Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,89 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.graph_objects as go
+import gdown
+
+# Configuration de la page
+st.set_page_config(page_title="Brake_Lab_Test", layout="wide")
+st.title("🔬 Lab_test_visual : Analyse de Performance")
+
+# --- PARAMÈTRES GOOGLE DRIVE ---
+# ID extrait de votre lien : 16tPsyYoe8O3LGM9FGOx5B-Je3S7ggVqUZPrs9XYDUHA
+file_id = '16tPsyYoe8O3LGM9FGOx5B-Je3S7ggVqUZPrs9XYDUHA'
+url = f'https://drive.google.com/uc?id={file_id}'
+output = 'Brake_Lab_Test_Data.xlsx'
+
+@st.cache_data(ttl=300) # Recharge les données toutes les 5 minutes
+def load_data():
+    gdown.download(url, output, quiet=True)
+    return pd.read_excel(output, sheet_name='Data')
+
+# --- CHARGEMENT ET INTERFACE ---
+try:
+    df = load_data()
+
+    # Barre latérale : Sélection de la valeur X (40 à 200)
+    st.sidebar.header("⚙️ Configuration")
+    x_input = st.sidebar.slider("Valeur cible (X)", 40, 200, 100)
+    
+    st.write(f"Comparaison des modèles pour une valeur d'entrée de **{x_input}**")
+
+    # --- GRAPHIQUE DES RÉGRESSIONS ---
+    fig = go.Figure()
+    x_range = np.linspace(40, 200, 100) # Plage de 40 à 200
+
+    # Couleurs pour différencier les modèles
+    colors = ['#636EFA', '#EF553B', '#00CC96', '#AB63FA', '#FFA15A', '#19D3F3']
+
+    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']
+
+        # 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')))
+
+    # Ligne verticale de l'entrée sélectionnée
+    fig.add_vline(x=x_input, line_width=2, line_dash="dash", line_color="black")
+
+    fig.update_layout(
+        height=600,
+        xaxis_title="Entrée (X)",
+        yaxis_title="Performance (Y)",
+        legend_title="Modèles",
+        hovermode="x unified"
+    )
+
+    st.plotly_chart(fig, use_container_width=True)
+
+    # --- TABLEAU RÉCAPITULATIF ---
+    st.subheader(f"📊 Performances calculées à 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
+        
+        recap_data.append({
+            "Modèle": row['model name'],
+            "Résultat Sec": round(val_dry, 2),
+            "Résultat Humide": round(val_wet, 2),
+            "Perte d'efficacité (%)": f"{round(perte, 1)}%"
+        })
+    
+    st.table(pd.DataFrame(recap_data))
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+except Exception as e:
+    st.error("Erreur de connexion aux données.")
+    st.write("Vérifiez que votre fichier Excel sur Drive a bien un onglet nommé 'Data' et les colonnes : 'model name', 'dry a', 'dry b', 'wet a', 'wet b'.")
+    st.info(f"Détails : {e}")