main page

main.py

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+import streamlit as st
+import torch
+import time
+from models.model_rnn import func
+from models.model_ml import final
+from models.LSTM_model import data_preprocessing, padding, preprocess_single_string, sentimentLSTM, load_model, predict_sentiment
+from models.bert_func import load_bert_lr_model, prediction
+import numpy as np
+
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+txt_label = 'Enter review text'
+txt = st.text_area(label=txt_label, height=200)
+    
+with st.form('button'):
+    button_click = st.form_submit_button("Get result")    
+
+col1, col2, col3, col4 = st.columns(4)
+
+with col1: # ML
+    st.write('ML model')
+    if button_click:
+        t = time.process_time()
+        output = final(txt)
+        elapsed_time = time.process_time() - t
+        st.write('`Negative review`' if np.around(output, 0) == 0 else '`Positive review`')
+        st.write('`Time elapsed :`', round(elapsed_time, 3))
+
+with col2: # RNN
+    st.write('RNN model')
+    if button_click:
+        t = time.process_time()
+        output = func(txt)
+        elapsed_time = time.process_time() - t
+        st.write('`Negative review`' if np.around(output, 0) == 0 else '`Positive review`')
+        st.write('`Time elapsed :`', round(elapsed_time, 3))
+
+with col3: # LSTM
+    st.write('LSTM model')
+    if button_click:
+        st.write(f'`{predict_sentiment(txt)}`')
+        t = time.process_time()
+        elapsed_time = time.process_time() - t
+        st.write('`Time elapsed :`', round(elapsed_time, 3))
+        
+    
+model, tokenizer, lr = load_bert_lr_model('models/bert_lr.joblib')
+
+with col4: # BERT
+    st.write('BERT')
+    if button_click:
+        t = time.process_time()
+        st.write(f'`{prediction(txt, model, tokenizer, lr)}`')
+        elapsed_time = time.process_time() - t
+        st.write('`Time elapsed :`', round(elapsed_time, 3))