Update src/streamlit_app.py

src/streamlit_app.py

@@ -7,25 +7,15 @@ import os
 # Page Configuration
 st.set_page_config(page_title="Brake Performance Lab", layout="wide", page_icon="🚲")
 
-# Style CSS pour réduire la police et compacter l'affichage
+# Style CSS
 st.markdown("""
     <style>
-    .small-font {
-        font-size:12px !important;
-        color: black !important;
-    }
-    [data-testid="stMetricValue"] {
-        font-size: 20px !important;
-        color: black !important;
-    }
-    [data-testid="stMetricDelta"] {
-        font-size: 13px !important;
-    }
-    [data-testid="column"] {
-        padding: 5px !important;
-        border: 1px solid #f0f2f6;
-        border-radius: 5px;
-    }
+    .small-font { font-size:12px !important; color: black !important; }
+    [data-testid="stMetricValue"] { font-size: 18px !important; color: black !important; }
+    [data-testid="column"] { padding: 5px !important; border: 1px solid #000000; border-radius: 5px; }
+    .alert-red { color: #ff4b4b; font-weight: bold; font-size: 12px; margin-top: 5px; }
+    .check-green { color: #00A14B; font-weight: bold; font-size: 11px; }
+    h1, h2, h3, h4, p, span { color: black !important; }
     </style>
     """, unsafe_allow_html=True)
 
@@ -41,41 +31,35 @@ 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")
         x_input = st.slider("🫱 Lever Effort [N]", 40, 200, 100)
         st.markdown("---")
-        with st.expander("🔍 Display Options"):
-            show_loss = st.checkbox("Show Wet Loss (%)", value=True)
-            enable_comparison = st.checkbox("Enable Ref. Comparison", value=False)
-            ref_model = st.selectbox("Reference Model", options=all_models)
-            condition_view = st.radio("Conditions to display", ["Both", "Dry only", "Wet only"], index=0)
+        selected_models = st.multiselect("Select Models to Display", options=all_models, default=all_models[:2])
         st.markdown("---")
-        selected_models = st.multiselect("Select Models", options=all_models, default=all_models[:2])
-
-    # --- DIAGNOSTIC HEADER (COMPACT) ---
-    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.subheader("📋 Standard Compliance")
+        norm_type = st.selectbox("Category", ["None", "City/Trekking", "Kids", "MTB", "Racing"])
+        
+        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
 
-    st.markdown(f"""
-        <div style="background-color:{color_alert}; padding:5px; border-radius:8px; text-align:center; border: 1.5px solid #333; margin-bottom: 10px;">
-            <span style="color:black; font-weight:bold; font-size:14px;">{label} | Effort: {round(float(x_input), 1)} N</span>
-        </div>
-    """, unsafe_allow_html=True)
+        with st.expander("🔍 Display Options"):
+            show_loss = st.checkbox("Show Wet Loss Analysis", value=True)
+            enable_comparison = st.checkbox("Enable Reference Comparison", value=True)
+            ref_model = st.selectbox("Reference Model", options=all_models)
+            condition_view = st.radio("Conditions", ["Both", "Dry only", "Wet only"], index=0)
 
     # --- GRAPHIC AREA ---
     filtered_df = df[df['model name'].isin(selected_models)]
     fig = go.Figure()
-    x_range = np.linspace(40, 200, 100)
+    x_range = np.linspace(40, 200, 150)
     colors = ['#0082C3', '#E63312', '#333333', '#00A14B', '#FFD200']
 
-    # Reference data
     row_ref = df[df['model name'] == ref_model].iloc[0]
     ref_dry_val = row_ref['dry a'] * x_input + row_ref['dry b']
     ref_wet_val = row_ref['wet a'] * x_input + row_ref['wet b']
@@ -83,56 +67,64 @@ try:
     comparison_results = []
     for i, (index, row) in enumerate(filtered_df.iterrows()):
         color = colors[i % len(colors)]
-        y_dry_val = row['dry a'] * x_input + row['dry b']
-        y_wet_val = row['wet a'] * x_input + row['wet b']
-        comparison_results.append({"name": row['model name'], "dry": y_dry_val, "wet": y_wet_val})
+        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_plot_dry = row['dry a'] * x_range + row['dry b']
-        y_plot_wet = row['wet a'] * x_range + row['wet b']
-
         if condition_view in ["Both", "Dry only"]:
-            fig.add_trace(go.Scatter(x=x_range, y=y_plot_dry, 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:
+                x_t = (n_dry - row['dry b']) / row['dry a']
+                if x_t <= 200: # On ne trace que si c'est visible
+                    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))
+
         if condition_view in ["Both", "Wet only"]:
-            fig.add_trace(go.Scatter(x=x_range, y=y_plot_wet, 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=2, dash='dot')))
+            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))
+
+    if n_dry > 0 and condition_view in ["Both", "Dry only"]:
+        fig.add_hline(y=n_dry, line_width=2, line_color="#000000", annotation_text=f"Norm Dry: {n_dry}N")
+    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="#000000", annotation_text=f"Norm Wet: {n_wet}N")
 
-    fig.add_vline(x=x_input, line_width=2, line_dash="dash", line_color="black")
-    fig.update_layout(height=450, xaxis_title="Lever Effort [N]", yaxis_title="Performance [N]", font=dict(color="black"), plot_bgcolor='white', paper_bgcolor='white', hovermode="x unified", legend=dict(font=dict(size=10, color="black"), bordercolor="black", borderwidth=1))
-    fig.update_xaxes(showline=True, linewidth=2, linecolor='black', gridcolor='#EEEEEE')
-    fig.update_yaxes(showline=True, linewidth=2, linecolor='black', gridcolor='#EEEEEE')
-    
+    fig.update_layout(height=450, xaxis_title="Lever Effort [N]", yaxis_title="Performance [N]", font=dict(color="#000000"), plot_bgcolor='white', paper_bgcolor='white', hovermode="x unified")
     st.plotly_chart(fig, use_container_width=True)
 
-    # --- ANALYSIS DASHBOARD (BOTTOM) ---
-    st.markdown(f"<p class='small-font'><b>📊 Dashboard Analysis [N]</b> | Reference: {ref_model}</p>", unsafe_allow_html=True)
-    
+    # --- ANALYSIS DASHBOARD ---
     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; margin-bottom:0;'>{res['name']} {'⭐' if is_ref else ''}</p>", unsafe_allow_html=True)
+                st.markdown(f"<p style='font-size:13px; font-weight:bold; color:black; margin-bottom:10px;'>{res['name']}</p>", unsafe_allow_html=True)
+                
+                compliance_issue = False
                 
-                if condition_view in ["Both", "Dry only"]:
-                    d_val = round(res['dry'], 1)
-                    if enable_comparison and not is_ref:
-                        diff = d_val - round(ref_dry_val, 1)
-                        pct = (diff / ref_dry_val * 100) if ref_dry_val != 0 else 0
-                        st.metric("Dry Performance", f"{d_val} N", f"{diff:+.1f} N ({pct:+.1f}%)")
+                # Check Dry Norm
+                if n_dry > 0 and condition_view in ["Both", "Dry only"]:
+                    x_target = (n_dry - res['row']['dry b']) / res['row']['dry a']
+                    st.metric("Dry Perf.", f"{round(res['dry'],1)} N")
+                    if x_target > 180:
+                        st.markdown(f"<div class='alert-red'>❌ NON CONFORME SEC ({norm_type})<br>Effort requis: {round(x_target,1)}N > 180N</div>", unsafe_allow_html=True)
+                        compliance_issue = True
                     else:
-                        st.metric("Dry Performance", f"{d_val} N")
+                        st.markdown(f"<div class='check-green'>✅ Conforme Sec ({round(x_target,1)}N)</div>", unsafe_allow_html=True)
                 
-                if condition_view in ["Both", "Wet only"]:
-                    w_val = 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 Performance", f"{w_val} N", f"{diff_w:+.1f} N ({pct_w:+.1f}%)")
+                # Check Wet Norm
+                if n_wet > 0 and condition_view in ["Both", "Wet only"]:
+                    x_target_w = (n_wet - res['row']['wet b']) / res['row']['wet a']
+                    st.metric("Wet Perf.", f"{round(res['wet'],1)} N")
+                    if x_target_w > 180:
+                        st.markdown(f"<div class='alert-red'>❌ NON CONFORME HUMIDE ({norm_type})<br>Effort requis: {round(x_target_w,1)}N > 180N</div>", unsafe_allow_html=True)
+                        compliance_issue = True
                     else:
-                        st.metric("Wet Performance", f"{w_val} N")
+                        st.markdown(f"<div class='check-green'>✅ Conforme Humide ({round(x_target_w,1)}N)</div>", unsafe_allow_html=True)
 
                 if show_loss and condition_view == "Both":
-                    loss = ((res['dry'] - res['wet']) / res['dry']) * 100 if res['dry'] != 0 else 0
-                    st.markdown(f"<p style='color:red; font-size:11px; margin-top:-10px;'>Wet Loss: -{round(loss, 1)}%</p>", unsafe_allow_html=True)
+                    loss_pct = ((res['dry'] - res['wet']) / res['dry'] * 100) if res['dry'] != 0 else 0
+                    st.metric("Wet Loss", f"-{round(loss_pct, 1)}%")
 
 except Exception as e:
-    st.error(f"Error: {e}")
+    st.error(f"Error: {e}")