Update app.py

app.py

@@ -1,31 +1,23 @@
-from flask import Flask, request, jsonify
-from sklearn.tree import DecisionTreeClassifier
-import numpy as np
+from flask import Flask, request, jsonify, send_from_directory
+import os
 
 app = Flask(__name__)
 
-# Basit örnek veriler (Bu veriler yerel olarak tutulabilir)
-# Örnek veri formatı: [phishing_response, ransomware_response, ddos_response, malware_response]
-example_data = [
-    [0, 0, 1, 1],
-    [1, 1, 0, 0],
-    [1, 0, 1, 0],
-    [0, 1, 0, 1]
-]
+@app.route('/')
+def index():
+    return send_from_directory('.', 'index.html')
 
-# Örnek sonuçlar: 1 = güvenli, 0 = güvensiz
-example_labels = [1, 0, 1, 0]
+@app.route('/<path:path>')
+def static_files(path):
+    return send_from_directory('.', path)
 
-# Model eğitimi
-model = DecisionTreeClassifier()
-model.fit(example_data, example_labels)
-
-@app.route('/analyze', methods=['POST'])
-def analyze():
-    data = request.json
-    response = [data['phishing'], data['ransomware'], data['ddos'], data['malware']]
-    prediction = model.predict([response])
-    return jsonify({'prediction': int(prediction[0])})
+@app.route('/log', methods=['POST'])
+def log_action():
+    action_log = request.get_json()
+    log_file_path = os.path.join(app.root_path, 'user_actions.json')
+    with open(log_file_path, 'a') as f:
+        f.write(f"{action_log}\n")
+    return jsonify({"status": "success"}), 200
 
 if __name__ == '__main__':
-    app.run(debug=True)
+    app.run(debug=True)