import pandas as pd
import numpy as np
import getpass
import joblib
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier




def word(password):
    character=[]
    for i in password:
        character.append(i)
    return character






def new():
    
    
    
    x = np.array(data["password"])
    y = np.array(data["strength"])

    tdif = TfidfVectorizer(tokenizer=word)
    x = tdif.fit_transform(x)
    xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.05, random_state=42)
    x = np.array(data["password"])
    y = np.array(data["strength"])

    tdif = TfidfVectorizer(tokenizer=word)
    x = tdif.fit_transform(x)
    joblib.dump(tdif,"tdif.joblib")

    model = RandomForestClassifier()
    model.fit(xtrain, ytrain)
    joblib.dump(model, "passmodel.joblib")
    
def load():
    m=joblib.load("passmodel.joblib")
    td=joblib.load("tdif.joblib")
    return m,td

def test(i,m,td):
    

    

    data = td.transform([str(i)])
    output = m.predict(data)
    return output