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app.py
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app.py
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import streamlit as st
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.utils import check_array
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
# Python program to convert a list to strin
from model_methods import predict
classes = {0:'url',1:'label'}
class_labels = list(classes.values())
st.title("MalURL Dectection")
st.markdown('**Objective** : Given details about the url')
st.markdown('The model can predict if a url is malicious or not ')
def predict_class():
scaler.fit([[user_list]])
data = scaler.transform([[url]])
result = predict(data)
st.write("The predicted class is ",result)
st.markdown("**Please enter the url link that you want to check if it's malicios or not")
url = st.text_input('Enter elements of a list separated by space ')
print("\n")
user_list = url.split()
print('list: ', user_list)
# print('list: ', user_list)
if st.button("Predict"):
predict_class()
# # data = st.text_input("Enter the url", "")
# input_string = st.text_input('Enter elements of a list separated by space ')
# print("\n")
# user_list = input_string.split()
# # print list
# print('list: ', user_list)
# # data = data.lower()
# # output = []
# # for character in data:
# # number = ord(character) - 96
# # output.append(number)
# # print(output)
# # # Driver code
# # # print(listToString(output))
# sampledata = ['100', '20', '30']
# if(st.button('Submit')):
# result = predict([[user_list]])
# print(type(result))
# st.success(result)