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app.py

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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.")
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+pandas
+scikit-learn
+joblib

svm_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:86cc2e53d9ed03b786c0b0d08162e63cd2a0eddf30c9ef3548837178f326a034
+size 384771

tfidf_vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:86a2ed13d6b9ccca4d42dd85f37ef23980bfb8f6c5090bd471f7d653bf2203df
+size 493534