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.gitattributes

@@ -33,3 +33,5 @@ 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
+tfidf_vectorizer.sav filter=lfs diff=lfs merge=lfs -text
+trained_model.sav filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,117 @@
+import streamlit as st
+import pickle
+
+# Load the trained model
+model_filename = 'trained_model.sav'
+with open(model_filename, 'rb') as model_file:
+    model = pickle.load(model_file)
+
+# Load the TF-IDF vectorizer
+vectorizer_filename = 'tfidf_vectorizer.sav'
+with open(vectorizer_filename, 'rb') as vectorizer_file:
+    vectorizer = pickle.load(vectorizer_file)
+
+# Define a function to predict cyberbullying and filter bad words
+def predict_cyberbullying_and_filter_bad_words(text):
+    text = text.lower()
+    # Vectorize the input text using the loaded vectorizer
+    text_tfidf = vectorizer.transform([text])
+    
+    # Use the trained model to make a prediction
+    prediction = model.predict(text_tfidf)
+    
+    # Initialize variables to store the filtered text and bad words
+    filtered_text = text
+    bad_words = []
+    
+    # List of cyberbullying words
+    cyberbullying_words = ["bitch", "fuck", "asshole", "shitty", "ass", "slut", "cunt", "motherfucker", "wanker", "dick", "shit", "bastard",
+                       "pissed off", "arse", "bugger", "bloody", "whore", "anal", "anus", "arse", "arrse", "assbag", "assbandit", 
+                       "assbanger", "assbite", "assclown", "asscock", "asscracker", "asses", "assface", "assfuck", "assfucker",
+                       "assfukka", "assgoblin", "asshat", "asshead", "asshole", "assholes", "asshopper", "assjacker", "asslick", 
+                       "asslicker", "assmonkey", "assmunch", "assmuncher", "assnigger", "asspirate", "assshit", "assshole", "asssucker", 
+                       "asswad", "asswhole", "asswipe", "auto erotic", "autoerotic", "asswound", "boobs", "bitch", "b1tch", "ballbag", 
+                       "balls", "ballsack", "bampot", "bangbros", "bareback", "barely legal", "barenaked", "bastard", "bastardo", "bastinado", 
+                       "bbw", "bdsm", "bitches", "bitchin", "bitching", "bitchtits", "bitchy", "blowjob", "boob", "boobs", "booobs", "boooobs",
+                       "booooobs", "booooooobs", "brotherfucker", "bumblefuck", "bung hole", "buttcheeks", "buttfucka", 
+                       "buttfucker", "butthole", "buttmuch", "buttplug", "cock", "cocksucker", "camgirl", "camslut", 
+                       "camwhore", "circlejerk", "clit", "cleveland steamer", "clit", "clitface", "clitfuck", "clitoris", 
+                       "clits", "clover clamps", "clusterfuck", "cockass", "cockbite", "cockburger", "cockeye", "cockface",
+                       "cockfucker", "cockhead", "cockjockey", "cockknoker", "cocklump", "cockmaster", "cockmongler",
+                       "cockmongruel", "cockmonkey", "cockmunch", "cockmuncher", "cocknose", "cocknugget", "cocks",
+                       "cockshit", "cocksmith", "cocksmoke", "cocksmoker", "cocksniffer", "cocksuck", "cocksucked", 
+                       "cocksucker", "cocksucking", "cocksucks", "cocksuka", "cocksukka", "cockwaffle", "cok", "cokmuncher",
+                       "coons", "cooter", "coprolagnia", "coprophilia", "cornhole", "creampie", "crotte", "cum", "cumbubble",
+                       "cumdumpster", "cumguzzler", "cumjockey", "cummer", "cumming", "cums", "cumshot", "cumslut", "cumtart",
+                       "cunilingus", "cunillingus", "cunnie", "cunnilingus", "cunt", "cuntass", "cuntface", "cunthole", 
+                       "cuntlick", "cuntlicker", "cuntlicking", "cuntrag", "cunts", "cuntslut", "cyalis", "cyberfuc",
+                       "cyberfuck", "cyberfucked", "cyberfucker", "cyberfuckers", "cyberfucking", "d1ck", "darkie", 
+                       "date rape", "daterape", "deep throat", "deepthroat", "deggo", "dendrophilia", "dick", "dickbag", 
+                       "dickbeaters", "dickface", "dickfuck", "dickfucker", "dickhead", "dickhole", "dickjuice", "dickmilk ", 
+                       "dickmonger", "dicks", "dickslap", "dicksucker", "dicksucking", "dicktickler", "dickwad", 
+                       "dickweasel", "dickweed", "dickwod", "dike", "dildo", "dildos", "doggystyle ","donkeyribber", 
+                       "doochbag", "double dong", "penetration", "doublelift", "douche", "douchebag", "dumbass",
+                       "dumbcunt", "dumbfuck", "ejaculate", "ejaculated", "ejaculates", "ejaculating", "ejaculatings",
+                       "ejaculation", "ejakulate", "erotism", "eunuch", "f u c k", "fagfucker", "fagging", "faggit",
+                       "faggitt", "faggot", "faggotcock", "fanny", "fannyflaps", "fannyfucker", "fanyy", "fatass", "fuck", 
+                       "fucker", "fucking", "fecal", "feck", "fecker", "felch", "felching", "fellate", "fellatio", "feltch",
+                       "female squirting", "femdom", "figging", "fingerbang", "fingerfuck", "fingerfucked", "fingerfucker",
+                       "fingerfuckers", "fingerfucking", "fingerfucks", "fingering", "fistfuck", "fistfucked", "fistfucker", 
+                       "fistfuckers", "fistfucking", "fistfuckings", "fistfucks", "fisting", 
+                       "footjob", "frotting", "fuckass", "fuckbag", "fuckboy", "fuckbrain", "fuckbutt",
+                       "fuckersucker", "fuckface", "fuckhead", "fuckheads", "fuckhole", "fuckin", "fucking",
+                       "fuckings", "fucking shit motherfucker", "fuckme", "fucknut", "fucknutt", "fuckoff", "fucks",
+                       "fuckstick", "fucktard", "fucktards", "fucktart", "fucktwat", "fuckup", "fuckwad", "fuckwhit", 
+                       "fuckwit", "fuckwitt", "fudge packer", "fudgepacker", "fuk", "fuker", "fukker", "fukkin", "fuks", 
+                       "fukwhit", "fukwit", "futanari", "fux", "fuxor", "g-spot", "gangbang", "gangbanged", "gangbangs",
+                       "gayass", "gaybob", "gaydo", "gayfuck", "gayfuckist", "goregasm", "handjob", "hard core", "hardcore",
+                       "hardcoresex", "hooker", "arse", "ass fuck", "ass hole", "assfucker", "asshole", "assshole", "bastard",
+                       "fucking bitch", "cock", "bloody hell", "boong", "cockfucker", "cocksuck", "coon", "cyberfuck",
+                       "erection", "erotic", "faggot fuck", "fuck off", "fuck you", "fuckass", "fuckhole", "hardcore", 
+                       "lesbian", "lesbians", "motherfuck", "negro", "nigger", "orgasim", "orgasm", "penis", "penisfucker", 
+                       "piss", "piss off", "pussy", "sexy shit", "sexy slut", "son of a bitch", "suck tits", "xxx", 
+                       "kill yourself", "fuck yourself", "beheading", "terrorist"]
+    
+    # Check for and filter out bad words from the text
+    for word in cyberbullying_words:
+        if word.lower() in text:
+            filtered_text = filtered_text.replace(word, '*' * len(word))
+            bad_words.append(word)
+    
+    # Map the prediction to a human-readable label
+   
+    return prediction[0], filtered_text, bad_words
+
+# Create a Streamlit app
+st.title("Cyberbullying Detection App (English)")
+
+# Add a text input field
+user_input = st.text_area("Enter text:", "")
+
+# Predict when a button is clicked
+if st.button("Predict"):
+    if user_input:
+        prediction, filtered_text, bad_words = predict_cyberbullying_and_filter_bad_words(user_input)
+        if prediction != "not_cyberbullying":
+            st.write("Prediction: Cyberbullying")
+            st.write(f"Cyberbullying Type: {prediction}")
+        else:
+            st.write("Prediction: Not Cyberbullying")
+        if bad_words:
+            st.write(f"Bad Words: {', '.join(bad_words)}")
+        else:
+            st.write("<span style='color:cyan;'>No bad words found.</span>", unsafe_allow_html=True)
+        if 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"{filtered_text}", unsafe_allow_html=True)
+
+
+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.")
+

requirements.txt

@@ -0,0 +1 @@
+scikit-learn

tfidf_vectorizer.sav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4348f9a8bf9f6296ccebee2a79efca27ff06d2ff76f48e8c587000c17143a71e
+size 1047315

trained_model.sav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ebdf45a74171d8a3dc272d2d21b3022979576f601f20ad5ec05445887935999
+size 1440050