Update src/streamlit_app.py

src/streamlit_app.py

@@ -7,53 +7,43 @@ import os
 # Configuration de la page
 st.set_page_config(page_title="Brake Performance Lab", layout="wide", page_icon="🚲")
 
-# --- CSS RADICAL POUR SIDEBAR ET CONTRASTE ---
+# --- CSS RADICAL : COMPACTAGE SIDEBAR + CONTRASTE ---
 st.markdown("""
     <style>
-    /* Force le fond global en blanc */
     .stApp { background-color: #FFFFFF !important; }
 
-    /* --- SIDEBAR --- */
-    /* Force le fond de la sidebar en gris très clair pour qu'on voie bien les contours */
-    [data-testid="stSidebar"] {
-        background-color: #F8F9FB !important;
-        border-right: 1px solid #000000;
+    /* --- COMPACTAGE SIDEBAR --- */
+    /* On réduit l'espace entre tous les blocs de la sidebar */
+    [data-testid="stSidebar"] [data-testid="stVerticalBlock"] {
+        gap: 0.5rem !important;
+        padding-top: 0rem !important;
     }
     
-    /* Force TOUS les textes et labels de la sidebar en NOIR */
-    [data-testid="stSidebar"] .stText, 
-    [data-testid="stSidebar"] label, 
-    [data-testid="stSidebar"] p,
-    [data-testid="stSidebar"] span {
-        color: #000000 !important;
-        font-weight: bold !important;
+    /* On réduit la marge au dessus des titres et sous les lignes --- */
+    hr { margin: 0.5rem 0 !important; }
+    .stSlider { margin-bottom: -15px !important; }
+    .stMultiSelect { margin-bottom: -10px !important; }
+    .stSelectbox { margin-bottom: -10px !important; }
+
+    [data-testid="stSidebar"] { background-color: #F8F9FB !important; border-right: 1px solid #000000; }
+    [data-testid="stSidebar"] label, [data-testid="stSidebar"] p, [data-testid="stSidebar"] span {
+        color: #000000 !important; font-weight: bold !important;
     }
 
-    /* Force le texte à l'intérieur des boites de sélection (Multiselect / Selectbox) */
-    div[data-baseweb="select"] * {
-        color: #000000 !important;
-    }
-    
-    /* Change le fond des boites de saisie pour qu'elles ne soient pas noires */
-    div[data-baseweb="select"] {
-        background-color: #FFFFFF !important;
-        border: 1px solid #000000 !important;
-    }
+    /* --- GESTION DES LISTES DÉROULANTES (NOIR SUR BLANC) --- */
+    div[data-baseweb="select"] > div { background-color: #000000 !important; color: #FFFFFF !important; }
+    div[data-baseweb="select"] span { color: #FFFFFF !important; }
+    ul[role="listbox"] { background-color: #000000 !important; }
+    ul[role="listbox"] li { color: #FFFFFF !important; background-color: #000000 !important; }
+    ul[role="listbox"] li:hover { background-color: #333333 !important; }
 
     /* --- MAIN AREA --- */
-    /* Texte Noir partout */
     * { color: #000000; }
-
-    /* Metrics et boîtes d'analyse */
     [data-testid="stMetricValue"] { color: #000000 !important; font-weight: 800 !important; }
-    [data-testid="stMetricLabel"] { color: #000000 !important; }
     [data-testid="column"] { 
-        padding: 10px !important; 
-        border: 2px solid #000000 !important; 
-        border-radius: 8px !important; 
-        background-color: #FFFFFF !important; 
+        padding: 10px !important; border: 2px solid #000000 !important; 
+        border-radius: 8px !important; background-color: #FFFFFF !important; 
     }
-
     .alert-red { color: #B71C1C !important; font-weight: 900; }
     .check-green { color: #1B5E20 !important; font-weight: 900; }
     </style>
@@ -72,31 +62,30 @@ try:
     all_models = df['model name'].unique().tolist()
 
     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.image("https://upload.wikimedia.org/wikipedia/commons/thumb/0/08/Decathlon_Logo.svg/1280px-Decathlon_Logo.svg.png", width=180)
         st.title("⚙️ SETTINGS")
+        
+        # Les blocs sont maintenant rapprochés par le CSS "gap" et "margin"
         x_input = st.slider("🫱 Lever Effort [N]", 40, 200, 100)
-        st.markdown("---")
         
-        # Sélection des modèles
         selected_models = st.multiselect("Select Models to Display", options=all_models, default=all_models[:2])
-        st.markdown("---")
         
-        # Standard Compliance
         st.subheader("📋 Standard Compliance")
         norm_type = st.selectbox("Category", ["None", "City/Trekking", "Kids", "MTB", "Racing"])
         
-        # Valeurs de normes
+        # Valeurs de normes officielles
         n_dry, n_wet = 0, 0
         if norm_type == "City/Trekking": n_dry, n_wet = 340, 220
         elif norm_type == "Kids": n_dry, n_wet = 204, 132
         elif norm_type == "MTB": n_dry, n_wet = 425, 280
         elif norm_type == "Racing": n_dry, n_wet = 425, 260
 
+        st.markdown("---")
         with st.expander("🔍 Display Options"):
             show_loss = st.checkbox("Show Wet Loss Analysis", value=True)
             enable_comparison = st.checkbox("Enable Reference Comparison", value=True)
-            ref_model = st.selectbox("Reference Model (Benchmark)", options=all_models)
-            condition_view = st.radio("Conditions to display", ["Both", "Dry only", "Wet only"], index=0)
+            ref_model = st.selectbox("Reference Model", options=all_models)
+            condition_view = st.radio("Conditions", ["Both", "Dry only", "Wet only"], index=0)
 
     # --- HEADER ---
     if x_input < 70: label, color_alert = "❄️ LIGHT BRAKING", "#a1c4fd"
@@ -104,7 +93,7 @@ try:
     else: label, color_alert = "🔥 POWERFUL BRAKING", "#ff4b4b"
 
     st.markdown(f"""
-        <div style="background-color:{color_alert}; padding:8px; border-radius:8px; text-align:center; border: 3px solid #000000; margin-bottom: 15px;">
+        <div style="background-color:{color_alert}; padding:8px; border-radius:8px; text-align:center; border: 3px solid #000000; margin-bottom: 10px;">
             <span style="color:#000000; font-weight:900; font-size:16px;">{label} | Effort: {round(float(x_input), 1)} N</span>
         </div>
     """, unsafe_allow_html=True)
@@ -115,7 +104,6 @@ try:
     x_range = np.linspace(40, 200, 150)
     colors = ['#0082C3', '#E63312', '#000000', '#00A14B', '#FFD200']
 
-    # On s'assure que la réf est chargée même si non affichée
     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']
@@ -131,56 +119,43 @@ try:
             fig.add_trace(go.Scatter(x=x_range, y=row['dry a']*x_range+row['dry b'], mode='lines', name=f"{row['model name']} (Dry)", line=dict(color=color, width=4)))
             if n_dry > 0:
                 xt = (n_dry - row['dry b']) / row['dry a']
-                if xt <= 200: fig.add_trace(go.Scatter(x=[xt], y=[n_dry], mode='markers+text', text=[f"{round(xt,1)}N"], textfont=dict(color="black", size=12, weight=700), textposition="top center", marker=dict(color=color, size=10, symbol='x'), showlegend=False))
+                if xt <= 200: fig.add_trace(go.Scatter(x=[xt], y=[n_dry], mode='markers+text', text=[f"{round(xt,1)}N"], textfont=dict(color="black", size=11, weight=700), textposition="top center", marker=dict(color=color, size=10, symbol='x'), showlegend=False))
 
         if condition_view in ["Both", "Wet only"]:
             fig.add_trace(go.Scatter(x=x_range, y=row['wet a']*x_range+row['wet b'], mode='lines', name=f"{row['model name']} (Wet)", line=dict(color=color, width=3, dash='dot')))
             if n_wet > 0:
                 xtw = (n_wet - row['wet b']) / row['wet a']
-                if xtw <= 200: fig.add_trace(go.Scatter(x=[xtw], y=[n_wet], mode='markers+text', text=[f"{round(xtw,1)}N"], textfont=dict(color="black", size=12, weight=700), textposition="bottom center", marker=dict(color=color, size=10, symbol='circle-open'), showlegend=False))
+                if xtw <= 200: fig.add_trace(go.Scatter(x=[xtw], y=[n_wet], mode='markers+text', text=[f"{round(xtw,1)}N"], textfont=dict(color="black", size=11, weight=700), textposition="bottom center", marker=dict(color=color, size=10, symbol='circle-open'), showlegend=False))
 
     fig.add_vline(x=x_input, line_width=2, line_dash="dash", line_color="#000000")
     
-    # Normes
     if n_dry > 0 and (condition_view in ["Both", "Dry only"]):
         fig.add_hline(y=n_dry, line_width=3, line_color="#000000", annotation_text=f"Norm Dry: {n_dry}N", annotation_font=dict(color="black", size=12, weight=700))
     if n_wet > 0 and (condition_view in ["Both", "Wet only"]):
         fig.add_hline(y=n_wet, line_width=3, line_dash="dot", line_color="#000000", annotation_text=f"Norm Wet: {n_wet}N", annotation_font=dict(color="black", size=12, weight=700))
 
     fig.update_layout(
-        height=480,
-        xaxis=dict(title="Lever Effort [N]", color="#000000", linecolor="#000000", linewidth=3, tickfont=dict(color="#000000", size=13, weight=700), gridcolor="#E0E0E0"),
-        yaxis=dict(title="Performance [N]", color="#000000", linecolor="#000000", linewidth=3, tickfont=dict(color="#000000", size=13, weight=700), gridcolor="#E0E0E0"),
+        height=450, xaxis=dict(title="Lever Effort [N]", color="#000000", linecolor="#000000", linewidth=3, tickfont=dict(color="#000000", weight=700), gridcolor="#E0E0E0"),
+        yaxis=dict(title="Performance [N]", color="#000000", linecolor="#000000", linewidth=3, tickfont=dict(color="#000000", weight=700), gridcolor="#E0E0E0"),
         font=dict(color="#000000"), plot_bgcolor='#FFFFFF', paper_bgcolor='#FFFFFF', hovermode="x unified",
-        legend=dict(font=dict(color="#000000", size=11, weight=700), bordercolor="#000000", borderwidth=2, bgcolor="#FFFFFF")
+        legend=dict(font=dict(color="#000000", size=11, weight=700), bordercolor="#000000", borderwidth=1, bgcolor="#FFFFFF")
     )
     st.plotly_chart(fig, use_container_width=True)
 
-    # --- ANALYSIS DASHBOARD ---
-    st.markdown(f"<p style='color:#000000; font-weight:900; font-size:16px;'>📊 Analysis | Ref: {ref_model}</p>", unsafe_allow_html=True)
+    # --- ANALYSIS ---
+    st.markdown(f"<p style='color:#000000; font-weight:900; font-size:16px; margin-top:-10px;'>📊 Analysis | Ref: {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]:
-                st.markdown(f"<p style='font-size:14px; font-weight:900; color:#000000; margin-bottom:5px; text-decoration: underline;'>{res['name']} {'⭐' if (res['name'] == ref_model) else ''}</p>", unsafe_allow_html=True)
-                
+                st.markdown(f"<p style='font-size:13px; font-weight:900; color:#000000; margin-bottom:0px; text-decoration: underline;'>{res['name']} {'⭐' if (res['name'] == ref_model) else ''}</p>", unsafe_allow_html=True)
                 if condition_view in ["Both", "Dry only"]:
                     dv = round(res['dry'], 1)
-                    st.metric("Dry Perf.", f"{dv} N", f"{dv - round(ref_dry_val, 1):+.1f} N Vs Ref." if enable_comparison and not (res['name'] == ref_model) else None)
-                    if n_dry > 0:
-                        xt = (n_dry - res['row']['dry b']) / res['row']['dry a']
-                        if xt > 180: st.markdown(f"<div class='alert-red'>❌ NON CONFORME SEC<br>Target: {round(xt,1)}N > 180N</div>", unsafe_allow_html=True)
-                        else: st.markdown(f"<div class='check-green'>✅ Conforme Sec ({round(xt,1)}N)</div>", unsafe_allow_html=True)
-
+                    st.metric("Dry Perf.", f"{dv} N", f"{dv - round(ref_dry_val, 1):+.1f} N" if enable_comparison and not (res['name'] == ref_model) else None)
                 if condition_view in ["Both", "Wet only"]:
                     wv = round(res['wet'], 1)
-                    st.metric("Wet Perf.", f"{wv} N", f"{wv - round(ref_wet_val, 1):+.1f} N Vs Ref." if enable_comparison and not (res['name'] == ref_model) else None)
-                    if n_wet > 0:
-                        xtw = (n_wet - res['row']['wet b']) / res['row']['wet a']
-                        if xtw > 180: st.markdown(f"<div class='alert-red'>❌ NON CONFORME HUMIDE<br>Target: {round(xtw,1)}N > 180N</div>", unsafe_allow_html=True)
-                        else: st.markdown(f"<div class='check-green'>✅ Conforme Humide ({round(xtw,1)}N)</div>", unsafe_allow_html=True)
-
+                    st.metric("Wet Perf.", f"{wv} N", f"{wv - round(ref_wet_val, 1):+.1f} N" if enable_comparison and not (res['name'] == ref_model) else None)
                 if show_loss and condition_view == "Both":
                     loss_pct = ((res['dry'] - res['wet']) / res['dry'] * 100) if res['dry'] != 0 else 0
                     st.metric("Wet Loss", f"-{round(loss_pct, 1)}%", delta_color="inverse")