main.py

import logging
import os
import pandas as pd
import yaml
import json
import time
import paho.mqtt.client as mqtt
from data_processing.analytics.anomaly_detection import detect_anomaly
from data_processing.analytics.pattern_recognition import PatternRecognizer
from data_processing.data_collector import collect_data
from data_processing.data_processor import process_data
from communication.mqtt_client import MQTTClient
from algorithms.supply_adjustment import SupplyAdjustment
from algorithms.asset_management import AssetManagement
from algorithms.prediction_model import DemandPredictionModel
from features.dynamic_pegging import DynamicPegging
from features.decentralized_reserve import DecentralizedReserve
from features.cross_chain_interoperability import CrossChainInteroperability
from features.security_protocols import AdvancedSecurity
from features.wallet_solutions import UserFriendlyWallet
from features.bridge_system import BridgeSystem
from features.dual_value_system import DualValueSystem

def load_config(env='development'):
    """Load configuration from the specified environment."""
    config_file = f'config/{env}.yaml'
    try:
        with open(config_file, 'r') as file:
            config = yaml.safe_load(file)
        logging.info(f'Loaded configuration for {env} environment.')
        return config
    except FileNotFoundError:
        logging.error(f'Configuration file {config_file} not found.')
        raise
    except yaml.YAMLError as e:
        logging.error(f'Error parsing YAML configuration: {e}')
        raise

def initialize_algorithms(config):
    """Initialize the algorithms with the loaded configuration."""
    supply_adjuster = SupplyAdjustment()
    asset_manager = AssetManagement()
    demand_model = DemandPredictionModel()

    # Load historical data for training
    try:
        historical_data = pd.read_csv(config['data']['historical_data_path'])
        supply_adjuster.train_model(historical_data)
        asset_manager.train_model(historical_data)
        demand_model.train_model(historical_data)
        logging.info('Successfully trained models with historical data.')
    except Exception as e:
        logging.error(f'Error loading historical data: {e}')
        raise

    return supply_adjuster, asset_manager, demand_model

def implement_dynamic_pegging(pegging_system, market_conditions):
    """Implement dynamic pegging mechanism."""
    try:
        pegging_system.adjust_supply(market_conditions)
        logging.info('Dynamic pegging mechanism executed successfully.')
    except Exception as e:
        logging.error(f'Error in dynamic pegging: {e}')
        raise

def manage_decentralized_reserve(reserve_system):
    """Manage decentralized reserve system."""
    try:
        reserve_system.update_reserves()
        logging.info('Decentralized reserve system updated successfully.')
    except Exception as e:
        logging.error(f'Error managing decentralized reserve: {e}')
        raise

def main():
    # Configure logging
    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

    # Load configuration
    environment = os.getenv('APP_ENV', 'development')  # Default to development
    config = load_config(environment)

    # Initialize algorithms
    supply_adjuster, asset_manager, demand_model = initialize_algorithms(config)

    # Initialize new features
    dynamic_pegging = DynamicPegging()
    decentralized_reserve = DecentralizedReserve()
    cross_chain = CrossChainInteroperability()
    security_protocols = AdvancedSecurity()
    wallet_solutions = UserFriendlyWallet()
    bridge_system = BridgeSystem()
    dual_value_system = DualValueSystem()

    # Initialize MQTT client for communication
    mqtt_client = MQTTClient(config['mqtt'])
    mqtt_client.connect()

    # Data collection and processing loop
    recognizer = PatternRecognizer(window_size=5)

    try:
        while True:
            # Collect data from sensors
            sensor_data = collect_data()  # Implement this function to collect data
            logging.info(f"Collected sensor data: {sensor_data}")

            # Process the collected data
            if detect_anomaly(sensor_data):
                logging.warning("Anomaly detected in sensor data.")
                continue  # Skip processing if anomaly detected

            recognizer.add_data(sensor_data['temperature'], sensor_data['humidity'])
            patterns = recognizer.recognize_patterns()

            # Example usage of the models
            market_price = 105
            current_supply = 1000
            other_factors = 1

            # Predict demand
            predicted_demand = demand_model.predict(market_price, current_supply, other_factors)

            # Current allocations
            current_allocations = {'Asset_A': 1000, 'Asset_B': 1100}
            
            # Adjust allocations based on market conditions
            market_conditions = 1  # Example market condition
            new_allocations = asset_manager.adjust_allocations(current_allocations, market_conditions)

            # Implement dynamic pegging
            implement_dynamic_pegging(dynamic_pegging, market_conditions)

            # Manage decentralized reserve
            manage_decentralized_reserve(decentralized_reserve)

            # Log final allocations
            logging.info(f'Final Allocations: {new_allocations}')

            # Publish processed data to MQTT broker
            mqtt_client.publish("sensor/data", json.dumps(sensor_data))
            mqtt_client.publish("market/allocations", json.dumps(new_allocations))

            # Sleep for a defined interval before the next iteration
            time.sleep(config['data']['collection_interval'])

    except KeyboardInterrupt:
        logging.info("Shutting down the application.")
    except Exception as e:
        logging.error(f'An unexpected error occurred: {e}')
    finally:
        mqtt_client.disconnect()

if __name__ == "__main__":
    main()