Update app.py

app.py

@@ -1,4 +1,4 @@
-# app.py – Titanic Data Explorer – Gradio One-Page Edition (Glossy Night Sky)
+# app.py – Titanic Data Explorer – Night Sky Edition (Gradio 4.44 compatibel)
 import gradio as gr
 import pandas as pd
 import numpy as np
@@ -7,9 +7,8 @@ import plotly.express as px
 import plotly.graph_objects as go
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder
-from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score, roc_curve
+from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score
 from sklearn.ensemble import RandomForestClassifier
-from sklearn.linear_model import LogisticRegression
 
 # =======================
 # DATA
@@ -20,13 +19,11 @@ def load_data(path="Titanic-Dataset.csv"):
     df = pd.read_csv(path)
     df.columns = [c.lower().strip() for c in df.columns]
 
-    # kolommen check
     req = {"survived", "pclass", "sex", "age", "sibsp", "parch", "fare", "embarked"}
     miss = req - set(df.columns)
     if miss:
         raise ValueError(f"Ontbrekende kolommen: {miss}")
 
-    # missende waarden vullen
     for col in df.columns:
         if df[col].isna().any():
             if df[col].dtype == "object":
@@ -73,13 +70,15 @@ def plot_survival_heatmap(x):
     return make_plot(f, "Overlevingspercentage per Geslacht en Klasse")
 
 def plot_density_age_fare(x):
-    f = px.density_contour(x, x="age", y="fare", color="status", marginal_x="histogram", marginal_y="histogram")
+    f = px.density_contour(x, x="age", y="fare", color="status",
+                           marginal_x="histogram", marginal_y="histogram")
     return make_plot(f, "Leeftijd vs Ticketprijs (dichtheidsverdeling)")
 
 def plot_bubble_family_fare(x):
     f = px.scatter(
         x, x="fare", y="family_size", size="age", color="status",
-        hover_data=["sex", "pclass"], size_max=40, color_discrete_sequence=px.colors.qualitative.Set3
+        hover_data=["sex", "pclass"], size_max=40,
+        color_discrete_sequence=px.colors.qualitative.Set3
     )
     return make_plot(f, "Bubble Chart — Fare vs Family Size vs Age")
 
@@ -123,7 +122,7 @@ def train_and_evaluate(x):
     return f"🎯 **Nauwkeurigheid:** {acc:.2%} | **ROC AUC:** {auc:.3f}", fig_cm
 
 # =======================
-# GRADIO INTERFACE
+# DASHBOARD
 # =======================
 def dashboard():
     acc_text, cm_fig = train_and_evaluate(df)
@@ -152,7 +151,7 @@ body {
   color: #EAF2FF;
 }
 .gradio-container {
-  background: rgba(10, 16, 26, 0.7);
+  background: rgba(10, 16, 26, 0.75);
 }
 .gradio-container::before {
   content: "";
@@ -166,8 +165,18 @@ body {
 .kpi .value {font-size:1.6rem; font-weight:800; color:#FFD26A;}
 .kpi .label {font-size:0.9rem; color:#C4D7F0;}
 .section-title {font-size:1.3rem; font-weight:800; color:#FFD26A; margin-top:12px;}
+.dataframe-container {
+  height: 320px;
+  overflow-y: auto;
+  background: rgba(20,28,42,0.7);
+  border-radius: 10px;
+  padding: 5px;
+}
 """
 
+# =======================
+# UI
+# =======================
 with gr.Blocks(css=CUSTOM_CSS, theme=gr.themes.Soft(primary_hue="blue", secondary_hue="blue")) as demo:
     gr.HTML("<h1 style='text-align:center;margin-top:10px;'>🛳️ Titanic Data Explorer – Night Sky Edition</h1>")
     gr.HTML("<p style='text-align:center;color:#C4D7F0;'>Interactieve visualisatie & machine learning analyse</p>")
@@ -196,7 +205,8 @@ with gr.Blocks(css=CUSTOM_CSS, theme=gr.themes.Soft(primary_hue="blue", secondar
     fig_cm = gr.Plot(label="Confusion Matrix")
 
     gr.HTML("<div class='section-title'>🗂️ Data voorbeeld</div>")
-    table = gr.Dataframe(height=300)
+    with gr.Column(elem_classes=["dataframe-container"]):
+        table = gr.Dataframe(interactive=False)
 
     def update_dashboard():
         return dashboard()