Update src/app.py

src/app.py

@@ -226,8 +226,8 @@ with tab1:
             key="download_binary_txt"
         )
 
-        # --- Output 2: Grouped Binary Matrix ---
-        st.markdown("### Output 2 – Grouped Binary Matrix")
+        # --- Output 2: Binary matrix split into reactions grouped by target position ---
+        st.markdown("### Output 2 – Binary matrix split into reactions grouped by target position")
         groups = []
         for i in range(0, len(binary_labels), group_size):
             group = binary_labels[i:i + group_size]
@@ -237,6 +237,7 @@ with tab1:
 
         columns = [f"Position {i+1}" for i in range(group_size)]
         df = pd.DataFrame(groups, columns=columns)
+        df.insert(0, "Sample", range(1, len(df) + 1))
         st.dataframe(df, use_container_width=True)
 
         st.download_button(
@@ -320,7 +321,7 @@ with tab3:
     Upload your sample file (Excel, CSV, or TXT) containing binary mutation data.
     The app will:
     - Auto-detect or create `Sample`, `Position#`, `Total edited`, and `Volume per "1"` columns  
-    - Let you set the **Desired total volume per sample (µL)** used to compute `Volume per "1"`  
+    - Let you set the **Maximum volume per input well (µL)** used to compute `Volume per "1"`  
     - Calculate total demand per input and suggest a **uniform layout** (same # consecutive wells per input)  
     - **Preview** the layout on a plate map (with tooltips)  
     - After confirmation, generate pipetting commands and a source volume summary  
@@ -468,16 +469,16 @@ with tab3:
 
             st.markdown("#### ⚙️ Volume Calculation Settings")
             default_total_vol = st.number_input(
-                "Desired total volume per sample (µL)",
+                "Maximum volume per input well (µL)",
                 min_value=1.0, max_value=10000.0, value=64.0, step=1.0,
-                help="Used to compute Volume per '1' as (Desired total volume / Total edited) when not provided."
+                help="Used to compute Volume per '1' as (Maximum volume per input well / Total edited) when not provided."
             )
 
             vol_candidates = [c for c in df.columns if "volume per" in c.lower()]
             if not vol_candidates:
                 df['Volume per "1"'] = default_total_vol / df["Total edited"].replace(0, np.nan)
                 df['Volume per "1"'] = df['Volume per "1"'].fillna(0)
-                st.info(f'`Volume per "1"` column missing — calculated automatically as {default_total_vol:.0f} µL / Total edited.')
+                st.info(f'`Volume per "1"` column missing — calculated automatically as {default_total_vol:.0f} µL (max per input well) / Total edited.')
                 volume_col = 'Volume per "1"'
             else:
                 volume_col = vol_candidates[0]