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backup file.py

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+from nltk.util import pr
+import pandas as pd
+import numpy as np
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+
+data = pd.read_csv("twitter.csv")
+#print(data.head())
+
+data["labels"] = data["class"].map({0: "Hate Speech", 1: "Offensive Language", 2: "No Hate and Offensive"})
+#print(data.head())
+
+data = data[["tweet", "labels"]]
+#print(data.head())
+
+import re
+import nltk
+stemmer = nltk.SnowballStemmer("english")
+from nltk.corpus import stopwords
+import string
+stopword=set(stopwords.words('english'))
+
+def clean(text):
+    text = str(text).lower()
+    text = re.sub('\[.*?\]', '', text)
+    text = re.sub('https?://\S+|www\.\S+', '', text)
+    text = re.sub('<.*?>+', '', text)
+    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
+    text = re.sub('\n', '', text)
+    text = re.sub('\w*\d\w*', '', text)
+    text = [word for word in text.split(' ') if word not in stopword]
+    text=" ".join(text)
+    text = [stemmer.stem(word) for word in text.split(' ')]
+    text=" ".join(text)
+    return text
+data["tweet"] = data["tweet"].apply(clean)
+#print(data.head())
+
+x = np.array(data["tweet"])
+y = np.array(data["labels"])
+
+cv = CountVectorizer()
+X = cv.fit_transform(x) # Fit the Data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
+
+clf = DecisionTreeClassifier()
+clf.fit(X_train,y_train)
+clf.score(X_test,y_test)
+
+def hate_speech_detection():
+    import streamlit as st
+    st.title("Hate Speech Detection")
+    user = st.text_area("Enter any Tweet: ")
+    if len(user) < 1:
+        st.write("  ")
+    else:
+        sample = user
+        data = cv.transform([sample]).toarray()
+        a = clf.predict(data)
+        st.title(a)
+hate_speech_detection()