Update src/streamlit_app.py

src/streamlit_app.py

@@ -5,28 +5,51 @@ import plotly.graph_objects as go
 import os
 
 # ==============================================================================
-# 1. CONFIGURATION ET STYLE CSS (HAUT CONTRASTE & INPUTS)
+# 1. CONFIGURATION ET STYLE CSS (FORÇAGE CONTRASTE TOTAL & ANTI-DARK MODE)
 # ==============================================================================
 st.set_page_config(page_title="Brake Performance Lab", layout="wide", page_icon="🚲")
 
 st.markdown("""
     <style>
-    /* 1.1 Thème Global Blanc */
-    .stApp, [data-testid="stSidebar"] { background-color: #FFFFFF !important; }
-    * { color: #000000 !important; font-family: 'Arial', sans-serif; }
+    /* 1.1 Thème de base blanc pur */
+    .stApp, [data-testid="stSidebar"] { 
+        background-color: #FFFFFF !important; 
+    }
+    
+    /* 1.2 Force le texte en NOIR PUR partout (Légendes, Labels, Titres) */
+    * { 
+        color: #000000 !important; 
+        font-family: 'Arial', sans-serif; 
+    }
     
-    /* 1.2 Sidebar & Interactivité */
-    [data-testid="stSidebar"] [data-testid="stVerticalBlock"] { gap: 0.1rem !important; padding-top: 0rem !important; }
-    hr { margin: 0.5rem 0 !important; }
-
-    /* 1.3 Listes déroulantes (Fix Noir/Noir) */
-    div[data-baseweb="select"] { border: 2px solid #000 !important; background-color: #FFF !important; }
-    div[data-baseweb="select"] * { color: #000 !important; }
-    ul[role="listbox"] { background-color: #FFFFFF !important; border: 2px solid #000 !important; }
-    li[role="option"] { color: #000 !important; font-weight: bold !important; background-color: #FFF !important; }
-    li[role="option"]:hover { background-color: #0082C3 !important; color: #FFF !important; }
-
-    /* 1.4 INPUTS NUMÉRIQUES (Texte blanc sur fond gris foncé) */
+    /* 1.3 Compactage Sidebar */
+    [data-testid="stSidebar"] [data-testid="stVerticalBlock"] { 
+        gap: 0.1rem !important; 
+        padding-top: 0rem !important; 
+    }
+    hr { border-top: 1px solid #000 !important; margin: 0.5rem 0 !important; }
+
+    /* 1.4 DROPDOWNS : Correction Noir sur Noir pour Hugging Face */
+    div[data-baseweb="select"], div[role="combobox"] { 
+        border: 2px solid #000000 !important; 
+        background-color: #FFFFFF !important; 
+    }
+    div[data-baseweb="select"] * { color: #000000 !important; }
+    ul[role="listbox"] { 
+        background-color: #FFFFFF !important; 
+        border: 2px solid #000000 !important; 
+    }
+    li[role="option"] { 
+        color: #000000 !important; 
+        font-weight: bold !important; 
+        background-color: #FFFFFF !important; 
+    }
+    li[role="option"]:hover { 
+        background-color: #0082C3 !important; 
+        color: #FFFFFF !important; 
+    }
+
+    /* 1.5 INPUTS NUMÉRIQUES (TA DEMANDE : Texte BLANC sur fond sombre) */
     input[type="number"], div[data-baseweb="input"] input {
         color: #FFFFFF !important;
         background-color: #333333 !important;
@@ -34,160 +57,204 @@ st.markdown("""
         font-weight: bold !important;
     }
 
-    /* 1.5 Tags multiselect */
-    [data-testid="stMultiSelect"] span { background-color: #0082C3 !important; color: #FFFFFF !important; }
+    /* 1.6 Correction des étiquettes Streamlit (Radios, Expanders, Labels) */
+    .st-emotion-cache-p4mowd, .st-emotion-cache-16idsys p, label, .st-ae {
+        color: #000000 !important;
+        font-weight: bold !important;
+    }
+
+    /* 1.7 Tags Multiselect (Bleu Decathlon) */
+    [data-testid="stMultiSelect"] span { 
+        background-color: #0082C3 !important; 
+        color: #FFFFFF !important; 
+    }
     
-    /* 1.6 Dashboard Analyse */
+    /* 1.8 Boites du Dashboard Analyse */
     [data-testid="column"] { 
-        padding: 12px !important; border: 2px solid #000 !important; 
-        border-radius: 8px !important; background-color: #FFFFFF !important; 
-        margin-bottom: 10px;
+        padding: 15px !important; 
+        border: 2px solid #000000 !important; 
+        border-radius: 10px !important; 
+        background-color: #FFFFFF !important; 
     }
+    [data-testid="stMetricValue"] { font-weight: 900 !important; font-size: 24px !important; }
     
-    /* 1.7 Alertes Normes (Texte blanc sur badges) */
-    .alert-red { color: #FFFFFF !important; background-color: #B71C1C !important; font-weight: 900 !important; padding: 6px; border-radius: 4px; text-align: center; }
-    .check-green { color: #FFFFFF !important; background-color: #1B5E20 !important; font-weight: 900 !important; padding: 6px; border-radius: 4px; text-align: center; }
+    /* 1.9 BADGES NORMES (TA DEMANDE : Texte BLANC sur fond couleur) */
+    .alert-red { 
+        color: #FFFFFF !important; 
+        background-color: #B71C1C !important; 
+        font-weight: 900 !important; 
+        padding: 8px; 
+        border-radius: 5px; 
+        text-align: center; 
+        margin-top: 5px;
+    }
+    .check-green { 
+        color: #FFFFFF !important; 
+        background-color: #1B5E20 !important; 
+        font-weight: 900 !important; 
+        padding: 8px; 
+        border-radius: 5px; 
+        text-align: center; 
+        margin-top: 5px;
+    }
     </style>
     """, unsafe_allow_html=True)
 
 # ==============================================================================
-# 2. CHARGEMENT DES DONNÉES
+# 2. ACCÈS AUX DONNÉES
 # ==============================================================================
 @st.cache_data
 def load_data():
     current_dir = os.path.dirname(__file__)
     file_path = os.path.join(current_dir, "Brake_Lab_Test_Data.xlsx")
-    if not os.path.exists(file_path): return pd.DataFrame()
+    if not os.path.exists(file_path):
+        return pd.DataFrame()
     df = pd.read_excel(file_path, sheet_name='Data')
     df.columns = df.columns.str.strip()
     return df
 
 df = load_data()
-if df.empty: st.error("Fichier Excel introuvable."); st.stop()
+if df.empty:
+    st.error("Fichier Excel introuvable.")
+    st.stop()
 
 try:
     all_models = df['model name'].unique().tolist()
 
-    # ==========================================================================
-    # 3. SIDEBAR (NAVIGATION & PARAMÈTRES)
-    # ==========================================================================
+    # ==============================================================================
+    # 3. SIDEBAR (CONTRÔLE TOTAL)
+    # ==============================================================================
     with st.sidebar:
         st.image("https://upload.wikimedia.org/wikipedia/commons/thumb/0/08/Decathlon_Logo.svg/1280px-Decathlon_Logo.svg.png", width=150)
-        st.title("⚙️ SETTINGS")
+        st.title("⚙️ CONFIGURATION")
         
-        # Effort Levier (Input principal)
-        x_input = st.slider("🫱 Effort Levier [N]", 40, 200, 100)
-        selected_models = st.multiselect("Models", options=all_models, default=all_models[:2])
+        # 3.1 Entrée d'effort au levier
+        effort_x = st.slider("🫱 Effort Levier [N]", 40, 200, 100)
+        selected_models = st.multiselect("Modèles", options=all_models, default=all_models[:2])
         
-        st.subheader("📋 Compliance")
-        norm_type = st.selectbox("Category", ["None", "City/Trekking", "Kids", "MTB", "Racing"])
+        # 3.2 Normes
+        st.subheader("📋 Normes de conformité")
+        norm_cat = st.selectbox("Catégorie", ["Aucune", "City/Trekking", "Kids", "MTB", "Racing"])
         
         st.markdown("---")
         
-        # Simulation Dynamique en Expandable
+        # 3.3 Simulation (TA DEMANDE : En déroulant)
         with st.expander("📏 Braking Simulation", expanded=False):
-            v_kmh = st.number_input("Speed (km/h)", value=25)
-            m_total = st.number_input("Total Mass (kg)", value=100)
-            rim_inch = st.selectbox("Wheel Size (inch)", [20, 24, 26, 27.5, 28, 29], index=4)
-            dist_av_pct = st.slider("Sharing Front (%)", 0, 100, 70)
-            mu_dry = st.slider("Friction (Dry ground)", 0.1, 1.2, 0.8)
-            mu_wet = st.slider("Friction (Wet ground)", 0.1, 1.2, 0.4)
-
-        # Options d'affichage
-        with st.expander("🔍 Display Options", expanded=False):
-            ref_model = st.selectbox("Reference Benchmark", options=all_models)
-            condition_view = st.radio("View Mode", ["Both", "Dry only", "Wet only"], index=0)
-
-    # ==========================================================================
-    # 4. LOGIQUE DE CALCULS PHYSIQUES (UNITÉS S.I.)
-    # ==========================================================================
-    # 4.1 Conversions
-    r_wheel = (rim_inch * 0.0254) / 2  # Rayon en mètres
-    v_ms = v_kmh / 3.6                # Vitesse en m/s
-    
-    # 4.2 Inertie (Approximation jante+pneu = 1.5kg par roue)
-    # Formule : J = m * R^2
-    j_wheel = 1.5 * (r_wheel**2)
-    w_speed = v_ms / r_wheel          # Vitesse angulaire (rad/s)
+            v_kmh = st.number_input("Vitesse (km/h)", value=25)
+            m_total = st.number_input("Masse Totale (kg)", value=100)
+            rim_inch = st.selectbox("Taille de roue (inch)", [20, 24, 26, 27.5, 28, 29], index=4)
+            sharing_av = st.slider("Sharing Front (%)", 0, 100, 70)
+            st.caption(f"Sharing Rear: {100 - sharing_av}%")
+
+        # 3.4 Options d'affichage (TA DEMANDE : En déroulant)
+        with st.expander("🔍 Options d'affichage", expanded=False):
+            ref_model_name = st.selectbox("Modèle de Benchmark", options=all_models)
+            view_mode = st.radio("Mode de vue", ["Sec & Humide", "Sec uniquement", "Humide uniquement"], index=0)
+            show_loss = st.checkbox("Calculer Perte Wet (%)", value=True)
+
+    # ==============================================================================
+    # 4. MOTEUR PHYSIQUE (CONVERSIONS S.I. & INERTIE)
+    # ==============================================================================
+    # 4.1 Unités S.I.
+    v_ms = v_kmh / 3.6  # km/h vers m/s
+    r_m = (rim_inch * 0.0254) / 2 # inch vers mètres
     
-    # 4.3 Énergie Totale (Translation + Rotation des 2 roues)
-    # E = 1/2mv^2 + 2 * (1/2 Jw^2)
-    energy_total = (0.5 * m_total * (v_ms**2)) + (2 * (0.5 * j_wheel * (w_speed**2)))
+    # 4.2 Énergie Totale (E_trans + E_rot)
+    # Estimation masse rotative (jante+pneu) = 1.5kg par roue
+    j_wheel = 1.5 * (r_m ** 2)
+    omega = v_ms / r_m
+    e_total_j = (0.5 * m_total * (v_ms**2)) + (2 * (0.5 * j_wheel * (omega**2)))
     
-    # 4.4 Normes
-    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
-
-    # 4.5 Benchmark
-    row_ref = df[df['model name'] == ref_model].iloc[0]
-    ref_d_val = row_ref['dry a'] * x_input + row_ref['dry b']
-    ref_w_val = row_ref['wet a'] * x_input + row_ref['wet b']
-
-    # Header Diagnostic
-    label, color = ("❄️ LIGHT", "#a1c4fd") if x_input < 70 else (("⚖️ MODERATE", "#ffdb58") if x_input <= 110 else ("🔥 POWERFUL", "#ff4b4b"))
-    st.markdown(f"<div style='background-color:{color}; padding:8px; border-radius:8px; text-align:center; border: 3px solid #000; margin-bottom: 10px;'><span style='font-weight:900;'>{label} BRAKING | {x_input} N</span></div>", unsafe_allow_html=True)
-
-    # ==========================================================================
-    # 5. GRAPHIQUE (DYNAMIQUE)
-    # ==========================================================================
+    # 4.3 Seuils Normes
+    n_s, n_w = 0, 0
+    if norm_cat == "City/Trekking": n_s, n_w = 340, 220
+    elif norm_cat == "Kids": n_s, n_w = 204, 132
+    elif norm_cat == "MTB": n_s, n_w = 425, 280
+    elif norm_cat == "Racing": n_s, n_w = 425, 260
+
+    # 4.4 Référence Benchmark
+    row_bench = df[df['model name'] == ref_model_name].iloc[0]
+    ref_f_sec = row_bench['dry a'] * effort_x + row_bench['dry b']
+    ref_f_wet = row_bench['wet a'] * effort_x + row_bench['wet b']
+
+    # 5. HEADER DIAGNOSTIC
+    label, color = ("❄️ LIGHT", "#a1c4fd") if effort_x < 70 else (("⚖️ MODERATE", "#ffdb58") if effort_x <= 110 else ("🔥 POWERFUL", "#ff4b4b"))
+    st.markdown(f"<div style='background-color:{color}; padding:8px; border-radius:8px; text-align:center; border: 3px solid #000; margin-bottom: 10px;'><span style='font-weight:900;'>{label} BRAKING | Effort: {effort_x} N</span></div>", unsafe_allow_html=True)
+
+    # ==============================================================================
+    # 6. GRAPHIQUE (FIX COULEURS TEXTE NOIR)
+    # ==============================================================================
     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']
+    x_grid = np.linspace(40, 200, 150)
+    color_palette = ['#0082C3', '#E63312', '#333333', '#00A14B', '#FFD200']
 
-    results_data = []
+    results_summary = []
     for i, (idx, row) in enumerate(filtered_df.iterrows()):
-        c = colors[i % len(colors)]
-        y_d, y_w = (row['dry a'] * x_input + row['dry b']), (row['wet a'] * x_input + row['wet b'])
-        results_data.append({"name": row['model name'], "dry": y_d, "wet": y_w, "row": row})
+        clr = color_palette[i % len(color_palette)]
+        f_sec = row['dry a'] * effort_x + row['dry b']
+        f_wet = row['wet a'] * effort_x + row['wet b']
+        results_summary.append({"name": row['model name'], "dry": f_sec, "wet": f_wet, "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'], name=f"{row['model name']} (D)", line=dict(color=c, 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', marker=dict(color=c, size=10, symbol='x'), showlegend=False))
+        if view_mode != "Humide uniquement":
+            fig.add_trace(go.Scatter(x=x_grid, y=row['dry a']*x_grid + row['dry b'], name=f"{row['name']} (D)", line=dict(color=clr, width=4)))
+            if n_s > 0:
+                xt = (n_s - row['dry b']) / row['dry a']
+                if 40 <= xt <= 200:
+                    fig.add_trace(go.Scatter(x=[xt], y=[n_s], mode='markers+text', text=[f"{round(xt,1)}N"], textfont=dict(color="black", weight=800), textposition="top center", marker=dict(color=clr, size=12, symbol='x'), showlegend=False))
+
+        if view_mode != "Sec uniquement":
+            fig.add_trace(go.Scatter(x=x_grid, y=row['wet a']*x_grid + row['wet b'], name=f"{row['name']} (W)", line=dict(color=clr, width=2, dash='dot')))
 
-        if condition_view in ["Both", "Wet only"]:
-            fig.add_trace(go.Scatter(x=x_range, y=row['wet a']*x_range + row['wet b'], name=f"{row['model name']} (W)", line=dict(color=c, width=2, dash='dot')))
+    # TA DEMANDE : VISUALISATION DES LIGNES DE NORMES EN NOIR
+    if n_s > 0 and view_mode != "Humide uniquement":
+        fig.add_hline(y=n_s, line_width=2, line_color="#000", annotation_text="NORME SEC", annotation_font=dict(color="black", weight=700))
+    if n_w > 0 and view_mode != "Sec uniquement":
+        fig.add_hline(y=n_w, line_width=2, line_dash="dot", line_color="#000", annotation_text="NORME HUMIDE", annotation_font=dict(color="black", weight=700))
 
-    fig.update_layout(height=450, plot_bgcolor='white', paper_bgcolor='white', xaxis=dict(linecolor="black", gridcolor="#EEE"), yaxis=dict(linecolor="black", gridcolor="#EEE"))
+    fig.add_vline(x=effort_x, line_width=2, line_dash="dash", line_color="#555")
+    
+    # FORÇAGE NOIR SUR TOUT LE GRAPHIQUE
+    fig.update_layout(
+        height=480, plot_bgcolor='white', paper_bgcolor='white', font=dict(color="black"),
+        xaxis=dict(title=dict(text="Effort Levier [N]", font=dict(color="black", weight=700)), tickfont=dict(color="black", weight=700), linecolor="black", gridcolor="#EEE"),
+        yaxis=dict(title=dict(text="Performance [N]", font=dict(color="black", weight=700)), tickfont=dict(color="black", weight=700), linecolor="black", gridcolor="#EEE"),
+        legend=dict(font=dict(color="black", weight=700), bordercolor="black", borderwidth=1, bgcolor="white")
+    )
     st.plotly_chart(fig, use_container_width=True)
 
-    # ==========================================================================
-    # 6. DASHBOARD ANALYSE (SIMULATION RÉELLE)
-    # ==========================================================================
-    st.markdown(f"**📊 Dynamics Analysis | {v_kmh}km/h -> {round(v_ms,2)}m/s | Energy: {int(energy_total)} J**")
-    cols = st.columns(len(results_data))
+    # ==============================================================================
+    # 7. DASHBOARD ANALYSE (ALIGNE SUR TON EXEMPLE)
+    # ==============================================================================
+    st.markdown(f"**📊 Dynamics Analysis | {v_kmh}km/h | Energy: {int(e_total_j)} J**")
+    cols = st.columns(len(results_summary))
 
-    for i, res in enumerate(results_data):
+    for i, res in enumerate(results_summary):
         with cols[i]:
-            st.markdown(f"<u>**{res['name']}**</u>", unsafe_allow_html=True)
+            is_bench = (res['name'] == ref_model_name)
+            st.markdown(f"<u>**{res['name']}**</u> {'⭐' if is_bench else ''}", unsafe_allow_html=True)
             
-            # --- DRY ANALYSIS ---
-            if condition_view in ["Both", "Dry only"]:
-                st.metric("Dry Perf.", f"{round(res['dry'], 1)} N")
-                # Force totale effective cumulée AV+AR au sol
-                f_eff_d = res['dry'] * mu_dry
-                d_stop_d = energy_total / f_eff_d if f_eff_d > 0 else 0
-                st.write(f"🛑 **Stop (Dry): {round(d_stop_d, 2)} m**")
-                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</div>", unsafe_allow_html=True)
+            if view_mode != "Humide uniquement":
+                st.metric("Force Sec", f"{round(res['dry'], 1)} N", f"{round(res['dry'] - ref_f_sec, 1)} N vs Ref" if not is_bench else None)
+                d_sec = e_total_j / res['dry'] if res['dry'] > 0 else 0
+                st.write(f"🛑 **Arrêt (Sec): {round(d_sec, 2)} m**")
+                if n_s > 0:
+                    xt = (n_s - res['row']['dry b']) / res['row']['dry a']
+                    if xt > 180: st.markdown(f"<div class='alert-red'>HORS NORME SEC ({round(xt,1)}N)</div>", unsafe_allow_html=True)
                     else: st.markdown(f"<div class='check-green'>CONFORME SEC</div>", unsafe_allow_html=True)
-
-            # --- WET ANALYSIS ---
-            if condition_view in ["Both", "Wet only"]:
-                st.metric("Wet Perf.", f"{round(res['wet'], 1)} N")
-                f_eff_w = res['wet'] * mu_wet
-                d_stop_w = energy_total / f_eff_w if f_eff_w > 0 else 0
-                st.write(f"🌧️ **Stop (Wet): {round(d_stop_w, 2)} m**")
-                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</div>", unsafe_allow_html=True)
+            
+            if view_mode != "Sec uniquement":
+                st.metric("Force Wet", f"{round(res['wet'], 1)} N", f"{round(res['wet'] - ref_f_wet, 1)} N vs Ref" if not is_bench else None)
+                d_wet = e_total_j / res['wet'] if res['wet'] > 0 else 0
+                st.write(f"🌧️ **Arrêt (Wet): {round(d_wet, 2)} m**")
+                if n_w > 0:
+                    xtw = (n_w - res['row']['wet b']) / res['row']['wet a']
+                    if xtw > 180: st.markdown(f"<div class='alert-red'>HORS NORME HUMIDE</div>", unsafe_allow_html=True)
                     else: st.markdown(f"<div class='check-green'>CONFORME HUMIDE</div>", unsafe_allow_html=True)
 
+            if show_loss and view_mode == "Sec & Humide":
+                loss = ((res['dry'] - res['wet']) / res['dry'] * 100) if res['dry'] != 0 else 0
+                st.metric("Wet Loss", f"-{round(loss, 1)}%", delta_color="inverse")
+
 except Exception as e:
-    st.error(f"Erreur système : {e}")
+    st.error(f"Erreur Système : {e}")