73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
import joblib
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import os
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from app.PreP import preprocess_single_url
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import numpy as np
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# model directory
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BASE_DIR = os.path.dirname(os.path.abspath(__file__))
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MODEL_DIR = os.path.join(BASE_DIR, "models")
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'''
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# 임의의 url을 입력하고 함수 적용하여 전처리
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url_test = 'https://www.msn.com/ko-kr/news/other/%EC%84%9C%EB%B2%84%EC%97%90-%EC%82%AC%EC%A7%84-%EC%95%88-%EC%98%AC%EB%A6%AC%EA%B3%A0%EB%8F%84-%EC%A7%80%EB%B8%8C%EB%A6%AC%ED%92%8D%EC%9C%BC%EB%A1%9C-%EC%82%AC%EC%83%9D%ED%99%9C-%EC%B9%A8%ED%95%B4-%EB%A7%89%EB%8A%94-ai-%EA%B0%9C%EB%B0%9C/ar-AA1CyK6x?ocid=msedgntp&pc=U531&cvid=fdbbff03231b4babb8bf42b0036d8141&ei=9' # 테스트할 URL 입력
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url_pre = preprocess_single_url(url_test)
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'''
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# 훈련에 사용할 features
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features_cols = ['subdomain_count', 'letters_count', \
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'digits_count', 'special_chars_count', 'use_of_ip', 'path_depth', \
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'max_numeric_sequence', 'file_extension', 'special_char_count', \
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'url_length_cat', 'suspicious_keyword_count', 'has_suspicious_keyword']
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'''
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# 적용할 데이터
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input_data = url_pre[features_cols]
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'''
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## threshold 설정
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BEST_THRESHOLD = 0.6563
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## 모델 불러오기
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# 단일 모델 불러오기
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# model_load = joblib.load('xgboost_model_fold1.pkl')
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# 여러 모델 불러오기
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models_load = [joblib.load(os.path.join(MODEL_DIR, f'xgboost_model_fold{i+1}.pkl')) for i in range(4)]
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## threshold 설정
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BEST_THRESHOLD = 0.6563
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def predict_url(url: str) -> dict:
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try:
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preprocessed = preprocess_single_url(url)
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input_data = preprocessed[features_cols]
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# ✅ 전처리된 데이터 확인
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#print("Preprocessed input:", input_data)
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# 평균 확률 계산
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probs = [float(model.predict_proba(input_data)[0, 1]) for model in models_load]
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mean_pred = float(np.mean(probs))
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# 모델 예측 결과 확인
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print("Model prediction result:", mean_pred)
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# 진단 결과 판단
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is_malicious = bool(mean_pred > BEST_THRESHOLD)
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# 예: malicious_probability가 np.float32 타입일 경우
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return {
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"url": url,
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"malicious_probability": mean_pred,
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"is_malicious": is_malicious,
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"threshold": float(BEST_THRESHOLD) # ⬅️ numpy -> float
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}
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except Exception as e:
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return {"error": str(e)}
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