Update app.py

app.py

@@ -2,16 +2,17 @@ from HebEMO import HebEMO
 from transformers import pipeline
 import streamlit as st
 import matplotlib.pyplot as plt
-import plotly.express as px
+# import plotly.express as px
 
 
+@st.cache
 HebEMO_model = HebEMO()
 
 
 x = st.slider("Select a value")
 st.write(x, "squared is", x * x)
 
-#@st.cache
+
 
 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.")
@@ -23,12 +24,12 @@ 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",
-                      )
+# fig = px.bar_polar(hebEMO.melt(), r="value", theta="variable",
+#                       color="variable", 
+#                       template="ggplot2",
+#                      )
 
-st.plotly_chart(fig, use_container_width=True)
+# st.plotly_chart(fig, use_container_width=True)
 st.write (hebEMO)