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"""
@author : Sakshi Tatak
"""
# Imports
import pandas as pd
import streamlit as st
from predict_flair import SentimentClassifier as FlairSentimentClassifier
from predict_ml import predict as predict_ml
from predict_setfit import SentimentClassifier as SetFitSentimentClassifier
from predict_spacy import SentimentClassifier as SpacySentimentClassifier
st.set_page_config(layout = 'wide')
st.title('SetFit, Flair, SpaCy, Naive Bayes Sentiment Classifiers')
if 'flair_model' not in st.session_state:
st.session_state['flair_model'] = None
if 'spacy_model' not in st.session_state:
st.session_state['spacy_model'] = None
if 'setfit_model' not in st.session_state:
st.session_state['setfit_model'] = None
if 'results' not in st.session_state:
st.session_state['results'] = pd.DataFrame(columns = ['model', 'query', 'sentiment', 'confidence'])
def main():
model_name = st.selectbox('Select Model', options = ['SetFit', 'Naive Bayes', 'Flair', 'SpaCy'])
if model_name == 'SetFit':
if st.session_state.setfit_model is None:
with st.spinner('Loading SetFit classifier ...'):
st.session_state.setfit_model = SetFitSentimentClassifier()
st.success('SetFit classifier loaded successfully!')
model = st.session_state.setfit_model
if model_name == 'Flair':
if st.session_state.flair_model is None:
with st.spinner('Loading Flair classifier ...'):
st.session_state.flair_model = FlairSentimentClassifier()
st.success('Flair classifier loaded successfully!')
model = st.session_state.flair_model
if model_name == 'SpaCy':
if st.session_state.spacy_model is None:
with st.spinner('Loading SpaCy classifier'):
st.session_state.spacy_model = SpacySentimentClassifier()
st.success('Spacy classifier loaded successfully!')
model = st.session_state.spacy_model
text = st.text_area('Input text', value = 'This is insane haha!')
if st.button('Compute sentiment'):
if model_name != 'Naive Bayes':
with st.spinner(f'Predicting with {model_name} ...'):
sentiment, conf = model.predict(text)
else:
with st.spinner('Predicting with Naive Bayes ...'):
sentiment, conf = predict_ml(text)
st.success(sentiment + ', ' + str(conf))
df = st.session_state.results
df.loc[len(df)] = [model_name, text, sentiment, conf]
st.table(df)
if __name__ == '__main__':
main()
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