import hashlib
import random
import pyttsx3
from datetime import datetime
from queue import Queue
import threading
import string
from time import sleep
import pandas as pd

# CONFIG
CSV_FILE = "bitcoin-puzzle-unsolved-20260220.csv"
NOTEPAD_FILE = "bitcoin_experiment_notepad.txt"
NUM_THREADS = 4
MAX_CANDIDATE_LENGTH = 4
VOICE_ENGINE = pyttsx3.init()
KANGAROO_MAX_STEPS = 1000

# VOICE NOTIFICATION
def notify_creator(message):
    print(f"LMLM Notification: {message}")
    VOICE_ENGINE.say(f"Hello my creator Seriki Yakub, update: {message}")
    VOICE_ENGINE.runAndWait()

# LOAD CSV
df = pd.read_csv(CSV_FILE)

# LOGGING
def log_to_notepad(entry):
    with open(NOTEPAD_FILE, "a") as f:
        f.write(entry + "\n")

# HASH FUNCTION
def candidate_hash(candidate):
    return hashlib.sha256(candidate.encode()).hexdigest()

# KANGAROO STEP FUNCTION
def kangaroo_jump(candidate, step_size=3):
    chars = string.ascii_letters + string.digits
    candidate_list = list(candidate)
    for _ in range(step_size):
        idx = random.randint(0, len(candidate_list)-1)
        candidate_list[idx] = random.choice(chars)
    return ''.join(candidate_list)

# PUZZLE WORKER WITH KANGAROO
def puzzle_worker(task_queue):
    while not task_queue.empty():
        task_id, target_hash = task_queue.get()
        log_to_notepad(f"[{datetime.now().isoformat()}] Starting puzzle {task_id}")
        
        # Initial candidate (could come from previous genetic population or heuristic)
        candidate = ''.join(random.choices(string.ascii_letters + string.digits, k=MAX_CANDIDATE_LENGTH))
        for step in range(KANGAROO_MAX_STEPS):
            chash = candidate_hash(candidate)
            success = chash == target_hash
            
            log_to_notepad(f"[{datetime.now().isoformat()}] Puzzle {task_id} Step {step} Candidate: {candidate} Success: {success}")
            
            if success:
                log_to_notepad(f"[{datetime.now().isoformat()}] Puzzle {task_id} SOLVED: {candidate}")
                notify_creator(f"Puzzle {task_id} solved! Key recorded in notepad.")
                break
            
            # Kangaroo jump to next candidate
            candidate = kangaroo_jump(candidate, step_size=random.randint(1,5))
            sleep(0.05)  # small pause to prevent CPU overload

        task_queue.task_done()

# MULTI-THREAD QUEUE
task_queue = Queue()
for _, row in df.iterrows():
    task_queue.put((row['puzzle_id'], row['hash']))

threads = []
for _ in range(NUM_THREADS):
    t = threading.Thread(target=puzzle_worker, args=(task_queue,))
    t.start()
    threads.append(t)

for t in threads:
    t.join()

notify_creator("All puzzle attempts completed! Check your notepad for full logs.")