Update app.py

app.py

@@ -17,46 +17,43 @@ def classify_toxicity(text):
     score = result['score']
     return label, score
 
+
 # Define the Streamlit app
 def app():
     # Create a persistent DataFrame
     if 'results' not in st.session_state:
         st.session_state.results = pd.DataFrame(columns=['text', 'toxicity', 'score'])
-
+    
     # Set page title and favicon
-    st.set_page_config(page_title='Toxicity Classifier', page_icon=':microscope:')
+    st.set_page_config(page_title='Toxicity Classification App', page_icon=':guardsman:')
 
-    # Set app title and description
-    st.title('Toxicity Classifier')
-    st.markdown('This app uses a pre-trained BERT model to classify text into three toxicity levels: **positive**, **neutral**, and **negative**.')
+    # Set app header
+    st.write('# Toxicity Classification App')
+    st.write('Enter some text and the app will classify its toxicity.')
 
     # Create a form for users to enter their text
     with st.form(key='text_form'):
-        text_input = st.text_input(label='Enter your text:', help='Enter some text to classify.')
+        text_input = st.text_input(label='Enter your text:')
         submit_button = st.form_submit_button(label='Classify')
 
     # Classify the text and display the results
     if submit_button and text_input != '':
         label, score = classify_toxicity(text_input)
-
-        # Create a collapsible section for the classification result
-        with st.beta_expander('Classification Result', expanded=True):
-            st.write(f'Text: {text_input}')
-            st.write(f'Toxicity: {label}')
-            st.write(f'Score: {score:.4f}')
+        st.write('## Classification Result')
+        st.write(f'**Text:** {text_input}')
+        st.write(f'**Toxicity:** {label}')
+        st.write(f'**Score:** {score:.2f}')
 
         # Add the classification result to the persistent DataFrame
         st.session_state.results = st.session_state.results.append({'text': text_input, 'toxicity': label, 'score': score}, ignore_index=True)
 
     # Display the persistent DataFrame
-    if not st.session_state.results.empty:
-        st.markdown('## Classification Results')
-        st.dataframe(st.session_state.results.style.highlight_max(subset=['score'], color='lightgreen'))
-
-    # Add a footer with some info and links
-    st.markdown('---')
-    st.markdown('Made with :heart: by ChatGPT')
-    st.markdown('[GitHub](https://github.com/chatgpt/toxicity-classifier) - [OpenAI Blog](https://openai.com/blog/) - [Streamlit Docs](https://docs.streamlit.io/)')
+    st.write('## Classification Results')
+    st.write(st.session_state.results)
+
+    # Display a chart of the classification results
+    chart_data = st.session_state.results.groupby('toxicity').size().reset_index(name='count')
+    chart = st.bar_chart(chart_data.set_index('toxicity'))
 
 if __name__ == '__main__':
     app()