PasswordAnalyzer/batch_analyzer.py at main · ShinyACash/PasswordAnalyzer · GitHub

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import random
import string
import pandas as pd
import joblib
from main import extract_features, update_model_with_new_example, load_model, check_pwned, convert_time, _compute_override_score

def generate_random_passwords(n=30, min_len=10, max_len=20):
    passwords = []
    chars = string.ascii_letters + string.digits + "!@#$%^&*()_+-="
    for _ in range(n):
        length = random.randint(min_len, max_len)
        pw = ''.join(random.choice(chars) for _ in range(length))
        passwords.append(pw)
    return passwords

def analyze_batch(password_list, model, auto_label=True):
    for pw in password_list:
        feat = extract_features(pw)
        entropy = feat["entropy"]

        classical_guesses = 2 ** entropy
        quantum_guesses = 2 ** (entropy / 2)
        classical_time = classical_guesses / 1e9
        quantum_time = quantum_guesses / 1e9

        ai_score = model.predict(pd.DataFrame([feat]))[0]

        override_score = _compute_override_score(entropy, quantum_time)

        if quantum_time < 3600 * 24:  # less than 1 day
            final_score = 0.2 * ai_score + 0.8 * override_score
        else:
            final_score = 0.5 * ai_score + 0.5 * override_score

        if final_score < 0.33:
            strength_label = "Weak"
        elif final_score < 0.66:
            strength_label = "Medium"
        else:
            strength_label = "Strong"

        try:
            breaches = check_pwned(pw)
        except:
            breaches = -1

        print(f"Password: {pw} | AI Score: {ai_score:.3f} | Override: {override_score:.3f} | "
              f"Final: {final_score:.3f} -> {strength_label} | Entropy: {entropy:.2f} | "
              f"Classical Crack: {convert_time(classical_time)} | Quantum Crack: {convert_time(quantum_time)} | "
              f"Breaches: {breaches}")

        if auto_label:
            update_model_with_new_example(pw, final_score)

    print(f"\n[+] Processed {len(password_list)} passwords.")

if __name__ == "__main__":
    model = load_model()

    random_passwords = generate_random_passwords(50)

    combined_batch = random_passwords
    analyze_batch(combined_batch, model, auto_label=True)