import pandas as pd
import pickle
from tensorflow.keras.models import load_model
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
from Feature import extract_url_features
import tensorflow as tf


# 4. 스케일러 불러오기
with open("scaler.pkl", "rb") as f:
    scaler = pickle.load(f)

# 5. 모델 불러오기
model = load_model("best_model.h5")

@tf.function(reduce_retracing=True)
def predict_with_model(model, input_data):
    return model(input_data)


url = input("URL입력 : ")

features = extract_url_features(url)
input_df = pd.DataFrame([list(features.values())], columns= features.keys())


input_scaled = scaler.transform(input_df)


prediction = predict_with_model(model, input_scaled)


# 7. 결과 출력
best_threshold = 0.5
if prediction[0][0] > best_threshold:
    print('악')
else:
    print('정')