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Nam/model.running_code.py

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+
+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')