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| # Library Streamlit | |
| import streamlit as st | |
| # Library Load Model | |
| import pandas as pd | |
| import numpy as np | |
| from tensorflow.keras.models import load_model | |
| # Library Pre-Processing | |
| from nltk.stem import WordNetLemmatizer | |
| import nltk | |
| import re | |
| from nltk.corpus import stopwords | |
| from nltk.tokenize import word_tokenize | |
| nltk.download('popular') | |
| def run() : | |
| # Load Model | |
| model_lstm = load_model('best_model') | |
| # Membuat Title | |
| st.markdown("<h1 style='text-align: center;'>Cyberbullying Tweet Prediction</h1>", unsafe_allow_html=True) | |
| # Menambahkan Deskripsi Form | |
| st.write('Page ini berisi model untuk memprediksi jenis Cyberbullying pada tweet') | |
| with st.form(key= 'form_tweet'): | |
| st.markdown('### **Tweet**') | |
| tweet_text = st.text_input('',value= '') | |
| submitted = st.form_submit_button('Predict') | |
| # Additional Stopwords | |
| additional_stopwords = ['rt', 'mkr', 'didn', 'bc', 'n', 'm', | |
| 'im', 'll', 'y', 've', 'u', 'ur', 'don', | |
| 'p', 't', 's', 'aren', 'kp', 'o', 'kat', | |
| 'de', 're', 'amp', 'will', 'wa', 'e', 'like', 'andre', 'na', 're', 'lil', 'd', 'na', 'pete', 'annie', 'nikki', 'lmao', 'miley', 'wan', 'gon'] | |
| # Setting stopwords english | |
| stpwds_eng = list(set(stopwords.words('english'))) | |
| for i in additional_stopwords: | |
| stpwds_eng.append(i) | |
| # Membuat Fungsi Pre-Processing Text | |
| cleaning_pattern = "@\S+|https?:\S+|http?:\S|[^A-Za-z0-9]+" | |
| lemmatizer = WordNetLemmatizer() | |
| def text_proses(teks): | |
| # Mengubah Teks ke Lowercase | |
| teks = teks.lower() | |
| # Menghilangkan Link | |
| teks = re.sub(cleaning_pattern, ' ', teks) | |
| # Menghilangkan Mention | |
| teks = re.sub("@[A-Za-z0-9_]+", " ", teks) | |
| # Menghilangkan Hashtag | |
| teks = re.sub("#[A-Za-z0-9_]+", " ", teks) | |
| # Menghilangkan \n | |
| teks = re.sub(r"\\n", " ",teks) | |
| # Menghilangkan kata dibawah 3 char | |
| teks = re.sub(r'\b\w{1,3}\b', " ",teks) | |
| # Menghilangkan Whitespace | |
| teks = teks.strip() | |
| # Menghilangkan yang Bukan Huruf seperti Emoji, Gamma dll | |
| teks = re.sub("[^A-Za-z\s']", " ", teks) | |
| # Menghilangkan double space | |
| teks = re.sub("\s\s+" , " ", teks) | |
| # Melakukan Tokenisasi | |
| tokens = word_tokenize(teks) | |
| # Menghilangkan Stopwords | |
| teks = ' '.join([word for word in tokens if word not in stpwds_eng]) | |
| # Melakukan Lemmatizer | |
| teks = lemmatizer.lemmatize(teks) | |
| return teks | |
| # Membuat Dataframe | |
| data_inf = { | |
| 'tweet_text' : tweet_text | |
| } | |
| data_inf = pd.DataFrame([data_inf]) | |
| if submitted : | |
| # Preprocessing Data Inference | |
| data_inf['tweet_processed'] = data_inf['tweet_text'].apply(lambda x: text_proses(x)) | |
| # Prediksi jenis tweet | |
| y_inf_pred = np.argmax(model_lstm.predict(data_inf['tweet_processed']), axis=-1) | |
| # Membuat fungsi untuk return result prediksi | |
| if y_inf_pred[0] == 0: | |
| result = 'age' | |
| elif y_inf_pred[0] == 1: | |
| result = 'ethnicity' | |
| elif y_inf_pred[0] == 2: | |
| result = 'gender' | |
| elif y_inf_pred[0] == 3: | |
| result = 'not_cyberbullying' | |
| elif y_inf_pred[0] == 4: | |
| result = 'other_cyberbullying' | |
| else: | |
| result = 'religion' | |
| st.write('# Cyberbullying Prediction : ', result) | |
| if __name__ == '__main__': | |
| run() |