Update src/streamlit_app.py

src/streamlit_app.py

@@ -7,6 +7,28 @@ import os
 # Page Configuration
 st.set_page_config(page_title="Brake Performance Lab", layout="wide", page_icon="🚲")
 
+# Style CSS pour réduire la police et compacter l'affichage
+st.markdown("""
+    <style>
+    .small-font {
+        font-size:12px !important;
+        color: black !important;
+    }
+    [data-testid="stMetricValue"] {
+        font-size: 20px !important;
+        color: black !important;
+    }
+    [data-testid="stMetricDelta"] {
+        font-size: 13px !important;
+    }
+    [data-testid="column"] {
+        padding: 5px !important;
+        border: 1px solid #f0f2f6;
+        border-radius: 5px;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
 @st.cache_data
 def load_data():
     current_dir = os.path.dirname(__file__)
@@ -17,23 +39,23 @@ def load_data():
 
 try:
     df = load_data()
+    all_models = df['model name'].unique().tolist()
 
     # --- SIDEBAR ---
     with st.sidebar:
         st.image("https://upload.wikimedia.org/wikipedia/commons/thumb/0/08/Decathlon_Logo.svg/1280px-Decathlon_Logo.svg.png", width=200)
         st.title("⚙️ Settings")
-        x_input = st.slider("🫱 Lever Effort (N)", 40, 200, 100)
-        
+        x_input = st.slider("🫱 Lever Effort [N]", 40, 200, 100)
+        st.markdown("---")
+        with st.expander("🔍 Display Options"):
+            show_loss = st.checkbox("Show Wet Loss (%)", value=True)
+            enable_comparison = st.checkbox("Enable Ref. Comparison", value=False)
+            ref_model = st.selectbox("Reference Model", options=all_models)
+            condition_view = st.radio("Conditions to display", ["Both", "Dry only", "Wet only"], index=0)
         st.markdown("---")
-        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)
-        
-        all_models = df['model name'].unique().tolist()
         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 HEADER ---
+    # --- DIAGNOSTIC HEADER (COMPACT) ---
     if x_input < 70:
         label, color_alert = "❄️ LIGHT BRAKING", "#a1c4fd"
     elif 70 <= x_input <= 110:
@@ -42,93 +64,75 @@ try:
         label, color_alert = "🔥 POWERFUL BRAKING", "#ff4b4b"
 
     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;">Lever Effort: {round(float(x_input), 1)} N</p>
+        <div style="background-color:{color_alert}; padding:5px; border-radius:8px; text-align:center; border: 1.5px solid #333; margin-bottom: 10px;">
+            <span style="color:black; font-weight:bold; font-size:14px;">{label} | Effort: {round(float(x_input), 1)} N</span>
         </div>
     """, unsafe_allow_html=True)
 
-    # --- 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)
-        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']
-            
-            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>"
-
-            if condition_view in ["Both", "Dry only"]:
-                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=hover_dry))
-            if condition_view in ["Both", "Wet only"]:
-                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=hover_wet))
-
-        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="Performance",
-            plot_bgcolor='white',
-            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")
+    # --- GRAPHIC AREA ---
+    filtered_df = df[df['model name'].isin(selected_models)]
+    fig = go.Figure()
+    x_range = np.linspace(40, 200, 100)
+    colors = ['#0082C3', '#E63312', '#333333', '#00A14B', '#FFD200']
+
+    # Reference data
+    row_ref = df[df['model name'] == ref_model].iloc[0]
+    ref_dry_val = row_ref['dry a'] * x_input + row_ref['dry b']
+    ref_wet_val = row_ref['wet a'] * x_input + row_ref['wet b']
+
+    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']
+        comparison_results.append({"name": row['model name'], "dry": y_dry_val, "wet": y_wet_val})
         
-        st.plotly_chart(fig, use_container_width=True)
-
-    with col2:
-        st.subheader("💡 Analysis")
-        if not filtered_df.empty:
-            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:
+        y_plot_dry = row['dry a'] * x_range + row['dry b']
+        y_plot_wet = row['wet a'] * x_range + row['wet b']
+
+        if condition_view in ["Both", "Dry only"]:
+            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=f"<b>{row['model name']}</b><br>Perf: %{{y:.1f}} N<extra></extra>"))
+        if condition_view in ["Both", "Wet only"]:
+            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=f"<b>{row['model name']}</b><br>Perf: %{{y:.1f}} N<extra></extra>"))
+
+    fig.add_vline(x=x_input, line_width=2, line_dash="dash", line_color="black")
+    fig.update_layout(height=450, xaxis_title="Lever Effort [N]", yaxis_title="Performance [N]", font=dict(color="black"), plot_bgcolor='white', paper_bgcolor='white', hovermode="x unified", legend=dict(font=dict(size=10, color="black"), bordercolor="black", borderwidth=1))
+    fig.update_xaxes(showline=True, linewidth=2, linecolor='black', gridcolor='#EEEEEE')
+    fig.update_yaxes(showline=True, linewidth=2, linecolor='black', gridcolor='#EEEEEE')
+    
+    st.plotly_chart(fig, use_container_width=True)
+
+    # --- ANALYSIS DASHBOARD (BOTTOM) ---
+    st.markdown(f"<p class='small-font'><b>📊 Dashboard Analysis [N]</b> | Reference: {ref_model}</p>", unsafe_allow_html=True)
+    
+    if not filtered_df.empty:
+        cols = st.columns(len(comparison_results))
+        for i, res in enumerate(comparison_results):
+            with cols[i]:
+                is_ref = (res['name'] == ref_model)
+                st.markdown(f"<p style='font-size:13px; font-weight:bold; margin-bottom:0;'>{res['name']} {'⭐' if is_ref else ''}</p>", unsafe_allow_html=True)
+                
+                if condition_view in ["Both", "Dry only"]:
+                    d_val = round(res['dry'], 1)
+                    if enable_comparison and not is_ref:
+                        diff = d_val - round(ref_dry_val, 1)
+                        pct = (diff / ref_dry_val * 100) if ref_dry_val != 0 else 0
+                        st.metric("Dry Performance", f"{d_val} N", f"{diff:+.1f} N ({pct:+.1f}%)")
+                    else:
+                        st.metric("Dry Performance", f"{d_val} N")
+                
+                if condition_view in ["Both", "Wet only"]:
+                    w_val = round(res['wet'], 1)
+                    if enable_comparison and not is_ref:
+                        diff_w = w_val - round(ref_wet_val, 1)
+                        pct_w = (diff_w / ref_wet_val * 100) if ref_wet_val != 0 else 0
+                        st.metric("Wet Performance", f"{w_val} N", f"{diff_w:+.1f} N ({pct_w:+.1f}%)")
+                    else:
+                        st.metric("Wet Performance", f"{w_val} N")
+
+                if show_loss and condition_view == "Both":
                     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("Select models.")
+                    st.markdown(f"<p style='color:red; font-size:11px; margin-top:-10px;'>Wet Loss: -{round(loss, 1)}%</p>", unsafe_allow_html=True)
 
 except Exception as e:
     st.error(f"Error: {e}")