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| import streamlit as st | |
| import numpy as np | |
| import pickle | |
| import streamlit.components.v1 as components | |
| from sklearn.feature_extraction.text import CountVectorizer | |
| from sklearn.model_selection import train_test_split | |
| import pandas as pd | |
| import nltk | |
| import re | |
| from sklearn.naive_bayes import BernoulliNB | |
| from collections import Counter | |
| from nltk.corpus import stopwords | |
| df = pd.read_csv('spam_new.csv',encoding= 'latin-1') | |
| # X will be the features | |
| X = np.array(df["message"]) | |
| # y will be the target variable | |
| y = np.array(df["class"]) | |
| cv = CountVectorizer() | |
| X = cv.fit_transform(X) | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, | |
| test_size=0.33, | |
| random_state=42) | |
| model = BernoulliNB() | |
| model.fit(X_train, y_train) | |
| # Function for model prediction | |
| def model_prediction(features): | |
| features = cv.transform([features]).toarray() | |
| Message = str(list(model.predict(features))) | |
| return Message | |
| def app_design(): | |
| image = '58.png' # Load image | |
| st.image(image, use_column_width=True) | |
| st.subheader("Enter the following values:") | |
| text= st.text_input("Enter your text") | |
| # Create a feature list from the user inputs | |
| features = text # add features according to notebook | |
| # Make a prediction when the user clicks the "Predict" button | |
| if st.button('Predict Spam'): | |
| predicted_value = model_prediction(features) | |
| if predicted_value == "['ham']": | |
| st.success("Your comment is not spam") | |
| elif predicted_value == "['spam']": | |
| st.success("Your Comment is spam") | |
| def about_hidevs(): | |
| components.html(""" | |
| <div> | |
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| <p class="subtitle">🔍 Seeking the perfect job? HiDevs Community is your gateway to career success in the tech industry. Explore free expert courses, job-seeking support, and career transformation tips.</p> | |
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| <p class="subtitle">Book free of cost 1:1 mentorship on any topic of your choice — <a class="link" href="https://topmate.io/deepakchawla1307">topmate</a></p> | |
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| <p class="subtitle">💡 Join us now, and turbocharge your career!</p> | |
| <p class="subtitle"><a class="link" href="https://hidevscommunity.wixsite.com/hidevs" target="__blank">Website</a> | |
| <a class="link" href="https://www.youtube.com/@HidevsCommunity1307/" target="__blank">YouTube</a> | |
| <a class="link" href="https://www.instagram.com/hidevs_community/" target="__blank">Instagram</a> | |
| <a class="link" href="https://medium.com/@hidevscommunity" target="__blank">Medium</a> | |
| <a class="link" href="https://www.linkedin.com/company/hidevs-community/" target="__blank">LinkedIn</a> | |
| <a class="link" href="https://github.com/hidevscommunity" target="__blank">GitHub</a></p> | |
| </div> | |
| """, | |
| height=600) | |
| def main(): | |
| # Set the app title and add your website name and logo | |
| st.set_page_config( | |
| page_title="Spam Detection", | |
| page_icon=":chart_with_upwards_trend:", | |
| ) | |
| st.title("Welcome to our Spam Detection App!") | |
| app_design() | |
| st.header("About HiDevs Community") | |
| about_hidevs() | |
| if __name__ == '__main__': | |
| main() |