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app.py

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+import pickle
+import streamlit as st
+from string import punctuation
+import nltk
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+from nltk import word_tokenize
+
+nltk.download('stopwords')
+nltk.download('punkt')
+nltk.download('wordnet')
+
+
+model = pickle.load(open('model.pkl', 'rb'))
+vectorizer = pickle.load(open('vectorizer.pkl', 'rb'))
+
+# Defining a function that will clean the text
+def clean_text(text):
+    punc = list(punctuation)
+    stop = stopwords.words('english')
+    bad_tokens = punc + stop
+    tokens = word_tokenize(text)
+    lemma = WordNetLemmatizer()
+    word_tokens = [t for t in tokens if t.isalpha()]
+    clean_tokens = [lemma.lemmatize(t.lower()) for t in word_tokens if t not in bad_tokens]
+    return ' '.join(clean_tokens)
+
+
+# defining the main function and creating the ui
+def main():
+    st.set_page_config(page_title='Hate Speech Detector', page_icon='🤖')
+    st.header("Hate Speech Detection System")
+    text = st.text_area('Enter Text')
+    submit = st.button('Submit')
+    if submit:
+        itext = clean_text(text)
+        vectorized_text = vectorizer.transform([itext])
+        response = model.predict(vectorized_text)[0]
+        if response == 0:
+            st.write('Hate Speech')
+        if response == 1:
+            st.write('Offensive Language')
+        else:
+            st.write('No hate speech or offensive language detected')
+
+if __name__ == "__main__":
+    main()
+

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4fa91712c6e78008a226376f3e1102d3ca526e26c16ee04c5ecf4fa939e57a78
+size 332738

requirements.txt

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+streamlit
+scikit-learn
+nltk

vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:56ae5c2cc4e5b91b24b7d0af7408054f1c257ea11fc446d5a46e9bbbcef2f6e0
+size 408930