Delete app.py

app.py

@@ -1,743 +0,0 @@
-import os 
-os.system("pip install seaborn")
-os.system("pip install scikit-learn")
-os.system("pip install whois")
-os.system("pip install googlesearch-python")
-import numpy as np # linear algebra
-import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
-
-import matplotlib.pyplot as plt
-#%matplotlib inline
-import seaborn as sns
-from sklearn import metrics 
-import warnings
-warnings.filterwarnings('ignore')
-
-data = pd.read_csv('phishing.csv')
-data.head(20)
-
-data.columns
-len(data.columns)
-data.isnull().sum()
-X = data.drop(["class","Index"],axis =1)
-y = data["class"]
-
-fig, ax = plt.subplots(1, 1, figsize=(15, 9))
-sns.heatmap(data.corr(), annot=True,cmap='viridis')
-plt.title('Correlation between different features', fontsize = 15, c='black')
-plt.show()
-
-corr=data.corr()
-corr.head()
-
-corr['class']=abs(corr['class'])
-corr.head()
-
-incCorr=corr.sort_values(by='class',ascending=False)
-incCorr.head()
-
-incCorr['class']
-
-tenfeatures=incCorr[1:11].index
-twenfeatures=incCorr[1:21].index
-
-#Structutre to Store metrics
-ML_Model = []
-accuracy = []
-f1_score = []
-precision = []
-
-def storeResults(model, a,b,c):
-  ML_Model.append(model)
-  accuracy.append(round(a, 3))
-  f1_score.append(round(b, 3))
-  precision.append(round(c, 3))
-
-def KNN(X):
-  x=[a for a in range(1,10,2)]
-  knntrain=[]
-  knntest=[]
-  from sklearn.model_selection import train_test_split
-  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)
-  X_train.shape, y_train.shape, X_test.shape, y_test.shape
-  for i in range(1,10,2):
-    from sklearn.neighbors import KNeighborsClassifier
-    knn = KNeighborsClassifier(n_neighbors=i)
-    knn.fit(X_train,y_train)
-    y_train_knn = knn.predict(X_train)
-    y_test_knn = knn.predict(X_test)
-    acc_train_knn = metrics.accuracy_score(y_train,y_train_knn)
-    acc_test_knn = metrics.accuracy_score(y_test,y_test_knn)
-    print("K-Nearest Neighbors with k={}: Accuracy on training Data: {:.3f}".format(i,acc_train_knn))
-    print("K-Nearest Neighbors with k={}: Accuracy on test Data: {:.3f}".format(i,acc_test_knn))
-    knntrain.append(acc_train_knn)
-    knntest.append(acc_test_knn)
-    print()
-  import matplotlib.pyplot as plt
-  plt.plot(x,knntrain,label="Train accuracy")
-  plt.plot(x,knntest,label="Test accuracy")
-  plt.legend()
-  plt.show()
-
-Xmain=X
-Xten=X[tenfeatures]
-Xtwen=X[twenfeatures]
-
-KNN(Xmain)
-
-KNN(Xten)
-
-KNN(Xtwen)
-
-from sklearn.model_selection import train_test_split
-from sklearn.neighbors import KNeighborsClassifier
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)
-X_train.shape, y_train.shape, X_test.shape, y_test.shape
-
-knn = KNeighborsClassifier(n_neighbors=5)
-knn.fit(X_train,y_train)
-
-y_train_knn = knn.predict(X_train)
-y_test_knn = knn.predict(X_test)
-
-acc_train_knn = metrics.accuracy_score(y_train,y_train_knn)
-acc_test_knn = metrics.accuracy_score(y_test,y_test_knn)
-
-f1_score_train_knn = metrics.f1_score(y_train,y_train_knn)
-f1_score_test_knn = metrics.f1_score(y_test,y_test_knn)
-
-precision_score_train_knn = metrics.precision_score(y_train,y_train_knn)
-precision_score_test_knn = metrics.precision_score(y_test,y_test_knn)
-
-storeResults('K-Nearest Neighbors',acc_test_knn,f1_score_test_knn,precision_score_train_knn)
-
-def SVM(X, y):
-    x=[a for a in range(1,10,2)]
-    svmtrain=[]
-    svmtest=[]
-    from sklearn.model_selection import train_test_split
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)
-    X_train.shape, y_train.shape, X_test.shape, y_test.shape
-    from sklearn.svm import SVC
-    for i in range(1,10,2):
-        svm = SVC(kernel='linear', C=i)
-        svm.fit(X_train, y_train)
-        y_train_svm = svm.predict(X_train)
-        y_test_svm = svm.predict(X_test)
-        acc_train_svm = metrics.accuracy_score(y_train, y_train_svm)
-        acc_test_svm = metrics.accuracy_score(y_test, y_test_svm)
-        print("SVM with C={}: Accuracy on training Data: {:.3f}".format(i,acc_train_svm))
-        print("SVM with C={}: Accuracy on test Data: {:.3f}".format(i,acc_test_svm))
-        svmtrain.append(acc_train_svm)
-        svmtest.append(acc_test_svm)
-        print()
-    import matplotlib.pyplot as plt
-    plt.plot(x,svmtrain,label="Train accuracy")
-    plt.plot(x,svmtest,label="Test accuracy")
-    plt.legend()
-    plt.show()
-
-
-Xmain=X
-Xten=X[tenfeatures]
-Xtwen=X[twenfeatures]
-
-SVM(Xmain,y)
-SVM(Xten,y)
-SVM(Xtwen,y)
-
-from sklearn.model_selection import train_test_split
-from sklearn.svm import SVC
-from sklearn import metrics
-
-
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-svm = SVC(kernel='linear', C=1, random_state=42)
-svm.fit(X_train, y_train)
-
-
-y_train_svm = svm.predict(X_train)
-y_test_svm = svm.predict(X_test)
-
-
-acc_train_svm = metrics.accuracy_score(y_train, y_train_svm)
-acc_test_svm = metrics.accuracy_score(y_test, y_test_svm)
-
-f1_score_train_svm = metrics.f1_score(y_train, y_train_svm)
-f1_score_test_svm = metrics.f1_score(y_test, y_test_svm)
-
-precision_score_train_svm = metrics.precision_score(y_train, y_train_svm)
-precision_score_test_svm = metrics.precision_score(y_test, y_test_svm)
-
-print("SVM with C={}: Accuracy on training data: {:.3f}".format(1, acc_train_svm))
-print("SVM with C={}: Accuracy on test data: {:.3f}".format(1, acc_test_svm))
-print("SVM with C={}: F1 score on training data: {:.3f}".format(1, f1_score_train_svm))
-print("SVM with C={}: F1 score on test data: {:.3f}".format(1, f1_score_test_svm))
-print("SVM with C={}: Precision on training data: {:.3f}".format(1, precision_score_train_svm))
-print("SVM with C={}: Precision on test data: {:.3f}".format(1, precision_score_test_svm))
-
-storeResults('Support Vector Machines',acc_test_svm,f1_score_test_svm,precision_score_train_svm)
-
-from sklearn.model_selection import train_test_split
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)
-X_train.shape, y_train.shape, X_test.shape, y_test.shape
-
-from sklearn.ensemble import GradientBoostingClassifier
-gbc = GradientBoostingClassifier(max_depth=4,learning_rate=0.7)
-gbc.fit(X_train,y_train)
-
-y_train_gbc = gbc.predict(X_train)
-y_test_gbc = gbc.predict(X_test)
-
-acc_train_gbc = metrics.accuracy_score(y_train,y_train_gbc)
-acc_test_gbc = metrics.accuracy_score(y_test,y_test_gbc)
-print("Gradient Boosting Classifier : Accuracy on training Data: {:.3f}".format(acc_train_gbc))
-print("Gradient Boosting Classifier : Accuracy on test Data: {:.3f}".format(acc_test_gbc))
-print()
-
-f1_score_train_gbc = metrics.f1_score(y_train,y_train_gbc)
-f1_score_test_gbc = metrics.f1_score(y_test,y_test_gbc)
-
-precision_score_train_gbc = metrics.precision_score(y_train,y_train_gbc)
-precision_score_test_gbc = metrics.precision_score(y_test,y_test_gbc)
-
-storeResults('Gradient Boosting Classifier',acc_test_gbc,f1_score_test_gbc,precision_score_train_gbc)
-
-df = pd.DataFrame({
-    'Modelname': ML_Model,
-    'Accuracy Score': accuracy,
-    'F1 Score': f1_score,
-    'Precision Score': precision
-})
-df.set_index('Modelname', inplace=True)
-
-# plot the scores for each model
-
-fig, ax = plt.subplots(figsize=(10,10))
-df.plot(kind='bar', ax=ax)
-ax.set_xticklabels(df.index, rotation=0)
-ax.set_ylim([0.9, 1])
-ax.set_yticks([0.9,0.91,0.92,0.93,0.94,0.95,0.96,0.97,0.98,0.99,1])
-ax.set_xlabel('Model')
-ax.set_ylabel('Score')
-ax.set_title('Model Scores')
-plt.show()
-
-import whois
-
-import googlesearch
-
-import ipaddress
-import re
-import urllib.request
-from bs4 import BeautifulSoup
-import socket
-import requests
-import google
-import whois
-from datetime import date, datetime
-import time
-from dateutil.parser import parse as date_parse
-from urllib.parse import urlparse
-
-class FeatureExtraction:
-    features = []
-    def __init__(self,url):
-        self.features = []
-        self.url = url
-        self.domain = ""
-        self.whois_response = ""
-        self.urlparse = ""
-        self.response = ""
-        self.soup = ""
-
-        try:
-            self.response = requests.get(url)
-            self.soup = BeautifulSoup(response.text, 'html.parser')
-        except:
-            pass
-
-        try:
-            self.urlparse = urlparse(url)
-            self.domain = self.urlparse.netloc
-        except:
-            pass
-
-        try:
-            self.whois_response = whois.whois(self.domain)
-        except:
-            pass
-
-
-        
-
-        self.features.append(self.UsingIp())
-        self.features.append(self.longUrl())
-        self.features.append(self.shortUrl())
-        self.features.append(self.symbol())
-        self.features.append(self.redirecting())
-        self.features.append(self.prefixSuffix())
-        self.features.append(self.SubDomains())
-        self.features.append(self.Hppts())
-        self.features.append(self.DomainRegLen())
-        self.features.append(self.Favicon())
-        
-
-        self.features.append(self.NonStdPort())
-        self.features.append(self.HTTPSDomainURL())
-        self.features.append(self.RequestURL())
-        self.features.append(self.AnchorURL())
-        self.features.append(self.LinksInScriptTags())
-        self.features.append(self.ServerFormHandler())
-        self.features.append(self.InfoEmail())
-        self.features.append(self.AbnormalURL())
-        self.features.append(self.WebsiteForwarding())
-        self.features.append(self.StatusBarCust())
-
-        self.features.append(self.DisableRightClick())
-        self.features.append(self.UsingPopupWindow())
-        self.features.append(self.IframeRedirection())
-        self.features.append(self.AgeofDomain())
-        self.features.append(self.DNSRecording())
-        self.features.append(self.WebsiteTraffic())
-        self.features.append(self.PageRank())
-        self.features.append(self.GoogleIndex())
-        self.features.append(self.LinksPointingToPage())
-        self.features.append(self.StatsReport())
-
-
-     # 1.UsingIp
-    def UsingIp(self):
-        try:
-            ipaddress.ip_address(self.url)
-            return -1
-        except:
-            return 1
-
-    # 2.longUrl
-    def longUrl(self):
-        if len(self.url) < 54:
-            return 1
-        if len(self.url) >= 54 and len(self.url) <= 75:
-            return 0
-        return -1
-
-    # 3.shortUrl
-    def shortUrl(self):
-        match = re.search('bit\.ly|goo\.gl|shorte\.st|go2l\.ink|x\.co|ow\.ly|t\.co|tinyurl|tr\.im|is\.gd|cli\.gs|'
-                    'yfrog\.com|migre\.me|ff\.im|tiny\.cc|url4\.eu|twit\.ac|su\.pr|twurl\.nl|snipurl\.com|'
-                    'short\.to|BudURL\.com|ping\.fm|post\.ly|Just\.as|bkite\.com|snipr\.com|fic\.kr|loopt\.us|'
-                    'doiop\.com|short\.ie|kl\.am|wp\.me|rubyurl\.com|om\.ly|to\.ly|bit\.do|t\.co|lnkd\.in|'
-                    'db\.tt|qr\.ae|adf\.ly|goo\.gl|bitly\.com|cur\.lv|tinyurl\.com|ow\.ly|bit\.ly|ity\.im|'
-                    'q\.gs|is\.gd|po\.st|bc\.vc|twitthis\.com|u\.to|j\.mp|buzurl\.com|cutt\.us|u\.bb|yourls\.org|'
-                    'x\.co|prettylinkpro\.com|scrnch\.me|filoops\.info|vzturl\.com|qr\.net|1url\.com|tweez\.me|v\.gd|tr\.im|link\.zip\.net', self.url)
-        if match:
-            return -1
-        return 1
-
-    # 4.Symbol@
-    def symbol(self):
-        if re.findall("@",self.url):
-            return -1
-        return 1
-    
-    # 5.Redirecting//
-    def redirecting(self):
-        if self.url.rfind('//')>6:
-            return -1
-        return 1
-    
-    # 6.prefixSuffix
-    def prefixSuffix(self):
-        try:
-            match = re.findall('\-', self.domain)
-            if match:
-                return -1
-            return 1
-        except:
-            return -1
-    
-    # 7.SubDomains
-    def SubDomains(self):
-        dot_count = len(re.findall("\.", self.url))
-        if dot_count == 1:
-            return 1
-        elif dot_count == 2:
-            return 0
-        return -1
-
-    # 8.HTTPS
-    def Hppts(self):
-        try:
-            https = self.urlparse.scheme
-            if 'https' in https:
-                return 1
-            return -1
-        except:
-            return 1
-
-    # 9.DomainRegLen
-    def DomainRegLen(self):
-        try:
-            expiration_date = self.whois_response.expiration_date
-            creation_date = self.whois_response.creation_date
-            try:
-                if(len(expiration_date)):
-                    expiration_date = expiration_date[0]
-            except:
-                pass
-            try:
-                if(len(creation_date)):
-                    creation_date = creation_date[0]
-            except:
-                pass
-
-            age = (expiration_date.year-creation_date.year)*12+ (expiration_date.month-creation_date.month)
-            if age >=12:
-                return 1
-            return -1
-        except:
-            return -1
-
-    # 10. Favicon
-    def Favicon(self):
-        try:
-            for head in self.soup.find_all('head'):
-                for head.link in self.soup.find_all('link', href=True):
-                    dots = [x.start(0) for x in re.finditer('\.', head.link['href'])]
-                    if self.url in head.link['href'] or len(dots) == 1 or domain in head.link['href']:
-                        return 1
-            return -1
-        except:
-            return -1
-
-    # 11. NonStdPort
-    def NonStdPort(self):
-        try:
-            port = self.domain.split(":")
-            if len(port)>1:
-                return -1
-            return 1
-        except:
-            return -1
-
-    # 12. HTTPSDomainURL
-    def HTTPSDomainURL(self):
-        try:
-            if 'https' in self.domain:
-                return -1
-            return 1
-        except:
-            return -1
-    
-    # 13. RequestURL
-    def RequestURL(self):
-        try:
-            for img in self.soup.find_all('img', src=True):
-                dots = [x.start(0) for x in re.finditer('\.', img['src'])]
-                if self.url in img['src'] or self.domain in img['src'] or len(dots) == 1:
-                    success = success + 1
-                i = i+1
-
-            for audio in self.soup.find_all('audio', src=True):
-                dots = [x.start(0) for x in re.finditer('\.', audio['src'])]
-                if self.url in audio['src'] or self.domain in audio['src'] or len(dots) == 1:
-                    success = success + 1
-                i = i+1
-
-            for embed in self.soup.find_all('embed', src=True):
-                dots = [x.start(0) for x in re.finditer('\.', embed['src'])]
-                if self.url in embed['src'] or self.domain in embed['src'] or len(dots) == 1:
-                    success = success + 1
-                i = i+1
-
-            for iframe in self.soup.find_all('iframe', src=True):
-                dots = [x.start(0) for x in re.finditer('\.', iframe['src'])]
-                if self.url in iframe['src'] or self.domain in iframe['src'] or len(dots) == 1:
-                    success = success + 1
-                i = i+1
-
-            try:
-                percentage = success/float(i) * 100
-                if percentage < 22.0:
-                    return 1
-                elif((percentage >= 22.0) and (percentage < 61.0)):
-                    return 0
-                else:
-                    return -1
-            except:
-                return 0
-        except:
-            return -1
-    
-    # 14. AnchorURL
-    def AnchorURL(self):
-        try:
-            i,unsafe = 0,0
-            for a in self.soup.find_all('a', href=True):
-                if "#" in a['href'] or "javascript" in a['href'].lower() or "mailto" in a['href'].lower() or not (url in a['href'] or self.domain in a['href']):
-                    unsafe = unsafe + 1
-                i = i + 1
-
-            try:
-                percentage = unsafe / float(i) * 100
-                if percentage < 31.0:
-                    return 1
-                elif ((percentage >= 31.0) and (percentage < 67.0)):
-                    return 0
-                else:
-                    return -1
-            except:
-                return -1
-
-        except:
-            return -1
-
-    # 15. LinksInScriptTags
-    def LinksInScriptTags(self):
-        try:
-            i,success = 0,0
-        
-            for link in self.soup.find_all('link', href=True):
-                dots = [x.start(0) for x in re.finditer('\.', link['href'])]
-                if self.url in link['href'] or self.domain in link['href'] or len(dots) == 1:
-                    success = success + 1
-                i = i+1
-
-            for script in self.soup.find_all('script', src=True):
-                dots = [x.start(0) for x in re.finditer('\.', script['src'])]
-                if self.url in script['src'] or self.domain in script['src'] or len(dots) == 1:
-                    success = success + 1
-                i = i+1
-
-            try:
-                percentage = success / float(i) * 100
-                if percentage < 17.0:
-                    return 1
-                elif((percentage >= 17.0) and (percentage < 81.0)):
-                    return 0
-                else:
-                    return -1
-            except:
-                return 0
-        except:
-            return -1
-
-    # 16. ServerFormHandler
-    def ServerFormHandler(self):
-        try:
-            if len(self.soup.find_all('form', action=True))==0:
-                return 1
-            else :
-                for form in self.soup.find_all('form', action=True):
-                    if form['action'] == "" or form['action'] == "about:blank":
-                        return -1
-                    elif self.url not in form['action'] and self.domain not in form['action']:
-                        return 0
-                    else:
-                        return 1
-        except:
-            return -1
-
-    # 17. InfoEmail
-    def InfoEmail(self):
-        try:
-            if re.findall(r"[mail\(\)|mailto:?]", self.soap):
-                return -1
-            else:
-                return 1
-        except:
-            return -1
-
-    # 18. AbnormalURL
-    def AbnormalURL(self):
-        try:
-            if self.response.text == self.whois_response:
-                return 1
-            else:
-                return -1
-        except:
-            return -1
-
-    # 19. WebsiteForwarding
-    def WebsiteForwarding(self):
-        try:
-            if len(self.response.history) <= 1:
-                return 1
-            elif len(self.response.history) <= 4:
-                return 0
-            else:
-                return -1
-        except:
-             return -1
-
-    # 20. StatusBarCust
-    def StatusBarCust(self):
-        try:
-            if re.findall("<script>.+onmouseover.+</script>", self.response.text):
-                return 1
-            else:
-                return -1
-        except:
-             return -1
-
-    # 21. DisableRightClick
-    def DisableRightClick(self):
-        try:
-            if re.findall(r"event.button ?== ?2", self.response.text):
-                return 1
-            else:
-                return -1
-        except:
-             return -1
-
-    # 22. UsingPopupWindow
-    def UsingPopupWindow(self):
-        try:
-            if re.findall(r"alert\(", self.response.text):
-                return 1
-            else:
-                return -1
-        except:
-             return -1
-
-    # 23. IframeRedirection
-    def IframeRedirection(self):
-        try:
-            if re.findall(r"[<iframe>|<frameBorder>]", self.response.text):
-                return 1
-            else:
-                return -1
-        except:
-             return -1
-
-    # 24. AgeofDomain
-    def AgeofDomain(self):
-        try:
-            creation_date = self.whois_response.creation_date
-            try:
-                if(len(creation_date)):
-                    creation_date = creation_date[0]
-            except:
-                pass
-
-            today  = date.today()
-            age = (today.year-creation_date.year)*12+(today.month-creation_date.month)
-            if age >=6:
-                return 1
-            return -1
-        except:
-            return -1
-
-    # 25. DNSRecording    
-    def DNSRecording(self):
-        try:
-            creation_date = self.whois_response.creation_date
-            try:
-                if(len(creation_date)):
-                    creation_date = creation_date[0]
-            except:
-                pass
-
-            today  = date.today()
-            age = (today.year-creation_date.year)*12+(today.month-creation_date.month)
-            if age >=6:
-                return 1
-            return -1
-        except:
-            return -1
-
-    # 26. WebsiteTraffic   
-    def WebsiteTraffic(self):
-        try:
-            rank = BeautifulSoup(urllib.request.urlopen("http://data.alexa.com/data?cli=10&dat=s&url=" + url).read(), "xml").find("REACH")['RANK']
-            if (int(rank) < 100000):
-                return 1
-            return 0
-        except :
-            return -1
-
-    # 27. PageRank
-    def PageRank(self):
-        try:
-            prank_checker_response = requests.post("https://www.checkpagerank.net/index.php", {"name": self.domain})
-
-            global_rank = int(re.findall(r"Global Rank: ([0-9]+)", rank_checker_response.text)[0])
-            if global_rank > 0 and global_rank < 100000:
-                return 1
-            return -1
-        except:
-            return -1
-            
-
-    # 28. GoogleIndex
-    def GoogleIndex(self):
-        try:
-            site = search(self.url, 5)
-            if site:
-                return 1
-            else:
-                return -1
-        except:
-            return 1
-
-    # 29. LinksPointingToPage
-    def LinksPointingToPage(self):
-        try:
-            number_of_links = len(re.findall(r"<a href=", self.response.text))
-            if number_of_links == 0:
-                return 1
-            elif number_of_links <= 2:
-                return 0
-            else:
-                return -1
-        except:
-            return -1
-
-    # 30. StatsReport
-    def StatsReport(self):
-        try:
-            url_match = re.search(
-        'at\.ua|usa\.cc|baltazarpresentes\.com\.br|pe\.hu|esy\.es|hol\.es|sweddy\.com|myjino\.ru|96\.lt|ow\.ly', url)
-            ip_address = socket.gethostbyname(self.domain)
-            ip_match = re.search('146\.112\.61\.108|213\.174\.157\.151|121\.50\.168\.88|192\.185\.217\.116|78\.46\.211\.158|181\.174\.165\.13|46\.242\.145\.103|121\.50\.168\.40|83\.125\.22\.219|46\.242\.145\.98|'
-                                '107\.151\.148\.44|107\.151\.148\.107|64\.70\.19\.203|199\.184\.144\.27|107\.151\.148\.108|107\.151\.148\.109|119\.28\.52\.61|54\.83\.43\.69|52\.69\.166\.231|216\.58\.192\.225|'
-                                '118\.184\.25\.86|67\.208\.74\.71|23\.253\.126\.58|104\.239\.157\.210|175\.126\.123\.219|141\.8\.224\.221|10\.10\.10\.10|43\.229\.108\.32|103\.232\.215\.140|69\.172\.201\.153|'
-                                '216\.218\.185\.162|54\.225\.104\.146|103\.243\.24\.98|199\.59\.243\.120|31\.170\.160\.61|213\.19\.128\.77|62\.113\.226\.131|208\.100\.26\.234|195\.16\.127\.102|195\.16\.127\.157|'
-                                '34\.196\.13\.28|103\.224\.212\.222|172\.217\.4\.225|54\.72\.9\.51|192\.64\.147\.141|198\.200\.56\.183|23\.253\.164\.103|52\.48\.191\.26|52\.214\.197\.72|87\.98\.255\.18|209\.99\.17\.27|'
-                                '216\.38\.62\.18|104\.130\.124\.96|47\.89\.58\.141|78\.46\.211\.158|54\.86\.225\.156|54\.82\.156\.19|37\.157\.192\.102|204\.11\.56\.48|110\.34\.231\.42', ip_address)
-            if url_match:
-                return -1
-            elif ip_match:
-                return -1
-            return 1
-        except:
-            return 1
-    
-    def getFeaturesList(self):
-        return self.features
-    
-gbc = GradientBoostingClassifier(max_depth=4,learning_rate=0.7)
-gbc.fit(X_train,y_train)  
-
-import streamlit as st
-st.title("Phishing Website Detection")
-#
-# User input for URL
-url = st.text_input("Enter the Url:", key="url_input")
-#can provide any URL. this URL was taken from PhishTank
-
-# Predict and display the result
-if st.button("Check"):
-    if url:
-        obj = FeatureExtraction(url)
-        x = np.array(obj.getFeaturesList()).reshape(1, 30)
-        y_pred = gbc.predict(x)[0]
-        if y_pred == 1:
-            st.write("We guess it is a safe website")
-        else:
-            st.write("Caution! Suspicious website detected")
-        st.write(y_pred)
-    else:
-        st.write("Please enter a URL.")