Update src/app.py

src/app.py

@@ -283,18 +283,19 @@ if model:
         st.markdown(colored_text_html, unsafe_allow_html=True)
         st.markdown("---")
 
-        '''
-        st.subheader("💡 Interactive Attention")
-        st.markdown("Hover over any word to highlight other words it pays strong attention to.")
-        start, end = st.session_state.start_index, st.session_state.end_index
-        user_tokens, user_attention_matrix = st.session_state.full_tokens[start:end], st.session_state.attention_matrix
-        max_attention = float(np.max(user_attention_matrix)) if user_attention_matrix.size > 0 else 0.1
-        default_slider_val = min(0.1, max_attention) if max_attention > 0 else 0.1
-        attention_threshold = st.slider("Attention Threshold", 0.0, max_attention, default_slider_val, 0.01, "%.2f")
-        interactive_html = render_interactive_text(user_tokens, user_attention_matrix, start, attention_threshold)
-        components.html(interactive_html, height=200, scrolling=True)
-        st.markdown("---")
-        '''
+
+        # Attention analysis 
+        # st.subheader("💡 Interactive Attention")
+        # st.markdown("Hover over any word to highlight other words it pays strong attention to.")
+        # start, end = st.session_state.start_index, st.session_state.end_index
+        # user_tokens, user_attention_matrix = st.session_state.full_tokens[start:end], st.session_state.attention_matrix
+        # max_attention = float(np.max(user_attention_matrix)) if user_attention_matrix.size > 0 else 0.1
+        # default_slider_val = min(0.1, max_attention) if max_attention > 0 else 0.1
+        # attention_threshold = st.slider("Attention Threshold", 0.0, max_attention, default_slider_val, 0.01, "%.2f")
+        # interactive_html = render_interactive_text(user_tokens, user_attention_matrix, start, attention_threshold)
+        # components.html(interactive_html, height=200, scrolling=True)
+        # st.markdown("---")
+        
         if st.session_state.suspicious_phrases:
             st.warning("High-perplexity phrases identified:")
             for phrase in st.session_state.suspicious_phrases: st.markdown(f"- *{filter_token(phrase)}*")