added instruction

app.py

@@ -94,7 +94,9 @@ adduct_input = st.text_input("Enter adduct:", value=st.session_state.adduct)
 st.subheader("Precursor mz")
 precursor_input = st.text_input("Enter precursor mz:", value=st.session_state.precursor_mz)
 
-# --- Run button toggles flag ---
+# ------------------------
+# Run model
+# ------------------------
 if st.button("Run"):
 
     for f in FIELDS:
@@ -132,18 +134,20 @@ if st.button("Run"):
             mass_diff_thresh=20,
             precursor_intensity=1.1
         )
-    st.write(st.session_state.sim_norm.shape)
+
     st.session_state.selected_spectrum_idx = None
     st.session_state.selected_node_idx = None
     st.session_state.run_clicked = True
 
-# --- Main results ---
+# ------------------------
+# Display visualization
+# ------------------------
 if st.session_state.run_clicked:
     st.subheader("Peak-to-Node Similarity")
+    st.text("Only annotated peaks are shown. Peaks assigned the same subformula are combined by summing all the intensities and the smallest m/z value is shown.")
     st.text("Double click on a peak or node to visualize similarity scores")
 
-    fig = st.session_state.fig  # get the figure
-    # Apply any coloring before rendering
+    fig = st.session_state.fig  
     if st.session_state.selected_spectrum_idx is not None:
         idx = st.session_state.selected_spectrum_idx
         scores = st.session_state.sim_norm[idx, :]
@@ -159,8 +163,7 @@ if st.session_state.run_clicked:
             "red" if i == idx else "lightgray" for i in range(st.session_state.sim_norm.shape[1])
         ]
 
-    # Render figure once with plotly_events
-    
+    # Render figure
     selected = plotly_events(
         st.session_state.fig,
         click_event=True,