| import gradio as gr | |
| import pickle | |
| import pandas as pd | |
| from urlextract import URLExtract | |
| import URLFeatureExtraction | |
| with open("models/spam-clf.pkl", "rb") as f: | |
| model = pickle.load(f) | |
| with open("models/xgb.dat", "rb") as f: | |
| model2 = pickle.load(f) | |
| df = pd.DataFrame(columns=['URL', 'Phishy?']) | |
| def isPhishing(link): | |
| global df | |
| features = URLFeatureExtraction.featureExtraction(link) | |
| print(features) | |
| prediction = model2.predict([features]) | |
| print(prediction[0]) | |
| df = df._append({'URL': link, 'Phishy?': "UnSafe" if prediction[0] else "Safe"}, ignore_index=True) | |
| return prediction[0] | |
| def isSpam(Email): | |
| out = model.predict([Email]) | |
| return "Spam" if out[0] else "Not Spam" | |
| def check_URL(Email): | |
| extractor = URLExtract() | |
| urls = extractor.find_urls(Email) | |
| n_urls = len(urls) | |
| if not urls: return -1 | |
| if urls: | |
| bad_urls = sum([isPhishing(url) for url in urls]) | |
| else: | |
| bad_urls = 0 | |
| print("Out of {} urls {} are legitimate.".format(n_urls, n_urls - bad_urls)) | |
| return bad_urls | |
| def check_Mail(Email): | |
| bad_urls = check_URL(Email) | |
| if bad_urls==-1: | |
| return [isSpam(Email), 0, 'Safe', df] | |
| if URLFeatureExtraction.flag or bad_urls: | |
| return [isSpam(Email), bad_urls, 'Risky', df] | |
| return [isSpam(Email), bad_urls, 'Safe', df] | |
| iface = gr.Interface( | |
| fn=check_Mail, | |
| inputs=gr.Textbox(lines=6, placeholder="Enter or paste email here", label="Email"), | |
| outputs=[ | |
| gr.Textbox(label="Spam or Not"), | |
| gr.Textbox(label="Phishing Links Detected"), | |
| gr.Textbox(label="Consider this Mail as"), | |
| gr.Dataframe(label="Insights", interactive=False) | |
| ] | |
| ) | |
| iface.launch() |