import pandas as pd
import pandas as pd
import re
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer

# Download necessary NLTK resources
# nltk.download('stopwords')
# nltk.download('punkt')
# nltk.download('wordnet')

# Read the CSV file
file_path = '/home/darth/#/SEQuestionClassifier/data/all_combined_data.csv'
df = pd.read_csv(file_path)

import ast
df["Tags"] = df["Tags"].apply(ast.literal_eval)

lemmatizer = WordNetLemmatizer()
stop_words = set(stopwords.words('english'))

def preprocess_text(text):
    """Function to clean text and perform lemitisation"""
    text = text.lower()
    text = re.sub(r'[^\w\s]', '', text)
    words = word_tokenize(text)
    words = [lemmatizer.lemmatize(word) for word in words if word not in stop_words]
    return " ".join(words)


from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MultiLabelBinarizer

def vectorirse_text(text):
    """ Recieves text as input and returns TF-IDF vectors"""
    text = text.apply(preprocess_text)
    tfidf = TfidfVectorizer(max_features=500000)
    X = tfidf.fit_transform(text)
    return X

def label_encoding(input):
    mlb = MultiLabelBinarizer()
    return mlb.fit_transform(input)


X = vectorirse_text(df['Input'])
y = label_encoding(df['Tags'])