Update app.py

app.py

@@ -3,7 +3,7 @@ from transformers import pipeline
 import streamlit as st
 import matplotlib.pyplot as plt
 import pandas as pd
-# import plotly.express as px
+from spider_plot import spider_plot
 
 
 # @st.cache
@@ -19,19 +19,24 @@ 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),)
-hebEMO_df = HebEMO_model.hebemo(sent, plot=False)
+
+hebEMO_df = HebEMO_model.hebemo(sent, read_lines=True plot=False)
 hebEMO = pd.DataFrame()
 for emo in hebEMO_df.columns[1::2]:
     hebEMO[emo] = abs(hebEMO_df[emo]-(1-hebEMO_df['confidence_'+emo]))
 
+st.write (hebEMO)
 
+plot= st.checkbox('Plot?')
+if plot:
+    ax = spider_plot(hebEMO)
+    st.pyplot(ax)
 # 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)