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# Import packages
import streamlit as st
import altair as alt
import pandas as pd
import numpy as np
import joblib
from transformers import pipeline
# Load pipeline
pipe_lr = joblib.load(open("emotion_detector_pipe_lr.pkl","rb"))
#emoroberta_emotion_classifier = pipeline(
# "text-classification", model="arpanghoshal/EmoRoBERTa", return_all_scores=True
#)
# Emojis
emotions_emoji_dict = {"anger":"😠","disgust":"🤮", "fear":"😨😱", "joy":"🤗", "neutral":"😐", "sadness":"😔", "shame":"😳", "surprise":"😮"}
# Functions
def predict_emotions(text):
result = pipe_lr.predict([text])
return result[0]
def get_prediction_proba(text):
result = pipe_lr.predict_proba([text])
return result
def predict_emotions_and_score_emoroberta(text):
emotionlabel = emoroberta_emotion_classifier(text)
emotion_dataframe = pd.DataFrame.from_records(emotionlabel[0]).sort_values(by=["score"], ascending=False)
emotion = emotion_dataframe.iloc[0, 0]
score = emotion_dataframe.iloc[0, 1]
return emotion_dataframe, emotion, score
def main():
st.title("Emotion Detection App")
with st.form(key='emotion_detection_form'):
raw_text = st.text_area("Type here")
submit_text = st.form_submit_button(label = 'Submit')
st.subheader("Predictions from LR model trained on labeled data (8 emotions)")
if submit_text:
col1,col2 = st.columns(2)
prediction = predict_emotions(raw_text)
prediction_probability = get_prediction_proba(raw_text)
with col1:
st.success("Original Text")
st.write(raw_text)
st.success("Emotion")
emoji_icon = emotions_emoji_dict[prediction]
st.write("{} {}".format(prediction,emoji_icon))
st.success("Emotion Score")
st.write("{:.4f}".format(np.max(prediction_probability)))
with col2:
st.success("Prediction Probability")
proba_df = pd.DataFrame(prediction_probability,columns = pipe_lr.classes_)
proba_df_clean = proba_df.T.reset_index()
proba_df_clean.columns = ["emotions","probability"]
fig = alt.Chart(proba_df_clean).mark_bar().encode(y=alt.Y('emotions', sort='-x'),x='probability',color='emotions')
st.altair_chart(fig,use_container_width=True)
st.markdown("***")
st.subheader("Predictions from EmoRoBERTa - BERT based pre-trained model (28 emotions)")
# col3,col4 = st.columns(2)
# emotion_dataframe_emoroberta, predicted_emotion_emoroberta, prediction_probability_emoroberta = predict_emotions_and_score_emoroberta(raw_text)
# with col3:
# st.success("Emotion")
# st.write(predicted_emotion_emoroberta)
#
# with col4:
# st.success("Emotion Score")
# st.write("{:.4f}".format(np.max(prediction_probability_emoroberta)))
# st.success("Prediction Probability")
# emotion_dataframe_emoroberta.columns = ["emotions","probability"]
# fig = alt.Chart(emotion_dataframe_emoroberta).mark_bar().encode(y=alt.Y('emotions', sort='-x'),x='probability',color='emotions')
# st.altair_chart(fig,use_container_width=True)
if __name__ == '__main__':
main() |