Update src/streamlit_app.py

src/streamlit_app.py

@@ -4,19 +4,32 @@ import numpy as np
 import plotly.graph_objects as go
 import os
 
-# Page Configuration
+# Configuration de la page
 st.set_page_config(page_title="Brake Performance Lab", layout="wide", page_icon="🚲")
 
-# Style CSS Global
+# --- FORÇAGE CSS NOIR ABSOLU POUR STREAMLIT ---
 st.markdown("""
     <style>
-    .small-font { font-size:12px !important; color: black !important; }
-    [data-testid="stMetricValue"] { font-size: 18px !important; color: black !important; }
-    [data-testid="stMetricDelta"] { font-size: 12px !important; }
-    [data-testid="column"] { padding: 8px !important; border: 1px solid #000000; border-radius: 5px; background-color: #ffffff; }
-    .alert-red { color: #ff4b4b; font-weight: bold; font-size: 11px; margin-top: 5px; }
-    .check-green { color: #00A14B; font-weight: bold; font-size: 11px; }
-    h1, h2, h3, h4, p, span { color: black !important; }
+    /* Tous les textes Streamlit en Noir pur */
+    html, body, [class*="css"], .stMarkdown, p, span, label {
+        color: #000000 !important;
+        font-weight: 500 !important;
+    }
+    /* Metrics en Noir pur */
+    [data-testid="stMetricValue"] { color: #000000 !important; font-weight: 800 !important; font-size: 22px !important; }
+    [data-testid="stMetricLabel"] { color: #000000 !important; font-weight: bold !important; }
+    
+    /* Contours des boites d'analyse */
+    [data-testid="column"] { 
+        padding: 10px !important; 
+        border: 2px solid #000000 !important; 
+        border-radius: 8px !important; 
+        background-color: #ffffff !important; 
+    }
+    
+    /* Alerte rouge/verte bien flashy */
+    .alert-red { color: #D32F2F !important; font-weight: 900 !important; font-size: 13px; }
+    .check-green { color: #2E7D32 !important; font-weight: 900 !important; font-size: 13px; }
     </style>
     """, unsafe_allow_html=True)
 
@@ -32,10 +45,9 @@ 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")
+        st.title("⚙️ SETTINGS")
         x_input = st.slider("🫱 Lever Effort [N]", 40, 200, 100)
         st.markdown("---")
         selected_models = st.multiselect("Select Models to Display", options=all_models, default=all_models[:2])
@@ -50,7 +62,6 @@ try:
         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)
@@ -58,16 +69,13 @@ try:
             condition_view = st.radio("Conditions to display", ["Both", "Dry only", "Wet only"], index=0)
 
     # --- DIAGNOSTIC HEADER ---
-    if x_input < 70:
-        label, color_alert = "❄️ LIGHT BRAKING", "#a1c4fd"
-    elif 70 <= x_input <= 110:
-        label, color_alert = "⚖️ MODERATE BRAKING", "#ffdb58"
-    else:
-        label, color_alert = "🔥 POWERFUL BRAKING", "#ff4b4b"
+    if x_input < 70: label, color_alert = "❄️ LIGHT BRAKING", "#a1c4fd"
+    elif 70 <= x_input <= 110: label, color_alert = "⚖️ MODERATE BRAKING", "#ffdb58"
+    else: label, color_alert = "🔥 POWERFUL BRAKING", "#ff4b4b"
 
     st.markdown(f"""
-        <div style="background-color:{color_alert}; padding:5px; border-radius:8px; text-align:center; border: 2px solid #000; margin-bottom: 10px;">
-            <span style="color:black; font-weight:bold; font-size:14px;">{label} | Effort: {round(float(x_input), 1)} N</span>
+        <div style="background-color:{color_alert}; padding:10px; border-radius:8px; text-align:center; border: 3px solid #000000; margin-bottom: 15px;">
+            <span style="color:#000000 !important; font-weight:900; font-size:16px;">{label} | Effort: {round(float(x_input), 1)} N</span>
         </div>
     """, unsafe_allow_html=True)
 
@@ -75,7 +83,7 @@ try:
     filtered_df = df[df['model name'].isin(selected_models)]
     fig = go.Figure()
     x_range = np.linspace(40, 200, 150)
-    colors = ['#0082C3', '#E63312', '#333333', '#00A14B', '#FFD200']
+    colors = ['#0082C3', '#E63312', '#000000', '#00A14B', '#FFD200']
 
     row_ref = df[df['model name'] == ref_model].iloc[0]
     ref_dry_val = row_ref['dry a'] * x_input + row_ref['dry b']
@@ -84,82 +92,78 @@ try:
     comparison_results = []
     for i, (index, row) in enumerate(filtered_df.iterrows()):
         color = colors[i % len(colors)]
-        y_dry_now = row['dry a'] * x_input + row['dry b']
-        y_wet_now = row['wet a'] * x_input + row['wet b']
-        comparison_results.append({"name": row['model name'], "dry": y_dry_now, "wet": y_wet_now, "row": row})
+        y_d = row['dry a'] * x_input + row['dry b']
+        y_w = row['wet a'] * x_input + row['wet b']
+        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), hovertemplate=f"<b>{row['model name']}</b><br>Perf: %{{y:.1f}} N<extra></extra>"))
             if n_dry > 0:
-                x_t = (n_dry - row['dry b']) / row['dry a']
-                if x_t <= 200:
-                    fig.add_trace(go.Scatter(x=[x_t], y=[n_dry], mode='markers+text', text=[f"{round(x_t,1)}N"], textposition="top center", marker=dict(color=color, size=10, symbol='x'), showlegend=False))
+                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), 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=2, 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'), hovertemplate=f"<b>{row['model name']}</b><br>Perf: %{{y:.1f}} N<extra></extra>"))
             if n_wet > 0:
-                x_t_w = (n_wet - row['wet b']) / row['wet a']
-                if x_t_w <= 200:
-                    fig.add_trace(go.Scatter(x=[x_t_w], y=[n_wet], mode='markers+text', text=[f"{round(x_t_w,1)}N"], textposition="bottom center", marker=dict(color=color, size=10, symbol='circle-open'), showlegend=False))
+                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), 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=2, line_color="#000", annotation_text=f"Norm Dry: {n_dry}N")
+        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))
     if n_wet > 0 and (condition_view in ["Both", "Wet only"]):
-        fig.add_hline(y=n_wet, line_width=2, line_dash="dot", line_color="#000", annotation_text=f"Norm Wet: {n_wet}N")
+        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_vline(x=x_input, line_width=2, line_dash="dash", line_color="#000")
-    fig.update_layout(height=450, xaxis_title="Lever Effort [N]", yaxis_title="Performance [N]", font=dict(color="#000"), plot_bgcolor='white', paper_bgcolor='white', hovermode="x unified", legend=dict(font=dict(color="#000"), bordercolor="#000", borderwidth=1))
-    fig.update_xaxes(showline=True, linewidth=2, linecolor='#000', gridcolor='#EEE')
-    fig.update_yaxes(showline=True, linewidth=2, linecolor='#000', gridcolor='#EEE')
+    fig.add_vline(x=x_input, line_width=2, line_dash="dash", line_color="#000000")
+    
+    # --- NOIR TOTAL SUR LE GRAPHIQUE ---
+    fig.update_layout(
+        height=480,
+        xaxis=dict(title="Lever Effort [N]", color="#000000", linecolor="#000000", linewidth=3, tickfont=dict(color="#000000", size=13, bold=True), gridcolor="#E0E0E0"),
+        yaxis=dict(title="Performance [N]", color="#000000", linecolor="#000000", linewidth=3, tickfont=dict(color="#000000", size=13, bold=True), gridcolor="#E0E0E0"),
+        font=dict(color="#000000", size=12),
+        plot_bgcolor='white', paper_bgcolor='white',
+        hovermode="x unified",
+        legend=dict(font=dict(color="#000000", size=12, bold=True), bordercolor="#000000", borderwidth=2, bgcolor="white")
+    )
     st.plotly_chart(fig, use_container_width=True)
 
     # --- ANALYSIS DASHBOARD (BOTTOM) ---
-    st.markdown(f"<p class='small-font'><b>📊 Performance Analysis [N]</b> | Ref: {ref_model}</p>", unsafe_allow_html=True)
+    st.markdown(f"<p style='color:black; 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:13px; font-weight:bold; color:black; margin-bottom:5px;'>{res['name']} {'⭐' if is_ref else ''}</p>", unsafe_allow_html=True)
+                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)
                 
-                # --- DRY PERFORMANCE & BENCHMARK ---
                 if condition_view in ["Both", "Dry only"]:
-                    d_val = round(res['dry'], 1)
+                    dv = round(res['dry'], 1)
                     if enable_comparison and not is_ref:
-                        diff = d_val - round(ref_dry_val, 1)
+                        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"{d_val} N", f"{diff:+.1f} N ({pct:+.1f}%) Vs Ref.")
-                    else:
-                        st.metric("Dry Perf.", f"{d_val} N")
+                        st.metric("Dry Perf.", f"{dv} N", f"{diff:+.1f} N ({pct:+.1f}%) Vs Ref.")
+                    else: st.metric("Dry Perf.", f"{dv} N")
                     
-                    # NORM CHECK DRY
                     if n_dry > 0:
-                        x_target = (n_dry - res['row']['dry b']) / res['row']['dry a']
-                        if x_target > 180:
-                            st.markdown(f"<div class='alert-red'>❌ NON CONFORME SEC ({norm_type})<br>Target: {round(x_target,1)}N > 180N</div>", unsafe_allow_html=True)
-                        else:
-                            st.markdown(f"<div class='check-green'>✅ Conforme Sec ({round(x_target,1)}N)</div>", unsafe_allow_html=True)
+                        xt = (n_dry - res['row']['dry b']) / res['row']['dry a']
+                        if xt > 180: st.markdown(f"<div class='alert-red'>❌ NON CONFORME SEC ({norm_type})<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)
 
-                # --- WET PERFORMANCE & BENCHMARK ---
                 if condition_view in ["Both", "Wet only"]:
-                    w_val = round(res['wet'], 1)
+                    wv = 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 Perf.", f"{w_val} N", f"{diff_w:+.1f} N ({pct_w:+.1f}%) Vs Ref.")
-                    else:
-                        st.metric("Wet Perf.", f"{w_val} N")
+                        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.")
+                    else: st.metric("Wet Perf.", f"{wv} N")
                     
-                    # NORM CHECK WET
                     if n_wet > 0:
-                        x_target_w = (n_wet - res['row']['wet b']) / res['row']['wet a']
-                        if x_target_w > 180:
-                            st.markdown(f"<div class='alert-red'>❌ NON CONFORME HUMIDE ({norm_type})<br>Target: {round(x_target_w,1)}N > 180N</div>", unsafe_allow_html=True)
-                        else:
-                            st.markdown(f"<div class='check-green'>✅ Conforme Humide ({round(x_target_w,1)}N)</div>", unsafe_allow_html=True)
+                        xtw = (n_wet - res['row']['wet b']) / res['row']['wet a']
+                        if xtw > 180: st.markdown(f"<div class='alert-red'>❌ NON CONFORME HUMIDE ({norm_type})<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)
 
-                # --- WET LOSS ---
                 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")