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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Cleaned_Hinglish_dataset.xlsx filter=lfs diff=lfs merge=lfs -text

Cleaned_Hinglish_dataset.xlsx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:50522beac6bdd99e328fc48a8a4d2cc27c044419659c6f67cb52e7d87a53a870
+size 1370811

CyberBullyingHinglish.py

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+import streamlit as st
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+import re
+
+
+def predict_bullying_type(input_text, bad_words):
+    df = pd.read_excel("Cleaned_Hinglish_dataset.xlsx")
+    df.dropna(subset=["cleaned_text"], inplace=True)
+    X = df["cleaned_text"]
+    y = df["type"]
+    tfidf_vectorizer = TfidfVectorizer()
+    X_tfidf = tfidf_vectorizer.fit_transform(X)
+    logistic_regression_classifier = LogisticRegression(max_iter=1000)
+    logistic_regression_classifier.fit(X_tfidf, y)
+
+    text_tfidf = tfidf_vectorizer.transform([input_text])
+    predicted_types = logistic_regression_classifier.predict(text_tfidf)
+
+    detected_bad_words = []
+
+    for bad_word in bad_words:
+        if re.search(r'\b{}\b'.format(re.escape(bad_word)), input_text, flags=re.IGNORECASE):
+            detected_bad_words.append(bad_word)
+        input_text = re.sub(r'\b{}\b'.format(re.escape(bad_word)), '*******', input_text, flags=re.IGNORECASE)
+
+    prediction = "Not Cyberbullying" if predicted_types[0] != "OAG" else "Cyberbullying"
+    return prediction, predicted_types[0], input_text, detected_bad_words
+
+st.title("Cyberbullying Detection App (Hinglish)")
+
+input_text = st.text_area("Enter a text for cyberbullying detection:")
+
+
+if st.button("Predict"):
+    bad_words = ['bahenchod', 'peshan', 'behenchod', 'bhenchod', 'bhenchodd', 'b.c.', 'bc', 'bakchod', 'bakchodd', 'bakchodi', 'bevda', 'bewda', 'bevdey', 'bewday', 'bhadwaa', 'bhosada', 'bhosda', 'bhosdaa', 'bhosdike', 'bhonsdike', 'bhosdiki', 'bhosdiwala', 'bhosdiwale', 'Bhosadchodal', 'Bhosadchod', 'Bhosadchodal', 'Bhosadchod', 'babbe', 'babbey', 'bube', 'bubey', 'bur', 'burr', 'buurr', 'buur', 'charsi', 'chooche', 'choochi', 'chuchi', 'chhod', 'chod', 'chodd', 'chudne', 'chudney', 'chudwa', 'chudwaa', 'chudwane', 'chudwaane', 'chaat', 'choot', 'chut', 'chute', 'chutia', 'chutiya', 'chutiye', 'dalaal', 'dalal', 'dalle', 'dalley', 'fattu', 'gadha', 'gadhe', 'gadhalund', 'gaand', 'gand', 'gandu', 'gandfat', 'gandfut', 'gandiya', 'gandiye', 'goo', 'gu', 'gote', 'gotey', 'gotte', 'hag', 'haggu', 'hagne', 'hagney', 'harami', 'haramjada', 'haraamjaada', 'haramzyada', 'haraamzyaada', 'haraamjaade', 'haraamzaade', 'haraamkhor', 'haramkhor', 'jhat', 'jhaat', 'jhaatu', 'jhatu', 'kutta', 'kutte', 'kuttey', 'kutia', 'kutiya', 'kuttiya', 'kutti',  'landi', 'landy', 'laude', 'laudey', 'laura', 'lora', 'lauda', 'ling', 'loda', 'lode', 'lund', 'launda', 'lounde', 'laundey', 'laundi', 'loundi', 'laundiya', 'loundiya', 'lulli', 'maar ja', 'madarchod', 'madarchodd', 'madarchood', 'madarchoot', 'madarchut', 'm.c.', 'mc', 'mamme', 'mammey', 'moot', 'mut', 'mootne', 'mutne', 'mooth', 'muth', 'nunnu', 'pesaab', 'pesab', 'peshaab', 'peshab', 'pillay', 'pille', 'pilley', 'pisaab', 'pisab', 'porkistan', 'raand', 'rand', 'randi', 'randy', 'suar', 'tatti', 'tatty', 'ullu', 'pappu']
+    prediction, bullying_type, filtered_text, detected_bad_words = predict_bullying_type(input_text, bad_words)
+    
+    st.write("Prediction:", prediction)
+    st.write("Cyberbullying Type: ", bullying_type)
+    if detected_bad_words:
+        st.write("Bad Words:", ', '.join(detected_bad_words))
+    else:
+        st.write("<span style='color:cyan;'>No bad words found.</span>", unsafe_allow_html=True)
+    
+    if detected_bad_words:
+        st.write("Filtered Text:")
+        st.write(f"<span style='color:red; font-weight:bold'>{filtered_text}</span>", unsafe_allow_html=True) 
+    else:
+        st.write("Original Text:")
+        st.write(f"{input_text}", unsafe_allow_html=True)
+    
+st.header("Sample Texts")
+st.write("es" + "<span style='color:red; font-weight:bold'> kutte</span> ko jail daal desh drohi hai", unsafe_allow_html=True)
+st.write("" + "<span style='color:red; font-weight:bold'>pappu </span>gandhi", unsafe_allow_html=True)
+st.write("bhaijan mera business partner ka naam alisher h hum aaps ak dusre par aankh band krke vishvash krte h")

requirements.txt

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+scikit-learn
+regex
+keras
+tensorflow
+numpy
+streamlit
+pandas
+openpyxl