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a30886e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | import numpy as np
import joblib
import pandas as pd
from flask import Flask, request, jsonify
from urllib.parse import urlparse, parse_qs
# Load model artifacts
model = joblib.load("model.joblib")
encoders = joblib.load("encoders.joblib")
scaler = joblib.load("scaler.joblib")
if_model = joblib.load("best_if_model.joblib")
svm_model = joblib.load("best_svm_model.joblib")
feature_names = joblib.load("feature_names.joblib")
# Initialize the Flask app
app = Flask("Bot Detection API")
def parse_url_params(url):
try:
query = urlparse(url).query
return {k: v[0] if isinstance(v, list) else v for k, v in parse_qs(query).items()}
except Exception:
return {}
def prepare_features(row_dict):
base = {
'region': row_dict.get('region', 'unknown'),
'browser': row_dict.get('browser', 'unknown'),
'device': row_dict.get('device', 'unknown'),
'd': row_dict.get('d', '')
}
query_params = parse_url_params(base['d'])
combined = {**base, **query_params}
combined.pop('d', None)
# Fill any missing expected features
for col in feature_names:
if col not in combined and col not in ["iso_anomaly_prob", "svm_anomaly_prob"]:
combined[col] = "unknown"
df = pd.DataFrame([combined])
# Encode categoricals
for col in df.columns:
if col in encoders:
try:
df[col] = encoders[col].transform(df[col].astype(str))
except:
df[col] = encoders[col].transform(["unknown"])[0]
else:
df[col] = pd.to_numeric(df[col], errors='coerce').fillna(0)
# Scale and compute anomaly scores
df_scaled = scaler.transform(df)
iso_score = if_model.decision_function(df_scaled)
svm_score = svm_model.decision_function(df_scaled)
iso_anomaly_prob = 1 - float(np.clip((iso_score - iso_score.min()) / (iso_score.max() - iso_score.min() + 1e-9), 0, 1))
svm_anomaly_prob = 1 - float(np.clip((svm_score - svm_score.min()) / (svm_score.max() - svm_score.min() + 1e-9), 0, 1))
df['iso_anomaly_prob'] = iso_anomaly_prob
df['svm_anomaly_prob'] = svm_anomaly_prob
return df[feature_names]
@app.get('/')
def home():
return "✅ Bot Detection API is live."
@app.post('/v1/predict')
def predict():
row = request.get_json()
X = prepare_features(row)
pred_prob = model.predict_proba(X)[0][1]
pred_label = int(pred_prob >= 0.5)
return jsonify({
"Prediction": "Bot Attack" if pred_label else "Legitimate",
"Bot Probability": round(float(pred_prob), 4)
})
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