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# app.py
import streamlit as st
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
import joblib
# Load the SVM model and TF-IDF vectorizer
svm_model = joblib.load('svm_model.pkl')
tfidf_vectorizer = joblib.load('tfidf_vectorizer.pkl')
def preprocess_input(text):
# Preprocess the input text (e.g., convert to lowercase, remove special characters, etc.)
# Implement your specific preprocessing steps based on the training data
processed_text = text.lower()
# Add more preprocessing steps as needed
return processed_text
def predict_bullying(text):
# Preprocess input text
processed_text = preprocess_input(text)
# Convert text to numerical representation using TF-IDF
text_tfidf = tfidf_vectorizer.transform([processed_text])
# Make prediction using the SVM model
prediction = svm_model.predict(text_tfidf)[0]
return prediction
# Streamlit UI
def main():
st.title("Cyberbullying Detection App (Arabic)")
user_input = st.text_area("Enter a text for cyberbullying detection:")
if st.button("Predict"):
if user_input:
prediction = predict_bullying(user_input)
if prediction == "Bullying":
st.write(f"<span style='color:red; font-weight:bold'>{prediction}</span>", unsafe_allow_html=True)
else:
st.write(f"<span style='color:cyan; font-weight:bold'>{prediction}</span>", unsafe_allow_html=True)
else:
st.warning("Please enter text for prediction.")
st.header("Sample Texts")
st.write("It's always the filthy " + "<span style='color:red; font-weight:bold'>bitch</span> that creates problem between us", unsafe_allow_html=True)
st.write("Do you believe it is appropriate to refer to a Muslim as a " + "<span style='color:red; font-weight:bold'>terrorist</span>?", unsafe_allow_html=True)
st.write("I hope you're doing well and having a great day. Let's catch up soon! 😊")
st.write("The team's score is disgraceful.")
if __name__ == "__main__":
main()
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