Update src/streamlit_app.py

src/streamlit_app.py

@@ -4,47 +4,28 @@ import numpy as np
 import plotly.graph_objects as go
 import os
 
-# Configuration
+# Configuration de la page
 st.set_page_config(page_title="Brake Performance Lab", layout="wide")
 
-# --- CSS DE LA DERNIÈRE CHANCE (FORÇAGE NOIR SUR BLANC) ---
+# --- CSS FINAL : FORÇAGE NOIR SUR BLANC + COMPACTAGE ---
 st.markdown("""
     <style>
-    /* 1. Fond blanc pur partout */
     .stApp, [data-testid="stSidebar"] { background-color: #FFFFFF !important; }
-    
-    /* 2. Compactage Sidebar */
     [data-testid="stSidebar"] [data-testid="stVerticalBlock"] { gap: 0.1rem !important; padding-top: 0rem !important; }
-
-    /* 3. Force le NOIR sur TOUS les textes (Labels, Titres, Menus) */
-    * { color: #000000 !important; font-family: sans-serif; }
+    * { color: #000000 !important; }
     
-    /* 4. Fix spécifique pour les LISTES DÉROULANTES (Selectbox / Multiselect) */
-    /* On force le fond en blanc et la bordure en noir */
-    div[data-baseweb="select"] {
-        border: 2px solid #000000 !important;
-        background-color: #FFFFFF !important;
-    }
-    /* On force le texte des options dans la liste qui s'ouvre */
-    ul[role="listbox"] { background-color: #FFFFFF !important; border: 2px solid #000000 !important; }
-    li[role="option"] { background-color: #FFFFFF !important; color: #000000 !important; }
-    li[role="option"]:hover { background-color: #0082C3 !important; color: #FFFFFF !important; }
-
-    /* Fix pour les tags (modèles sélectionnés) : Fond bleu, texte blanc pour qu'ils ressortent */
-    [data-testid="stMultiSelect"] span {
-        background-color: #0082C3 !important;
-        color: #FFFFFF !important;
-    }
+    /* Dropdowns et listes */
+    div[data-baseweb="select"] { border: 2px solid #000 !important; background-color: #FFF !important; }
+    ul[role="listbox"] { background-color: #FFFFFF !important; border: 2px solid #000 !important; }
+    li[role="option"] { background-color: #FFFFFF !important; color: #000 !important; }
+    li[role="option"]:hover { background-color: #0082C3 !important; color: #FFF !important; }
 
-    /* 5. Metrics et boîtes d'analyse en bas */
-    [data-testid="column"] { 
-        padding: 8px !important; border: 2px solid #000000 !important; 
-        border-radius: 8px !important; background-color: #FFFFFF !important; 
-    }
-    [data-testid="stMetricValue"] { font-weight: 800 !important; font-size: 20px !important; }
+    /* Tags Multiselect */
+    [data-testid="stMultiSelect"] span { background-color: #0082C3 !important; color: #FFFFFF !important; }
     
-    /* On cache les indicateurs de flèches qui peuvent être gris */
-    [data-testid="stMetricDelta"] svg { display: none; }
+    /* Metrics */
+    [data-testid="column"] { border: 2px solid #000 !important; border-radius: 8px !important; padding: 8px !important; }
+    [data-testid="stMetricValue"] { font-weight: 800 !important; }
     </style>
     """, unsafe_allow_html=True)
 
@@ -52,9 +33,9 @@ st.markdown("""
 def load_data():
     current_dir = os.path.dirname(__file__)
     file_path = os.path.join(current_dir, "Brake_Lab_Test_Data.xlsx")
-    data = pd.read_excel(file_path, sheet_name='Data')
-    data.columns = data.columns.str.strip()
-    return data
+    df = pd.read_excel(file_path, sheet_name='Data')
+    df.columns = df.columns.str.strip()
+    return df
 
 try:
     df = load_data()
@@ -62,71 +43,80 @@ try:
 
     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.markdown("**SETTINGS**")
+        st.markdown("### **SETTINGS**")
         x_input = st.slider("Lever Effort [N]", 40, 200, 100)
         selected_models = st.multiselect("Models", options=all_models, default=all_models[:2])
         norm_type = st.selectbox("Norm Category", ["None", "City/Trekking", "Kids", "MTB", "Racing"])
         
+        # Définition des seuils de norme
         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
 
-        with st.expander("Options"):
-            show_loss = st.checkbox("Show Loss", value=True)
-            enable_comparison = st.checkbox("Enable Ref", value=True)
-            ref_model = st.selectbox("Ref Model", options=all_models)
-            condition_view = st.radio("View", ["Both", "Dry only", "Wet only"])
+        with st.expander("Display Options"):
+            ref_model = st.selectbox("Benchmark Ref", options=all_models, index=0)
+            condition_view = st.radio("View", ["Both", "Dry only", "Wet only"], index=0)
 
-    # --- 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:5px; border:2px solid #000; text-align:center; font-weight:bold;'>{label} BRAKING | {x_input} N</div>", unsafe_allow_html=True)
-
-    # --- GRAPHIC (FIX NOIR TOTAL) ---
+    # --- CALCULS ---
     filtered_df = df[df['model name'].isin(selected_models)]
+    row_ref = df[df['model name'] == ref_model].iloc[0]
+    ref_d = row_ref['dry a'] * x_input + row_ref['dry b']
+    ref_w = row_ref['wet a'] * x_input + row_ref['wet b']
+
+    # --- GRAPH ---
     fig = go.Figure()
     x_range = np.linspace(40, 200, 100)
     colors = ['#0082C3', '#E63312', '#333333', '#00A14B', '#FFD200']
 
-    row_ref = df[df['model name'] == ref_model].iloc[0]
-    ref_d, ref_w = row_ref['dry a']*x_input+row_ref['dry b'], row_ref['wet a']*x_input+row_ref['wet b']
-
     for i, (idx, row) in enumerate(filtered_df.iterrows()):
-        c = colors[i % len(colors)]
+        color = colors[i % len(colors)]
+        # Courbe SEC
         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)))
+            fig.add_trace(go.Scatter(x=x_range, y=row['dry a']*x_range + row['dry b'], 
+                                     name=f"{row['model name']} (Dry)", line=dict(color=color, width=4)))
+        # Courbe HUMIDE
         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')))
+            fig.add_trace(go.Scatter(x=x_range, y=row['wet a']*x_range + row['wet b'], 
+                                     name=f"{row['model name']} (Wet)", line=dict(color=color, width=2, dash='dot')))
+
+    # Ajout des lignes de NORMES (Lignes horizontales noires)
+    if n_dry > 0 and condition_view in ["Both", "Dry only"]:
+        fig.add_hline(y=n_dry, line_width=3, line_color="black", annotation_text=f"NORM DRY {n_dry}N", 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=3, line_dash="dot", line_color="black", annotation_text=f"NORM WET {n_wet}N", annotation_font=dict(color="black", size=12))
+
+    fig.add_vline(x=x_input, line_width=2, line_dash="dash", line_color="black")
 
-    # Fix pour les noms des AXES (on force le NOIR pur ici)
     fig.update_layout(
         height=450, plot_bgcolor='white', paper_bgcolor='white',
-        xaxis=dict(title=dict(text="Lever Effort [N]", font=dict(color="black", size=14, family="Arial Black")), 
-                   tickfont=dict(color="black", size=12, weight=700), linecolor="black", linewidth=2, gridcolor="#EEE"),
-        yaxis=dict(title=dict(text="Performance [N]", font=dict(color="black", size=14, family="Arial Black")), 
-                   tickfont=dict(color="black", size=12, weight=700), linecolor="black", linewidth=2, gridcolor="#EEE"),
-        legend=dict(font=dict(color="black", weight=700), bordercolor="black", borderwidth=1),
-        hovermode="x unified"
+        xaxis=dict(title="Lever Effort [N]", gridcolor="#EEE", tickfont=dict(color="black", weight=700), titlefont=dict(color="black", size=14)),
+        yaxis=dict(title="Performance [N]", gridcolor="#EEE", tickfont=dict(color="black", weight=700), titlefont=dict(color="black", size=14)),
+        legend=dict(font=dict(color="black", weight=700), bordercolor="black", borderwidth=1)
     )
     st.plotly_chart(fig, use_container_width=True)
 
-    # --- ANALYSIS ---
-    st.markdown("**ANALYSIS**")
+    # --- DASHBOARD BAS ---
+    st.markdown("### **PERFORMANCE ANALYSIS**")
     if not filtered_df.empty:
         cols = st.columns(len(filtered_df))
         for i, (idx, row) in enumerate(filtered_df.iterrows()):
             with cols[i]:
                 st.markdown(f"**{row['model name']}**")
-                d_val = round(row['dry a']*x_input + row['dry b'], 1)
-                w_val = round(row['wet a']*x_input + row['wet b'], 1)
+                d_val = row['dry a'] * x_input + row['dry b']
+                w_val = row['wet a'] * x_input + row['wet b']
                 
                 if condition_view in ["Both", "Dry only"]:
-                    st.metric("Dry", f"{d_val}N", f"{d_val-round(ref_d,1):+.1f}N Vs Ref" if enable_comparison and row['model name']!=ref_model else None)
+                    st.metric("Dry", f"{round(d_val,1)} N", f"{d_val-ref_d:+.1f} N vs Ref" if row['model name']!=ref_model else None)
                 if condition_view in ["Both", "Wet only"]:
-                    st.metric("Wet", f"{w_val}N", f"{w_val-round(ref_w,1):+.1f}N Vs Ref" if enable_comparison and row['model name']!=ref_model else None)
-                if show_loss and condition_view=="Both":
-                    loss = ((d_val - w_val) / d_val * 100) if d_val != 0 else 0
-                    st.metric("Loss", f"-{round(loss,1)}%", f"{round(w_val-d_val,1)}N vs Dry", delta_color="inverse")
+                    st.metric("Wet", f"{round(w_val,1)} N", f"{w_val-ref_w:+.1f} N vs Ref" if row['model name']!=ref_model else None)
+                
+                # Check Conformité Norme
+                if n_dry > 0:
+                    needed_dry = (n_dry - row['dry b']) / row['dry a']
+                    if needed_dry > 180: st.error("❌ DRY NON-COMPLIANT")
+                    else: st.success("✅ DRY OK")
+
 except Exception as e:
-    st.error(f"Error: {e}")
+    st.error(f"Erreur d'affichage : {e}")