import pandas as pd
import re
import nltk
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
from nltk.tokenize import RegexpTokenizer
import nltk
nltk.download('wordnet')
nltk.download('stopwords')
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
nltk.download('punkt')
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split
import xgboost as xgb
from PIL import Image

import warnings
warnings.filterwarnings('ignore')


data = pd.read_csv('chatgpt_reviews.csv')

data['complete_review'] = data['title'] +' .'+data['review']
data = data.drop(['date','review','title'],axis='columns')

def preprocess_data(text):
    emoji_pattern = re.compile("["
                               u"\U0001F600-\U0001F64F"
                               u"\U0001F300-\U0001F5FF"
                               u"\U0001F680-\U0001F6FF"
                               u"\U0001F1E0-\U0001F1FF"
                               u"\U00002702-\U000027B0"
                               u"\U000024C2-\U0001F251"
                               "]+", flags=re.UNICODE)
    special_char_removal = re.compile(r'[^a-z\d\s]+', re.IGNORECASE)
    text = text.lower()
    text = emoji_pattern.sub('', text)
    text = special_char_removal.sub('', text)
    return text
data['complete_review'] = data['complete_review'].apply(lambda x: preprocess_data(x))

stop = stopwords.words('english')
data['complete_review'] = data['complete_review'].apply(lambda x: " ".join(x for x in x.split() if x not in stop))

lemmatizer = WordNetLemmatizer()

def lemmatize_text(text):
    tokens = word_tokenize(text)  # Tokenisasi teks menjadi kata-kata
    lemmatized_tokens = [lemmatizer.lemmatize(token) for token in tokens]  # Melakukan lemmatization pada setiap token
    lemmatized_text = ' '.join(lemmatized_tokens)  # Menggabungkan kembali kata-kata menjadi teks
    return lemmatized_text

data['complete_review']= data['complete_review'].apply(lemmatize_text)

words_to_remove = ['chatgpt','app','chatgpts','chat','gpt','iphone','ipad','gpt4','phone','number','ai','use','io']
data['complete_review'] = data['complete_review'].apply(lambda x: " ".join(x for x in x.split() if x not in words_to_remove))

data['sentiment'] = data['rating'].apply(lambda rating: 2 if rating > 3 else (1 if rating == 3 else 0))

data['sentiment'].value_counts(normalize=True).mul(100).round(2)

stopword = set(stopwords.words('english'))


#Pre-Prcoessing and Bag of Word Vectorization using Count Vectorizer
token = RegexpTokenizer(r'[a-zA-Z0-9]+')
cv = CountVectorizer(stop_words='english',ngram_range = (1,1),tokenizer = token.tokenize)
X = cv.fit_transform(data['complete_review'])
y = data['sentiment']


smote = SMOTE()
X_oversampled, y_oversampled = smote.fit_resample(X, y)



X_train, X_test, y_train, y_test = train_test_split(X_oversampled,
                                                  y_oversampled,
                                                  test_size=0.15,
                                                  random_state=17,stratify=y_oversampled)


dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)


params = {
    'objective': 'multi:softmax',
    'num_class': 3,
    'eval_metric': 'merror',
    'eta': 0.4,
    'max_depth': 6,
    'subsample': 0.8,
    'colsample_bytree': 0.8,
    'seed': 42
}

num_rounds = 100
model = xgb.train(params, dtrain, num_rounds)

preds = model.predict(dtest)
pred_labels = [int(pred) for pred in preds]

def predict(kata):
    preprocessed_kata = preprocess_data(kata)
    cv_fit = cv.fit(data['complete_review'])
    X_pred = cv_fit.transform(pd.Series([preprocessed_kata]))
    dmatrix = xgb.DMatrix(X_pred)
    preds = model.predict(dmatrix)
    return preds[0]