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
import re
import nltk
stemmer = nltk.SnowballStemmer("english")
from nltk.corpus import stopwords
import string
stopword=set(stopwords.words('english'))
data = pd.read_csv("datasets//twitter.csv")

#Step 2 I will add a new column to this dataset as labels which will contain the values as: Hate Speech O. ffensive Language No Hate and Offensive

data["labels"] = data["class"].map({0: "Hate Speech",
                                    1: "Offensive Language",
                                    2: "No Hate and Offensive"})
data = data[["tweet", "labels"]]


#Step 3 Now I will only select the tweet and labels columns for the rest of the task of training a hate speech detection model:

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)

#Step 5 Now I will create a function to clean the texts in the tweet column


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)

#Step 6 Now let’s split the dataset into training and test sets and train a machine learning model for the task of hate speech detection

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)
#print(data.head())

#Step 7 Now let’s test this machine learning model to see if it detects hate speech or not

sample = "Let's unite and kill all the people who are protesting against the government"
data = cv.transform([sample]).toarray()
print(clf.predict(data))