Update src/streamlit_app.py

src/streamlit_app.py

@@ -7,23 +7,43 @@ import os
 # Configuration de la page
 st.set_page_config(page_title="Brake Performance Lab", layout="wide", page_icon="🚲")
 
-# --- FORÇAGE CSS NOIR ABSOLU POUR STREAMLIT ---
+# --- LE "SUPER CSS" POUR FORCER LE BLANC ET LE NOIR ---
 st.markdown("""
     <style>
-    html, body, [class*="css"], .stMarkdown, p, span, label {
+    /* 1. On force le fond de toute la page en BLANC */
+    .stApp {
+        background-color: #FFFFFF !important;
+    }
+    
+    /* 2. On force TOUT le texte en NOIR ABSOLU */
+    * {
+        color: #000000 !important;
+    }
+
+    /* 3. On force les étiquettes des menus (Selectbox, Slider, Multiselect) en NOIR */
+    label, .stMultiSelect div, .stSelectbox div, .stSlider div {
         color: #000000 !important;
-        font-weight: 500 !important;
+        font-weight: bold !important;
     }
+
+    /* 4. On s'assure que les entrées de texte et menus ont un fond clair pour voir le texte noir dedans */
+    .stMultiSelect span, .stSelectbox div[data-baseweb="select"] {
+        background-color: #F0F2F6 !important;
+    }
+
+    /* 5. Metrics et boîtes d'analyse */
     [data-testid="stMetricValue"] { color: #000000 !important; font-weight: 800 !important; font-size: 22px !important; }
     [data-testid="stMetricLabel"] { color: #000000 !important; font-weight: bold !important; }
     [data-testid="column"] { 
         padding: 10px !important; 
         border: 2px solid #000000 !important; 
         border-radius: 8px !important; 
-        background-color: #ffffff !important; 
+        background-color: #FFFFFF !important; 
     }
-    .alert-red { color: #D32F2F !important; font-weight: 900 !important; font-size: 13px; }
-    .check-green { color: #2E7D32 !important; font-weight: 900 !important; font-size: 13px; }
+
+    /* 6. Alertes (on garde les couleurs mais en version foncée pour la lisibilité) */
+    .alert-red { color: #B71C1C !important; font-weight: 900 !important; }
+    .check-green { color: #1B5E20 !important; font-weight: 900 !important; }
     </style>
     """, unsafe_allow_html=True)
 
@@ -77,6 +97,7 @@ try:
     filtered_df = df[df['model name'].isin(selected_models)]
     fig = go.Figure()
     x_range = np.linspace(40, 200, 150)
+    # Couleurs courbes : Bleu Decathlon, Rouge, Noir, Vert, Jaune
     colors = ['#0082C3', '#E63312', '#000000', '#00A14B', '#FFD200']
 
     row_ref = df[df['model name'] == ref_model].iloc[0]
@@ -91,53 +112,50 @@ try:
         comparison_results.append({"name": row['model name'], "dry": y_d, "wet": y_w, "row": row})
         
         if condition_view in ["Both", "Dry only"]:
-            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), hovertemplate=f"<b>{row['model name']}</b><br>Perf: %{{y:.1f}} N<extra></extra>"))
+            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="#000000", size=12, 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'), hovertemplate=f"<b>{row['model name']}</b><br>Perf: %{{y:.1f}} N<extra></extra>"))
+            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="#000000", size=12, weight=700), textposition="bottom center", marker=dict(color=color, size=10, symbol='circle-open'), showlegend=False))
 
     # Lignes de 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"<b>Norm Dry: {n_dry}N</b>", annotation_font=dict(color="black", size=12))
+        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"<b>Norm Wet: {n_wet}N</b>", annotation_font=dict(color="black", size=12))
+        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.add_vline(x=x_input, line_width=2, line_dash="dash", line_color="#000000")
     
-    # --- NOIR TOTAL AVEC 'WEIGHT' AU LIEU DE 'BOLD' ---
     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"),
         font=dict(color="#000000", size=12),
-        plot_bgcolor='white', paper_bgcolor='white',
+        plot_bgcolor='#FFFFFF', paper_bgcolor='#FFFFFF',
         hovermode="x unified",
-        legend=dict(font=dict(color="#000000", size=12, weight=700), bordercolor="#000000", borderwidth=2, bgcolor="white")
+        legend=dict(font=dict(color="#000000", size=12, weight=700), bordercolor="#000000", borderwidth=2, bgcolor="#FFFFFF")
     )
     st.plotly_chart(fig, use_container_width=True)
 
-    # --- ANALYSIS DASHBOARD (BOTTOM) ---
-    st.markdown(f"<p style='color:black; font-weight:900; font-size:16px;'>📊 Performance Analysis [N] | Ref: {ref_model}</p>", unsafe_allow_html=True)
+    # --- ANALYSIS DASHBOARD ---
+    st.markdown(f"<p style='color:#000000; font-weight:900; font-size:16px;'>📊 Performance Analysis [N] | 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]:
-                is_ref = (res['name'] == ref_model)
-                st.markdown(f"<p style='font-size:14px; font-weight:900; color:black; margin-bottom:5px; text-decoration: underline;'>{res['name']} {'⭐' if is_ref else ''}</p>", unsafe_allow_html=True)
+                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)
                 
                 if condition_view in ["Both", "Dry only"]:
                     dv = round(res['dry'], 1)
-                    if enable_comparison and not is_ref:
+                    if enable_comparison and not (res['name'] == ref_model):
                         diff = dv - round(ref_dry_val, 1)
-                        pct = (diff / ref_dry_val * 100) if ref_dry_val != 0 else 0
-                        st.metric("Dry Perf.", f"{dv} N", f"{diff:+.1f} N ({pct:+.1f}%) Vs Ref.")
+                        st.metric("Dry Perf.", f"{dv} N", f"{diff:+.1f} N Vs Ref.")
                     else: st.metric("Dry Perf.", f"{dv} N")
                     
                     if n_dry > 0:
@@ -147,10 +165,9 @@ try:
 
                 if condition_view in ["Both", "Wet only"]:
                     wv = round(res['wet'], 1)
-                    if enable_comparison and not is_ref:
+                    if enable_comparison and not (res['name'] == ref_model):
                         diffw = wv - round(ref_wet_val, 1)
-                        pctw = (diffw / ref_wet_val * 100) if ref_wet_val != 0 else 0
-                        st.metric("Wet Perf.", f"{wv} N", f"{diffw:+.1f} N ({pctw:+.1f}%) Vs Ref.")
+                        st.metric("Wet Perf.", f"{wv} N", f"{diffw:+.1f} N Vs Ref.")
                     else: st.metric("Wet Perf.", f"{wv} N")
                     
                     if n_wet > 0:
@@ -160,7 +177,7 @@ try:
 
                 if show_loss and condition_view == "Both":
                     loss_pct = ((res['dry'] - res['wet']) / res['dry'] * 100) if res['dry'] != 0 else 0
-                    st.metric("Efficiency Loss", f"-{round(loss_pct, 1)}%", f"{round(res['wet']-res['dry'], 1)} N vs Dry", delta_color="inverse")
+                    st.metric("Efficiency Loss", f"-{round(loss_pct, 1)}%", delta_color="inverse")
 
 except Exception as e:
     st.error(f"Error: {e}")