Update colab_app.py

colab_app.py

@@ -8,12 +8,12 @@ from spider_plot import spider_plot
 
 @st.cache
 def HebEMO_model():
-    st.title("Emotion Recognition in Hebrew Texts")
-      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). More information can be found in our git: https://github.com/avichaychriqui/HeBERT")
-      st.write("Write Hebrew sentences in the text box below to analyze (each sentence in a different rew). It takes a while, be patient :). An additional demo can be found in the Colab notebook: https://colab.research.google.com/drive/1Jw3gOWjwVMcZslu-ttXoNeD17lms1-ff ")
-    
-    return HebEMO()
+  st.title("Emotion Recognition in Hebrew Texts")
+  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). More information can be found in our git: https://github.com/avichaychriqui/HeBERT")
+  st.write("Write Hebrew sentences in the text box below to analyze (each sentence in a different rew). It takes a while, be patient :). An additional demo can be found in the Colab notebook: https://colab.research.google.com/drive/1Jw3gOWjwVMcZslu-ttXoNeD17lms1-ff ")
   
+  return HebEMO()
+
 HebEMO_model = HebEMO_model()