Update src/streamlit_app.py

src/streamlit_app.py

@@ -5,28 +5,28 @@ import plotly.graph_objects as go
 import os
 
 # ==============================================================================
-# 1. CONFIGURATION ET STYLE CSS (CONTRASTE MAXIMUM & VISIBILITÉ INPUTS)
+# 1. CONFIGURATION ET STYLE CSS (HAUT CONTRASTE & INPUTS)
 # ==============================================================================
 st.set_page_config(page_title="Brake Performance Lab", layout="wide", page_icon="🚲")
 
 st.markdown("""
     <style>
-    /* 1.1 Fond blanc pur et texte noir global */
+    /* 1.1 Thème Global Blanc */
     .stApp, [data-testid="stSidebar"] { background-color: #FFFFFF !important; }
     * { color: #000000 !important; font-family: 'Arial', sans-serif; }
     
-    /* 1.2 Sidebar ultra-compacte */
+    /* 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 Fix pour les Listes Déroulantes (Anti-Dark Mode) */
+    /* 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 TA DEMANDE : TEXTE BLANC DANS LES INPUTS DE MASSE/VITESSE */
+    /* 1.4 INPUTS NUMÉRIQUES (Texte blanc sur fond gris foncé) */
     input[type="number"], div[data-baseweb="input"] input {
         color: #FFFFFF !important;
         background-color: #333333 !important;
@@ -34,102 +34,104 @@ st.markdown("""
         font-weight: bold !important;
     }
 
-    /* 1.5 Tags multiselect bleu Decathlon */
+    /* 1.5 Tags multiselect */
     [data-testid="stMultiSelect"] span { background-color: #0082C3 !important; color: #FFFFFF !important; }
     
-    /* 1.6 Style des boites de Dashboard Analyse */
+    /* 1.6 Dashboard Analyse */
     [data-testid="column"] { 
         padding: 12px !important; border: 2px solid #000 !important; 
         border-radius: 8px !important; background-color: #FFFFFF !important; 
         margin-bottom: 10px;
     }
-    [data-testid="stMetricValue"] { font-weight: 800 !important; font-size: 22px !important; }
     
-    /* 1.7 Alertes de conformité */
-    .alert-red { color: #B71C1C !important; font-weight: 900 !important; font-size: 12px; margin-top: 5px; border: 1px solid #B71C1C; padding: 4px; border-radius: 4px; }
-    .check-green { color: #1B5E20 !important; font-weight: 900 !important; font-size: 12px; margin-top: 5px; }
+    /* 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; }
     </style>
     """, unsafe_allow_html=True)
 
 # ==============================================================================
-# 2. CHARGEMENT DES DONNÉES EXCEL
+# 2. CHARGEMENT DES 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("Erreur : 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 : PARAMÉTRAGE COMPLET (SANS AUCUN RETRAIT)
+    # 3. SIDEBAR (NAVIGATION & PARAMÈTRES)
     # ==========================================================================
     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")
         
-        # 3.1 Effort principal
-        x_input = st.slider("🫱 Lever Effort [N]", 40, 200, 100)
-        selected_models = st.multiselect("Select Models to Display", options=all_models, default=all_models[:2])
+        # 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])
         
-        # 3.2 Normes
-        st.subheader("📋 Standard Compliance")
+        st.subheader("📋 Compliance")
         norm_type = st.selectbox("Category", ["None", "City/Trekking", "Kids", "MTB", "Racing"])
         
-        # 3.3 Simulation Freinage (Vitesse, Masse, Répartition)
         st.markdown("---")
-        st.subheader("📏 Braking Simulation")
-        v_kmh = st.number_input("Bike Speed (km/h)", value=25, min_value=1)
-        m_total = st.number_input("Total Mass (kg)", value=100, min_value=1)
-        mass_front_pct = st.slider("Mass Balance Front (%)", 0, 100, 70)
-        st.caption(f"Calculated Rear Balance: {100 - mass_front_pct}%")
         
-        # 3.4 Coefficients de frottement
-        mu_dry = st.slider("Friction Coeff (Dry)", 0.1, 1.2, 0.8)
-        mu_wet = st.slider("Friction Coeff (Wet)", 0.1, 1.2, 0.4)
-
-        # 3.5 Options d'affichage (Benchmark & Loss)
-        st.markdown("---")
-        with st.expander("🔍 Display Options"):
-            show_loss = st.checkbox("Show Wet Loss (%)", value=True)
-            enable_comparison = st.checkbox("Enable Reference (Vs Ref)", value=True)
-            ref_model = st.selectbox("Benchmark Reference", options=all_models)
+        # Simulation Dynamique en Expandable
+        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. CALCULS LOGIQUES (NORMES & RÉFÉRENCE)
+    # 4. LOGIQUE DE CALCULS PHYSIQUES (UNITÉS S.I.)
     # ==========================================================================
-    # 4.1 Seuils de Normes
+    # 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)
+    
+    # 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)))
+    
+    # 4.4 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
 
-    # 4.2 Valeurs de référence pour les deltas
+    # 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']
 
-    # ==========================================================================
-    # 5. HEADER : DIAGNOSTIC VISUEL
-    # ==========================================================================
+    # 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: 15px;'><span style='font-weight:900; font-size:16px;'>{label} BRAKING | Applied Effort: {x_input} N</span></div>", unsafe_allow_html=True)
+    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)
 
     # ==========================================================================
-    # 6. GRAPHIQUE INTERACTIF (INTERSECTIONS X ET COURBES)
+    # 5. GRAPHIQUE (DYNAMIQUE)
     # ==========================================================================
     filtered_df = df[df['model name'].isin(selected_models)]
     fig = go.Figure()
@@ -142,79 +144,50 @@ try:
         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})
         
-        # Courbes Dry
         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+text', text=[f"{round(xt,1)}N"], textfont=dict(color="black", weight=700), textposition="top center", marker=dict(color=c, size=10, symbol='x'), showlegend=False))
+                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))
 
-        # Courbes Wet
         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')))
-            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", weight=700), textposition="bottom center", marker=dict(color=c, size=10, symbol='circle-open'), showlegend=False))
-
-    # Lignes de Normes Horizontales
-    if n_dry > 0 and (condition_view in ["Both", "Dry only"]):
-        fig.add_hline(y=n_dry, line_width=3, line_color="#000", annotation_text="Norm Dry")
-    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="#000", annotation_text="Norm Wet")
-
-    # Ligne d'effort vertical
-    fig.add_vline(x=x_input, line_width=2, line_dash="dash", line_color="#000")
-    
-    # Mise en page (Fix Plotly title)
-    fig.update_layout(height=480, plot_bgcolor='white', paper_bgcolor='white', hovermode="x unified",
-        xaxis=dict(title=dict(text="Lever Effort [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"))
+
+    fig.update_layout(height=450, plot_bgcolor='white', paper_bgcolor='white', xaxis=dict(linecolor="black", gridcolor="#EEE"), yaxis=dict(linecolor="black", gridcolor="#EEE"))
     st.plotly_chart(fig, use_container_width=True)
 
     # ==========================================================================
-    # 7. DASHBOARD : ANALYSE DÉTAILLÉE & SIMULATION STOP DISTANCE
+    # 6. DASHBOARD ANALYSE (SIMULATION RÉELLE)
     # ==========================================================================
-    st.markdown(f"**📊 System Performance Analysis | Speed: {v_kmh}km/h | Mass: {m_total}kg**")
-    
+    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))
-    v_ms = v_kmh / 3.6
-    energy = 0.5 * m_total * (v_ms**2)
-    r_av, r_ar = mass_front_pct / 100, (100 - mass_front_pct) / 100
 
     for i, res in enumerate(results_data):
         with cols[i]:
-            is_ref = (res['name'] == ref_model)
-            st.markdown(f"<u>**{res['name']}**</u> {'⭐' if is_ref else ''}", unsafe_allow_html=True)
+            st.markdown(f"<u>**{res['name']}**</u>", unsafe_allow_html=True)
             
-            # 7.1 Analyse SEC (Force cumulée AV+AR)
+            # --- DRY ANALYSIS ---
             if condition_view in ["Both", "Dry only"]:
-                d_p = round(res['dry'], 1)
-                st.metric("Dry Perf.", f"{d_p} N", f"{round(d_p - ref_d_val, 1)} N vs Ref" if enable_comparison and not is_ref else None)
-                f_tot_d = (res['dry'] * mu_dry * r_av) + (res['dry'] * mu_dry * r_ar)
-                st.write(f"🛑 **Stop (Dry): {round(energy/f_tot_d, 2) if f_tot_d > 0 else 0} m**")
+                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 ({round(xt,1)}N)</div>", unsafe_allow_html=True)
-                    else: st.markdown(f"<div class='check-green'>✅ Conforme Sec</div>", unsafe_allow_html=True)
-            
-            # 7.2 Analyse HUMIDE (Force cumulée AV+AR)
+                    if xt > 180: st.markdown(f"<div class='alert-red'>NON CONFORME SEC</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"]:
-                w_p = round(res['wet'], 1)
-                st.metric("Wet Perf.", f"{w_p} N", f"{round(w_p - ref_w_val, 1)} N vs Ref" if enable_comparison and not is_ref else None)
-                f_tot_w = (res['wet'] * mu_wet * r_av) + (res['wet'] * mu_wet * r_ar)
-                st.write(f"🌧️ **Stop (Wet): {round(energy/f_tot_w, 2) if f_tot_w > 0 else 0} m**")
+                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 ({round(xtw,1)}N)</div>", unsafe_allow_html=True)
-                    else: st.markdown(f"<div class='check-green'>✅ Conforme Humide</div>", unsafe_allow_html=True)
-
-            # 7.3 Perte d'efficacité
-            if show_loss and condition_view == "Both":
-                loss = ((res['dry'] - res['wet']) / res['dry'] * 100) if res['dry'] != 0 else 0
-                st.metric("Wet Loss", f"-{round(loss, 1)}%", f"{round(res['wet']-res['dry'], 1)} N abs.", delta_color="inverse")
+                    if xtw > 180: st.markdown(f"<div class='alert-red'>NON CONFORME HUMIDE</div>", unsafe_allow_html=True)
+                    else: st.markdown(f"<div class='check-green'>CONFORME HUMIDE</div>", unsafe_allow_html=True)
 
 except Exception as e:
-    st.error(f"Une erreur est survenue : {e}")
+    st.error(f"Erreur système : {e}")