Update app.py

app.py

@@ -2,6 +2,7 @@ from HebEMO import HebEMO
 from transformers import pipeline
 import streamlit as st
 import matplotlib.pyplot as plt
+import plotly.express as px
 
 
 HebEMO_model = HebEMO()
@@ -16,9 +17,19 @@ st.title("Find sentiment")
 st.write("HebEMO is a tool to detect polarity and extract emotions from Hebrew user-generated content (UGC), which was trained on a unique Covid-19 related dataset that we collected and annotated. HebEMO yielded a high performance of weighted average F1-score = 0.96 for polarity classification. Emotion detection reached an F1-score of 0.78-0.97, with the exception of *surprise*, which the model failed to capture (F1 = 0.41). These results are better than the best-reported performance, even when compared to the English language.")
 sent = st.text_area("Text", "write here", height = 20)
 # interact(HebEMO_model.hebemo, text='החיים יפים ומאושרי', plot=fixed(True), input_path=fixed(False), save_results=fixed(False),)
-res, fig = HebEMO_model.hebemo(sent, plot=True)
-st.write (res)
-st.pyplot(fig=fig)
+hebEMO_df = HebEMO_model.hebemo(sent, plot=False)
+hebEMO = pd.DataFrame()
+for emo in hebEMO_df.columns[1::2]:
+    hebEMO[emo] = abs(hebEMO_df[emo]-(1-hebEMO_df['confidence_'+emo]))
+
+
+fig = px.bar_polar(hebEMO.melt(), r="value", theta="variable",
+                       color="variable", 
+                       template="ggplot2",
+                      )
+
+st.plotly_chart(fig, use_container_width=True)
+st.write (hebEMO)