Upload 3 files

phishing (2).pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:33ce28564ef60466fd7514a1d34c4b3742c11769f4ecd9cd5e1d760abbdc6623
+size 7659105

phising.py

@@ -0,0 +1,28 @@
+from flask import Flask, render_template, request
+import pickle
+
+app = Flask(__name__)
+
+# Load the pre-trained phishing detection model from the .pkl file
+with open('C:/Users/vtu24/Documents/phisning_dection/phisning/phisning/phishing (2).pkl', 'rb') as model_file:
+    phishing_model = pickle.load(model_file)
+
+def is_phishing(url):
+    # Replace this with your actual prediction logic
+    # Example: You might need to preprocess the URL before making predictions
+    # prediction = phishing_model.predict(preprocess(url))
+    prediction = phishing_model.predict([url])  # Assuming the model expects a list of URLs
+    return prediction[0]
+
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    result = None
+
+    if request.method == 'POST':
+        url = request.form['url']
+        result = is_phishing(url)
+
+    return render_template('index.html', result=result)
+
+if __name__ == '__main__':
+    app.run(debug=True)