Update app.py

app.py

@@ -1,15 +1,21 @@
+from HebEMO import HebEMO
 from transformers import pipeline
 import streamlit as st
 
+
+HebEMO_model = HebEMO()
+
+
 x = st.slider("Select a value")
 st.write(x, "squared is", x * x)
 
 #@st.cache
-sentiment = pipeline(
-  "sentiment-analysis",
-    model="avichr/heBERT_sentiment_analysis",
-)
+
 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)
 st.write (sentiment(sent))
+interact(HebEMO_model.hebemo, text=sent, plot=fixed(True), input_path=fixed(False), save_results=fixed(False),)
+interact(HebEMO_model.hebemo, text='החיים יפים ומאושרי', plot=fixed(True), input_path=fixed(False), save_results=fixed(False),)
+st.write (HebEMO_model.hebemo(sent, plot=True))
+