Update app.py

app.py

@@ -1,4 +1,3 @@
-
 import gradio as gr
 import numpy as np
 import pandas as pd
@@ -8,7 +7,7 @@ from sklearn.preprocessing import StandardScaler
 from sklearn.decomposition import PCA
 
 # ======================
-# Labels (NL) voor features
+# NL labels voor features
 # ======================
 FEATURE_LABELS = {
     "age": "Leeftijd",
@@ -54,7 +53,7 @@ Doe alsof je een arts bent en kies links bovenin een waarde, bijvoorbeeld **chol
 """
 
 # ======================
-# Data
+# Data helpers
 # ======================
 def load_diabetes_df():
     d = datasets.load_diabetes()
@@ -64,8 +63,8 @@ def load_diabetes_df():
     df["target"] = y
     return df
 
-# Overzichtstabel (gemiddelde & % boven/onder mean)
 def compute_overview_table(df: pd.DataFrame):
+    """Gemiddelde + % boven/onder gemiddelde voor kernmetingen (gestandaardiseerd)."""
     keys = ["bmi","bp","s1","s2","s3","s4","s5","s6"]
     rows = []
     for k in keys:
@@ -73,7 +72,7 @@ def compute_overview_table(df: pd.DataFrame):
         if vals.size == 0:
             continue
         mean = float(vals.mean())
-        pct_above = float((vals > 0).mean() * 100.0)  # standardised: 0 ≈ overall mean
+        pct_above = float((vals > 0).mean() * 100.0)  # 0 ≈ globaal gemiddelde
         pct_below = float((vals < 0).mean() * 100.0)
         rows.append({
             "Meting": FEATURE_LABELS.get(k, k),
@@ -90,9 +89,16 @@ def compute_overview_table(df: pd.DataFrame):
     return table, note
 
 # ======================
-# Kern: PCA biplot
+# PCA biplot kernel
 # ======================
-def pca_biplot(color_label="BMI (Body Mass Index)", arrow_scale=2.0, point_size=32, alpha=0.85, n_components=10, standardize=True):
+def pca_biplot(
+    color_label="BMI (Body Mass Index)",
+    arrow_scale=2.0,
+    point_size=32,
+    alpha=0.85,
+    n_components=10,
+    standardize=True,
+):
     df = load_diabetes_df()
     overview_df, overview_note = compute_overview_table(df)
 
@@ -126,8 +132,11 @@ def pca_biplot(color_label="BMI (Body Mass Index)", arrow_scale=2.0, point_size=
     for i, key in enumerate(feature_keys):
         x_arrow = loadings[i, 0] * arrow_scale
         y_arrow = loadings[i, 1] * arrow_scale
-        ax.arrow(0, 0, x_arrow, y_arrow, head_width=0.05, head_length=0.08, fc="k", ec="k", length_includes_head=True)
-        ax.text(x_arrow * 1.08, y_arrow * 1.08, FEATURE_LABELS.get(key, key), fontsize=9, ha="center", va="center")
+        ax.arrow(0, 0, x_arrow, y_arrow, head_width=0.05, head_length=0.08,
+                 fc="k", ec="k", length_includes_head=True)
+        ax.text(x_arrow * 1.08, y_arrow * 1.08,
+                FEATURE_LABELS.get(key, key),
+                fontsize=9, ha="center", va="center")
 
     ax.axhline(0, color="grey", linewidth=0.6, linestyle=":")
     ax.axvline(0, color="grey", linewidth=0.6, linestyle=":")
@@ -201,7 +210,7 @@ with gr.Blocks(title="PCA Biplot — Diabetes (NL labels)") as demo:
             n_components = gr.Slider(2, 10, value=10, step=1, label="Aantal PCA-componenten (voor variatieplot)")
             standardize = gr.Checkbox(value=True, label="Standaardiseer metingen (aanbevolen)")
             run_btn = gr.Button("Update visualisaties")
-            gr.HTML('<div class="callout smallnote">💡 *Tip:* kies links een meting (bijv. BMI of cholesterol) en klik daarna op <b>Update visualisaties</b>.</div>')
+            gr.HTML('<div class="callout smallnote">💡 <b>Tip:</b> kies links een meting (bijv. BMI of cholesterol) en klik daarna op <b>Update visualisaties</b>.</div>')
         with gr.Column(scale=2):
             plot_biplot = gr.Plot(label="PCA-biplot — punten + pijlen")
             plot_expl = gr.Plot(label="Uitlegvariantie per component")
@@ -212,8 +221,14 @@ with gr.Blocks(title="PCA Biplot — Diabetes (NL labels)") as demo:
             overview_note_md = gr.Markdown()
 
     inputs = [color_feat, arrow_scale, point_size, alpha, n_components, standardize]
-    run_btn.click(fn=pca_biplot, inputs=inputs, outputs=[plot_biplot, plot_expl, table, summary, overview_tbl, overview_note_md])
-    demo.load(fn=pca_biplot, inputs=inputs, outputs=[plot_biplot, plot_expl, table, summary, overview_tbl, overview_note_md])
+    run_btn.click(fn=pca_biplot, inputs=inputs,
+                  outputs=[plot_biplot, plot_expl, table, summary, overview_tbl, overview_note_md])
+    demo.load(fn=pca_biplot, inputs=inputs,
+              outputs=[plot_biplot, plot_expl, table, summary, overview_tbl, overview_note_md])
 
 if __name__ == "__main__":
+    # Belangrijk voor Spaces: queue + ssr_mode uit
     demo.queue().launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False, show_api=False)
+
+
+