| import streamlit as st |
| import pandas as pd |
| import gensim |
| import joblib |
| import numpy as np |
| import tensorflow as tf |
| from tensorflow.keras.models import Sequential, Model |
| from tensorflow.keras.layers import Embedding, TextVectorization, GlobalAveragePooling1D, Input, LSTM, GRU, Dropout, Dense |
|
|
| import nltk |
| import func |
| nltk.download('wordnet') |
| nltk.download('punkt') |
| from func import TextProcess, Label, TextProcess2 |
|
|
| lda_model_inf = gensim.models.LdaModel.load('lda.model') |
|
|
| model = tf.keras.models.load_model('model_imp') |
|
|
| |
| |
|
|
| with open('dictionary.pkl', 'rb') as file_4: |
| dictionary_inf = joblib.load(file_4) |
|
|
| def run(): |
| |
| st.title('Negative Review clustering insights') |
| |
|
|
| |
| with st.form(key='form_parameters'): |
| text_input = st.text_input('Reviews', 'Barangnya tidak sesuai gambar') |
| submitted_text = st.form_submit_button('Show topic') |
|
|
| data_inf = { |
| 'text': text_input |
| } |
|
|
|
|
| data_inf = pd.DataFrame([data_inf]) |
| |
| if submitted_text: |
| data_inf['text_processed'] = data_inf['text'].apply(lambda x: TextProcess(x)) |
| Message_body_inf = data_inf['text_processed'] |
|
|
| data_inf['text_processed_2'] = data_inf['text'].apply(lambda x: TextProcess2(x)) |
| Message_body_inf_2 = data_inf['text_processed_2'] |
|
|
| y_pred_inf = model.predict(Message_body_inf_2) |
| y_pred_inf = np.where(y_pred_inf >= 0.5, 1, 0) |
|
|
| if y_pred_inf == 0: |
| st.write('Negative sentiment') |
| for i in range(len(Message_body_inf)): |
| bow_vector = dictionary_inf.doc2bow(Message_body_inf[i]) |
| for index, score in sorted(lda_model_inf[bow_vector], key=lambda tup: -1*tup[1]): |
| |
| st.write(f"Complaint: {Label(index)}") |
| |
| break |
| st.write('------------------------------------') |
| else: |
| st.write('Positive sentiment') |
|
|
|
|
| |
|
|
| if __name__ == '__main__': |
| run() |
|
|
