import streamlit as st from datetime import datetime from modules.prediction import prepare, predict STATUS_STOPPED = 120001 STATUS_SUBMIT = 120002 STATUS_ERROR = 120003 has_prepared = False st.session_state['running_status'] = STATUS_STOPPED if not has_prepared: print('>>> [PREPARE] Preparing...') prepare() has_prepared = True st.markdown("""

Entity Referring Classifier

Ver 2.0.1208.01
It knows exactly when you are calling it.

""", unsafe_allow_html=True) livedemo_col1, livedemo_col2, livedemo_col3 = st.columns([12,1,6]) with livedemo_col1: st.subheader('Live Demo') with st.form("my_form"): entity = st.text_input('Entity Name:', 'Jimmy') sentence = st.text_input('Sentence Input:', 'Are you feeling good, Jimmy?', help='The classifier is going to analyze this sentence.') if st.form_submit_button('🚀 Submit'): if entity.lower() not in sentence.lower(): st.session_state['running_status'] = STATUS_ERROR else: st.session_state['running_status'] = STATUS_SUBMIT if st.session_state['running_status'] == STATUS_STOPPED: st.info('Type something and submit to start!') elif st.session_state['running_status'] == STATUS_SUBMIT: if predict(sentence, entity) == 'CALLING': st.success('It is a **calling**!') else: st.success('It is a **mentioning**!') st.caption(f'Submitted: `{sentence.lower()}` by `{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}`') elif st.session_state['running_status'] == STATUS_ERROR: st.warning('The entity name is not in the sentence!') with livedemo_col2: st.empty() with livedemo_col3: st.markdown(""" #### Get Started """) st.markdown(""" Hi! I'm the Entity Referring Classifier. I will fill this part later. """) st.markdown(""" #### Terms """) st.markdown(""" ##### `Calling` """) st.markdown(""" ##### `Mentioning` """)