 | n_steps = params["n_steps"] 47 | model = LSTMModel(self.data[:, :, :self.n_features], self.n_features, n_steps, self.n_epochs, self.batch_size, self.optimizer) 48 | model.fit() 49 | return model.model_fit.history['loss'][-1] 50 | 51 | space = { 52 | "n_steps": hp.quniform("n_steps", 1, self.data.shape[1], 1) 53 | } 54 | trials = Trials() 55 | best = fmin(objective, space, algo=tpe.suggest, max_evals=10, trials=trials) 56 | return int(best["n_steps"]) 57 | 58 | def _auto_n_epochs(self): 59 | def objective(params): 60 | n_epochs = params["n_epochs"] 61 | model = LSTMModel(self.data[:, :, :self.n_features], self.n_features, self.n_steps, n_epochs, self.batch_size, self.optimizer) 62 | model.fit() 63 | return model.model_fit.history['loss'][-1] 64 | 65 | space = { 66 | "n_epochs": hp.quniform("n_epochs", 1, 100, 1) 67 | } 68 | trials = Trials() 69 | best = fmin(objective, space, algo=tpe.suggest, max_evals=10, trials=trials) 70 | return int(best["n_epochs"]) 71 | 72 | def _auto_batch_size(self): 73 | def objective(params): 74 | batch_size = params["batch_size"] 75 | model = LSTMModel(self.data[:, :, :self.n_features], self.n_features, self.n_steps, self.n_epochs, batch_size, self.optimizer) 76 | model.fit() 77 | return model.model_fit.history['loss'][-1] 78 | 79 | space = { 80 | "batch_size": hp.quniform("batch_size", 1, 128, 1) 81 | } 82 | trials = Trials() 83 | best = fmin(objective, space, algo=tpe.suggest, max_evals=10, trials=trials) 84 | return int(best["batch_size"]) 85 | 86 | def _auto_optimizer(self): 87 | def objective(params): 88 | optimizer = params["optimizer"] 89 | model = LSTMModel(self.data[:, :, :self.n_features], self.n_features, self.n_steps, self.n_epochs, self.batch_size, optimizer) 90 | model.fit() 91 | return model.model_fit.history['loss'][-1] 92 | 93 | space = { 94 | "optimizer": hp.choice("optimizer", ['adam', 'rmsprop', 'sgd']) 95 | } 96 | trials = Trials() 97 | best = fmin(objective, space, algo=tpe.suggest, max_evals=10, trials=trials) 98 | return best["optimizer"] 99 | 100 | def forecast(self, steps): 101 | forecast = self.model.predict(steps) 102 | return forecast 103 | 104 | def evaluate(self, test_data): 105 | predictions = self.model.predict(test_data) 106 | mse = mean_squared_error(test_data, predictions) 107 | rmse = np.sqrt(mse) 108 | return rmse 109 | 110 | def plot_forecast(self, steps): 111 | forecast = self.forecast(steps) 112 | plt.plot(forecast) 113 | plt.title("Forecast") 114 | plt.xlabel("Time") 115 | plt.ylabel("Value") 116 | plt.show() 117 | 118 | def walk_forward_validation(self, test_size=0.2): 119 | tscv = TimeSeriesSplit(n_splits=5) 120 | scores = [] 121 | for train_index, test_index in tscv.split(self.data): 122 | X_train, X_test = self.data[train_index], self.data[test_index] 123 | self.fit() 124 | score = self.evaluate(X_test) 125 | scores.append(score) 126 | return scores 127 | 128 | def hyperparameter_tuning(self): 129 | def objective(params): 130 | self.n_features = params["n_features"] 131 | self.n_steps = params["n_steps"] 132 | self.n_epochs = params["n_epochs"] 133 | self.batch_size = params["batch_size"] 134 | self.optimizer = params["optimizer"] 135 | self.fit() 136 | score = self.walk_forward_validation() 137 | return score 138 | 139 | space = { 140 | "n_features": hp.quniform("n_features", 1, self.data.shape[1], 1), 141 | "n_steps": hp.quniform("n_steps", 1, self.data.shape[1], 1), 142 | "n_epochs": hp.quniform("n_epochs", 1, 100, 1), 143 | "batch_size": hp.quniform("batch_size", 1, 128, 1), 144 | "optimizer": hp.choice("optimizer", ['adam', 'rmsprop', 'sgd']) 145 | } 146 | trials = Trials() 147 | best = fmin(objective, space, algo=tpe.suggest, max_evals=50, trials=trials) 148 | return best 149 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND 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