Create colab_app.py

colab_app.py

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+from HebEMO import HebEMO
+from transformers import pipeline
+import streamlit as st
+import matplotlib.pyplot as plt
+import pandas as pd
+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()
+  
+HebEMO_model = HebEMO_model()
+
+
+sent = st.text_area("Text", "החיים יפים ומאושרים", 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, 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)