Update src/streamlit_app.py

src/streamlit_app.py

@@ -4,87 +4,131 @@ import numpy as np
 import plotly.graph_objects as go
 import os
 
-# Configuration de la page
-st.set_page_config(page_title="Brake_Lab_Test", layout="wide")
-st.title("🔬 Lab_test_visual : Analyse de Performance")
+# Page Configuration
+st.set_page_config(page_title="Brake Performance Lab", layout="wide", page_icon="🚲")
 
-# --- CHARGEMENT ---
 @st.cache_data
 def load_data():
-    # Détection du chemin du fichier dans le dossier /src
     current_dir = os.path.dirname(__file__)
     file_path = os.path.join(current_dir, "Brake_Lab_Test_Data.xlsx")
-    
-    # On utilise 'Data' avec la majuscule ici
+    # Reading 'Data' sheet as specified
     data = pd.read_excel(file_path, sheet_name='Data')
-    
-    # Nettoyage des noms de colonnes (enlève les espaces avant/après)
     data.columns = data.columns.str.strip()
     return data
 
 try:
     df = load_data()
 
-    # Barre latérale pour le réglage X
-    st.sidebar.header("⚙️ Configuration")
-    x_input = st.sidebar.slider("Valeur cible (X)", 40, 200, 100)
-    
-    st.info(f"Visualisation des régressions linéaires pour X entre 40 et 200")
+    # --- SIDEBAR (ADVANCED FILTERS) ---
+    with st.sidebar:
+        # Decathlon International Logo
+        st.image("https://upload.wikimedia.org/wikipedia/commons/thumb/0/08/Decathlon_Logo.svg/1280px-Decathlon_Logo.svg.png", width=200)
+        st.title("⚙️ Settings")
+        
+        # 1. Lever Effort Input
+        x_input = st.slider("🫱 Lever Effort (N)", 40, 200, 100, help="Force applied to the brake lever in Newtons")
+        
+        st.markdown("---")
+        st.subheader("🔍 Graph Filters")
+        
+        # 2. Model Selection
+        all_models = df['model name'].unique().tolist()
+        selected_models = st.multiselect(
+            "Select Models",
+            options=all_models,
+            default=all_models[:2] if len(all_models) > 1 else all_models
+        )
+        
+        # 3. Condition Selection
+        condition_view = st.radio(
+            "Conditions to display",
+            ["Both", "Dry only", "Wet only"],
+            index=0
+        )
 
-    # --- CRÉATION DU GRAPHIQUE ---
-    fig = go.Figure()
-    x_range = np.linspace(40, 200, 100)
-    
-    # Palette de couleurs Decathlon / Sport
-    colors = ['#0082C3', '#E63312', '#333333', '#FFD200', '#00A14B']
+    # --- DIAGNOSTIC LOGIC ---
+    if x_input < 70:
+        label, color_alert = "❄️ LIGHT BRAKING", "#a1c4fd" # Light Blue
+    elif 70 <= x_input <= 110:
+        label, color_alert = "⚖️ MODERATE BRAKING", "#ffdb58" # Yellow
+    else:
+        label, color_alert = "🔥 POWERFUL BRAKING", "#ff4b4b" # Red
 
-    for i, (index, row) in enumerate(df.iterrows()):
-        color = colors[i % len(colors)]
-        model = row['model name']
-        
-        # Calcul des droites : y = ax + b
-        y_dry = row['dry a'] * x_range + row['dry b']
-        y_wet = row['wet a'] * x_range + row['wet b']
+    # Diagnostic Header
+    st.markdown(f"""
+        <div style="background-color:{color_alert}; padding:15px; border-radius:10px; text-align:center; border: 1px solid #ddd;">
+            <h2 style="color:black; margin:0;">{label}</h2>
+            <p style="color:black; font-weight:bold; margin:5px 0 0 0;">Current Lever Effort: {x_input} N</p>
+        </div>
+    """, unsafe_allow_html=True)
 
-        # Ajout Courbe SEC (Pleine)
-        fig.add_trace(go.Scatter(x=x_range, y=y_dry, mode='lines', 
-                                 name=f"{model} (Sec)", line=dict(color=color, width=3)))
-        
-        # Ajout Courbe HUMIDE (Pointillée)
-        fig.add_trace(go.Scatter(x=x_range, y=y_wet, mode='lines', 
-                                 name=f"{model} (Wet)", line=dict(color=color, width=2, dash='dot')))
+    # --- PLOT AREA ---
+    col1, col2 = st.columns([3, 1])
 
-    # Ligne verticale pour la valeur sélectionnée
-    fig.add_vline(x=x_input, line_width=2, line_dash="dash", line_color="red")
+    with col1:
+        filtered_df = df[df['model name'].isin(selected_models)]
+        
+        fig = go.Figure()
+        x_range = np.linspace(40, 200, 100)
+        # Decathlon-friendly color palette
+        colors = ['#0082C3', '#E63312', '#333333', '#00A14B', '#FFD200', '#AB63FA']
 
-    fig.update_layout(
-        height=600,
-        xaxis_title="Entrée (Pression/Vitesse)",
-        yaxis_title="Performance (Coefficient)",
-        legend_title="Modèles & Conditions",
-        hovermode="x unified"
-    )
+        for i, (index, row) in enumerate(filtered_df.iterrows()):
+            color = colors[i % len(colors)]
+            
+            # Linear Regression Calculation: y = ax + b
+            y_dry = row['dry a'] * x_range + row['dry b']
+            y_wet = row['wet a'] * x_range + row['wet b']
 
-    st.plotly_chart(fig, use_container_width=True)
+            # DRY Trace
+            if condition_view in ["Both", "Dry only"]:
+                fig.add_trace(go.Scatter(x=x_range, y=y_dry, mode='lines', 
+                                         name=f"{row['model name']} (Dry)", 
+                                         line=dict(color=color, width=4),
+                                         hovertemplate="Effort: %{x}N<br>Perf: %{y:.2f}"))
+            
+            # WET Trace
+            if condition_view in ["Both", "Wet only"]:
+                fig.add_trace(go.Scatter(x=x_range, y=y_wet, mode='lines', 
+                                         name=f"{row['model name']} (Wet)", 
+                                         line=dict(color=color, width=2, dash='dot'),
+                                         hovertemplate="Effort: %{x}N<br>Perf: %{y:.2f}"))
 
-    # --- TABLEAU RÉCAPITULATIF ---
-    st.subheader(f"📊 Performances au point X = {x_input}")
-    
-    recap_data = []
-    for index, row in df.iterrows():
-        val_dry = row['dry a'] * x_input + row['dry b']
-        val_wet = row['wet a'] * x_input + row['wet b']
-        perte = ((val_dry - val_wet) / val_dry) * 100 if val_dry != 0 else 0
+        # Vertical line for current effort
+        fig.add_vline(x=x_input, line_width=3, line_dash="dash", line_color="black")
         
-        recap_data.append({
-            "Modèle": row['model name'],
-            "Résultat Sec": round(val_dry, 3),
-            "Résultat Humide": round(val_wet, 3),
-            "Perte d'efficacité": f"{round(perte, 1)}%"
-        })
-    
-    st.table(pd.DataFrame(recap_data))
+        fig.update_layout(
+            xaxis_title="Lever Effort (N)",
+            yaxis_title="Braking Performance",
+            legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
+            plot_bgcolor='white',
+            hovermode="x unified"
+        )
+        st.plotly_chart(fig, use_container_width=True)
+
+    with col2:
+        st.subheader("💡 Analysis")
+        if not filtered_df.empty:
+            for index, row in filtered_df.iterrows():
+                val_dry = row['dry a'] * x_input + row['dry b']
+                val_wet = row['wet a'] * x_input + row['wet b']
+                # Drop in efficiency calculation
+                loss = ((val_dry - val_wet) / val_dry) * 100 if val_dry != 0 else 0
+                
+                st.metric(label=row['model name'], 
+                          value=f"{round(val_dry, 2)}", 
+                          delta=f"-{round(loss, 1)}% Wet Loss", 
+                          delta_color="inverse")
+                st.write(f"**Wet Perf:** {round(val_wet, 2)}")
+                st.markdown("---")
+        else:
+            st.info("Please select at least one model in the sidebar.")
+
+    # --- DATA EXPORT ---
+    with st.expander("📂 View Raw Data & Export"):
+        st.dataframe(filtered_df, use_container_width=True)
+        csv = filtered_df.to_csv(index=False).encode('utf-8')
+        st.download_button("📥 Download as CSV", csv, "brake_test_report.csv", "text/csv")
 
 except Exception as e:
-    st.error(f"Erreur de lecture : {e}")
-    st.warning("Vérifiez que les colonnes 'model name', 'dry a', 'dry b', 'wet a', 'wet b' sont bien présentes dans l'onglet 'Data'.")
+    st.error(f"Technical Error: {e}")