first commit

app.py

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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()

requirements.txt

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+streamlit
+altair
+pandas 
+numpy
+joblib
+transformers