shibber.py

import hashlib
import json
import requests
import time
import numpy as np
import threading
import logging
import yaml
from web3 import Web3
from bayes_opt import BayesianOptimization
from deap import base, creator, tools
import smtplib
from email.mime.text import MIMEText

# Configure logging
logging.basicConfig(filename='shib_miner.log', level=logging.INFO,
                    format='%(asctime)s - %(levelname)s - %(message)s')

# Email notification function
def send_email(subject, body):
    """Send email notifications."""
    sender_email = "[email protected]"
    receiver_email = "[email protected]"
    password = "your_email_password"  # Use environment variables for security

    msg = MIMEText(body)
    msg['Subject'] = subject
    msg['From'] = sender_email
    msg['To'] = receiver_email

    with smtplib.SMTP('smtp.gmail.com', 587) as server:
        server.starttls()
        server.login(sender_email, password)
        server.send_message(msg)
    logging.info("Notification email sent.")

# SHIB Miner Class
class ShibMiner:
    def __init__(self, wallet_address, mining_pool_url, eth_provider_url, config):
        self.wallet_address = wallet_address
        self.mining_pool_url = mining_pool_url
        self.web3 = Web3(Web3.HTTPProvider(eth_provider_url))
        self.difficulty = None
        self.nonce = 0
        self.best_solution = None
        self.hash_rates = []
        self.max_threads = config['max_threads']
        self.thread_lock = threading.Lock()

    def get_pool_data(self):
        """Fetch pool data including difficulty."""
        try:
            response = requests.get(f'{self.mining_pool_url}/getDifficulty')
            if response.status_code == 200:
                self.difficulty = response.json()['difficulty']
                logging.info(f"Retrieved difficulty: {self.difficulty}")
            else:
                logging.error("Could not retrieve pool data")
        except Exception as e:
            logging.error(f"Error fetching pool data: {e}")

    def sha256(self, data):
        return hashlib.sha256(data.encode('utf-8')).hexdigest()

    def mine(self):
        """Start the mining process."""
        self.get_pool_data()
        block_data = f"{self.wallet_address}"

        while True:
            with self.thread_lock:
                block_hash = self.sha256(f"{block_data}{self.nonce}")
                if int(block_hash, 16) < self.difficulty:
                    logging.info(f'Mined a block: {block_hash} with nonce: {self.nonce}')
                    self.submit_work(block_hash, self.nonce)
                    break
                self.nonce += 1

    def submit_work(self, block_hash, nonce):
        """Submit mined work to the pool."""
        payload = {
            'wallet': self.wallet_address,
            'blockHash': block_hash,
            'nonce': nonce,
        }
        try:
            response = requests.post(f'{self.mining_pool_url}/submitWork', json=payload)
            if response.status_code == 200:
                logging.info("Work submitted successfully.")
                send_email("Mining Success", f"Successfully mined block: {block_hash}")
            else:
                logging.error(f"Error submitting work: {response.json()}")
                # Retry logic
                self.retry_submission(payload)
        except Exception as e:
            logging.error(f"Error submitting work: {e}")

    def retry_submission(self, payload, attempts=3):
        """Retry submission of mined work."""
        for attempt in range(attempts):
            try:
                response = requests.post(f'{self.mining_pool_url}/submitWork', json=payload)
                if response.status_code == 200:
                    logging.info("Work submitted successfully on retry.")
                    send_email("Mining Success", f"Successfully mined block on retry: {payload['blockHash']}")
                    return
            except Exception as e:
                logging.error(f"Retry attempt {attempt + 1} failed: {e}")
            time.sleep(5)  # Wait before retrying
        logging.error("All retry attempts failed.")

    def optimize_parameters(self, iterations=10):
        """Optimize mining parameters using Bayesian Optimization."""
        def black_box_function(param1, param2):
            # Simulated hash rate based on parameters
            return np.sin(param1) * np.cos(param2) + 0.5  # Simulated function

        pbounds = {'param1': (0, 10), 'param2': (0, 10)}
        optimizer = BayesianOptimization(
            f=black_box_function,
            pbounds=pbounds,
            random_state=1,
        )
        optimizer.maximize(init_points=5, n_iter=iterations)

        # Get the best parameters
        self.best_solution = optimizer.max
        logging.info("Best parameters from Bayesian Optimization: %s", self.best_solution)

    def genetic_algorithm(self, iterations=10):
        """Optimize using a Genetic Algorithm."""
        creator.create("FitnessMax", base.Fitness, weights=(1.0,))
        creator.create("Individual", list, fitness=creator.FitnessMax)

        toolbox = base.Toolbox()
        toolbox.register("attr_float", np.random.uniform, 0, 10)
        toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=2)
        toolbox.register("population", tools.initRepeat, list, toolbox.individual)

        def eval_individual(individual):
            return (np.sin(individual[0]) * np.cos(individual[1]) + 0.5,)

        toolbox.register("evaluate", eval_individual)
        toolbox.register("mate", tools.cxBlend, alpha=0.5)
        toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=1, indpb=0.2)
        toolbox.register("select", tools.selTournament, tournsize=3)

        population = toolbox.population(n=50)
        for gen in range(iterations):
            offspring = toolbox.select(population, len(population))
            offspring = list(map(toolbox.clone, offspring))

            for child1, child2 in zip(offspring[::2], offspring[1::2]):
                if np.random.rand() < 0.5:
                    toolbox.mate(child1, child2)
                    del child1.fitness.values
                    del child2.fitness.values

            for mutant in offspring:
                if np.random.rand() < 0.2:
                    toolbox.mutate(mutant)
                    del mutant.fitness.values

            invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
            fitnesses = map(toolbox.evaluate, invalid_ind)
            for ind, fit in zip(invalid_ind, fitnesses):
                ind.fitness.values = fit

            population[:] = offspring

        fits = [ind.fitness.values[0] for ind in population]
        self.best_solution = population[np.argmax(fits)]
        logging.info("Best parameters from Genetic Algorithm: %s", self.best_solution)

    def trinary_qubit_optimization(self):
        """Simulated Trinary Qubit Optimization."""
        # A placeholder for trinary qubit logic
        qubits = [np.random.choice([0, 1, 2]) for _ in range(10)]
        logging.info("Simulated Trinary Qubits: %s", qubits)

    def start_mining_threads(self):
        """Start multiple mining threads."""
        threads = []
        for _ in range(self.max_threads):
            thread = threading.Thread(target=self.mine)
            thread.start()
            threads.append(thread)
        
        for thread in threads:
            thread.join()

if __name__ == "__main__":


    wallet_address = config['wallet_address']
    mining_pool_url = config['mining_pool_url']
    eth_provider_url = config['eth_provider_url']

    miner = ShibMiner(wallet_address, mining_pool_url, eth_provider_url, config)

    # Perform optimizations
    miner.optimize_parameters(iterations=config['bayesian_iterations'])
    miner.genetic_algorithm(iterations=config['ga_iterations'])
    miner.trinary_qubit_optimization()

    # Start mining with multiple threads
    miner.start_mining_threads()