Create app.py

app.py

@@ -0,0 +1,75 @@
+from flask import Flask, request, jsonify
+import numpy as np
+import joblib  # to save and load the trained model
+import re
+from urllib.parse import urlparse
+from tld import get_tld
+from sklearn.ensemble import RandomForestClassifier
+
+app = Flask(__name__)
+
+# Load your trained model
+model = joblib.load('random_forest_model.pkl')  # save your trained model using joblib
+
+# Define your feature extraction functions here...
+
+def having_ip_address(url):
+    # Your implementation
+    pass
+
+def abnormal_url(url):
+    # Your implementation
+    pass
+
+def count_dot(url):
+    # Your implementation
+    pass
+
+# Define other functions similarly...
+
+def main(url):
+    status = []
+    status.append(having_ip_address(url))
+    status.append(abnormal_url(url))
+    status.append(count_dot(url))
+    status.append(count_www(url))
+    status.append(count_atrate(url))
+    status.append(no_of_dir(url))
+    status.append(no_of_embed(url))
+    status.append(shortening_service(url))
+    status.append(count_https(url))
+    status.append(count_http(url))
+    status.append(count_per(url))
+    status.append(count_ques(url))
+    status.append(count_hyphen(url))
+    status.append(count_equal(url))
+    status.append(url_length(url))
+    status.append(hostname_length(url))
+    status.append(suspicious_words(url))
+    status.append(digit_count(url))
+    status.append(letter_count(url))
+    status.append(fd_length(url))
+    tld = get_tld(url, fail_silently=True)
+    status.append(tld_length(tld))
+    return status
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    data = request.json
+    url = data.get('url')
+    features = np.array(main(url)).reshape(1, -1)
+    prediction = model.predict(features)
+    
+    if int(prediction[0]) == 0:
+        result = "SAFE"
+    elif int(prediction[0]) == 1:
+        result = "DEFACEMENT"
+    elif int(prediction[0]) == 2:
+        result = "PHISHING"
+    elif int(prediction[0]) == 3:
+        result = "MALWARE"
+        
+    return jsonify({"prediction": result})
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=8000)