Update src/streamlit_app.py

src/streamlit_app.py

@@ -11,7 +11,6 @@ st.set_page_config(page_title="Brake Performance Lab", layout="wide", page_icon=
 def load_data():
     current_dir = os.path.dirname(__file__)
     file_path = os.path.join(current_dir, "Brake_Lab_Test_Data.xlsx")
-    # Reading 'Data' sheet as specified
     data = pd.read_excel(file_path, sheet_name='Data')
     data.columns = data.columns.str.strip()
     return data
@@ -19,116 +18,117 @@ def load_data():
 try:
     df = load_data()
 
-    # --- SIDEBAR (ADVANCED FILTERS) ---
+    # --- SIDEBAR ---
     with st.sidebar:
-        # Decathlon International Logo
         st.image("https://upload.wikimedia.org/wikipedia/commons/thumb/0/08/Decathlon_Logo.svg/1280px-Decathlon_Logo.svg.png", width=200)
         st.title("⚙️ Settings")
-        
-        # 1. Lever Effort Input
-        x_input = st.slider("🫱 Lever Effort (N)", 40, 200, 100, help="Force applied to the brake lever in Newtons")
+        x_input = st.slider("🫱 Lever Effort (N)", 40, 200, 100)
         
         st.markdown("---")
-        st.subheader("🔍 Graph Filters")
+        st.subheader("🔍 Display Options")
+        show_loss = st.checkbox("Show Efficiency Loss (Wet vs Dry)", value=True)
+        enable_comparison = st.checkbox("Enable Comparison Mode (Model vs Model)", value=False)
         
-        # 2. Model Selection
         all_models = df['model name'].unique().tolist()
-        selected_models = st.multiselect(
-            "Select Models",
-            options=all_models,
-            default=all_models[:2] if len(all_models) > 1 else all_models
-        )
-        
-        # 3. Condition Selection
-        condition_view = st.radio(
-            "Conditions to display",
-            ["Both", "Dry only", "Wet only"],
-            index=0
-        )
+        selected_models = st.multiselect("Select Models", options=all_models, default=all_models[:2])
+        condition_view = st.radio("Conditions to display", ["Both", "Dry only", "Wet only"], index=0)
 
-    # --- DIAGNOSTIC LOGIC ---
+    # --- DIAGNOSTIC HEADER ---
     if x_input < 70:
-        label, color_alert = "❄️ LIGHT BRAKING", "#a1c4fd" # Light Blue
+        label, color_alert = "❄️ LIGHT BRAKING", "#a1c4fd"
     elif 70 <= x_input <= 110:
-        label, color_alert = "⚖️ MODERATE BRAKING", "#ffdb58" # Yellow
+        label, color_alert = "⚖️ MODERATE BRAKING", "#ffdb58"
     else:
-        label, color_alert = "🔥 POWERFUL BRAKING", "#ff4b4b" # Red
+        label, color_alert = "🔥 POWERFUL BRAKING", "#ff4b4b"
 
-    # Diagnostic Header
     st.markdown(f"""
         <div style="background-color:{color_alert}; padding:15px; border-radius:10px; text-align:center; border: 1px solid #ddd;">
             <h2 style="color:black; margin:0;">{label}</h2>
-            <p style="color:black; font-weight:bold; margin:5px 0 0 0;">Current Lever Effort: {x_input} N</p>
+            <p style="color:black; font-weight:bold; margin:5px 0 0 0;">Lever Effort: {round(float(x_input), 1)} N</p>
         </div>
     """, unsafe_allow_html=True)
 
-    # --- PLOT AREA ---
+    # --- MAIN AREA ---
     col1, col2 = st.columns([3, 1])
 
     with col1:
         filtered_df = df[df['model name'].isin(selected_models)]
-        
         fig = go.Figure()
         x_range = np.linspace(40, 200, 100)
-        # Decathlon-friendly color palette
-        colors = ['#0082C3', '#E63312', '#333333', '#00A14B', '#FFD200', '#AB63FA']
+        colors = ['#0082C3', '#E63312', '#333333', '#00A14B', '#FFD200']
+
+        comparison_results = []
 
         for i, (index, row) in enumerate(filtered_df.iterrows()):
             color = colors[i % len(colors)]
+            y_dry_val = row['dry a'] * x_input + row['dry b']
+            y_wet_val = row['wet a'] * x_input + row['wet b']
             
-            # Linear Regression Calculation: y = ax + b
-            y_dry = row['dry a'] * x_range + row['dry b']
-            y_wet = row['wet a'] * x_range + row['wet b']
+            comparison_results.append({"name": row['model name'], "dry": y_dry_val, "wet": y_wet_val})
+
+            y_plot_dry = row['dry a'] * x_range + row['dry b']
+            y_plot_wet = row['wet a'] * x_range + row['wet b']
+
+            # Custom Hover Template: %{x} is removed from the body and put in the title
+            hover_dry = "<b>" + row['model name'] + " (Dry)</b><br>Perf: %{y:.1f}<extra></extra>"
+            hover_wet = "<b>" + row['model name'] + " (Wet)</b><br>Perf: %{y:.1f}<extra></extra>"
 
-            # DRY Trace
             if condition_view in ["Both", "Dry only"]:
-                fig.add_trace(go.Scatter(x=x_range, y=y_dry, mode='lines', 
+                fig.add_trace(go.Scatter(x=x_range, y=y_plot_dry, mode='lines', 
                                          name=f"{row['model name']} (Dry)", 
                                          line=dict(color=color, width=4),
-                                         hovertemplate="Effort: %{x}N<br>Perf: %{y:.2f}"))
-            
-            # WET Trace
+                                         hovertemplate=hover_dry))
             if condition_view in ["Both", "Wet only"]:
-                fig.add_trace(go.Scatter(x=x_range, y=y_wet, mode='lines', 
+                fig.add_trace(go.Scatter(x=x_range, y=y_plot_wet, mode='lines', 
                                          name=f"{row['model name']} (Wet)", 
                                          line=dict(color=color, width=2, dash='dot'),
-                                         hovertemplate="Effort: %{x}N<br>Perf: %{y:.2f}"))
+                                         hovertemplate=hover_wet))
 
-        # Vertical line for current effort
         fig.add_vline(x=x_input, line_width=3, line_dash="dash", line_color="black")
         
         fig.update_layout(
             xaxis_title="Lever Effort (N)",
-            yaxis_title="Braking Performance",
-            legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
+            yaxis_title="Performance",
             plot_bgcolor='white',
-            hovermode="x unified"
+            hovermode="x unified", # Combine labels at the same X
+            hoverlabel=dict(bgcolor="white", font_size=12)
         )
+        
+        # This part ensures the X value appears only once at the top of the unified hoverbox
+        fig.update_xaxes(showspikes=True, spikecolor="gray", spikesnap="cursor", spikemode="across")
+        
         st.plotly_chart(fig, use_container_width=True)
 
     with col2:
         st.subheader("💡 Analysis")
         if not filtered_df.empty:
-            for index, row in filtered_df.iterrows():
-                val_dry = row['dry a'] * x_input + row['dry b']
-                val_wet = row['wet a'] * x_input + row['wet b']
-                # Drop in efficiency calculation
-                loss = ((val_dry - val_wet) / val_dry) * 100 if val_dry != 0 else 0
-                
-                st.metric(label=row['model name'], 
-                          value=f"{round(val_dry, 2)}", 
-                          delta=f"-{round(loss, 1)}% Wet Loss", 
-                          delta_color="inverse")
-                st.write(f"**Wet Perf:** {round(val_wet, 2)}")
+            max_dry = max([r['dry'] for r in comparison_results])
+            max_wet = max([r['wet'] for r in comparison_results])
+
+            for res in comparison_results:
+                st.write(f"### {res['name']}")
+                dry_val = round(res['dry'], 1)
+                if enable_comparison and len(selected_models) > 1:
+                    diff_dry = round(res['dry'] - max_dry, 1)
+                    suffix = " (Best)" if diff_dry == 0 else f" ({diff_dry} vs Best)"
+                    st.write(f"**Dry:** {dry_val}{suffix}")
+                else:
+                    st.write(f"**Dry:** {dry_val}")
+
+                wet_val = round(res['wet'], 1)
+                if enable_comparison and len(selected_models) > 1:
+                    diff_wet = round(res['wet'] - max_wet, 1)
+                    suffix = " (Best)" if diff_wet == 0 else f" ({diff_wet} vs Best)"
+                    st.write(f"**Wet:** {wet_val}{suffix}")
+                else:
+                    st.write(f"**Wet:** {wet_val}")
+
+                if show_loss:
+                    loss = ((res['dry'] - res['wet']) / res['dry']) * 100 if res['dry'] != 0 else 0
+                    st.warning(f"Efficiency Loss: -{round(loss, 1)}%")
                 st.markdown("---")
         else:
-            st.info("Please select at least one model in the sidebar.")
-
-    # --- DATA EXPORT ---
-    with st.expander("📂 View Raw Data & Export"):
-        st.dataframe(filtered_df, use_container_width=True)
-        csv = filtered_df.to_csv(index=False).encode('utf-8')
-        st.download_button("📥 Download as CSV", csv, "brake_test_report.csv", "text/csv")
+            st.info("Select models.")
 
 except Exception as e:
-    st.error(f"Technical Error: {e}")
+    st.error(f"Error: {e}")