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app.py

@@ -8,7 +8,11 @@ from sklearn.preprocessing import StandardScaler
 from sklearn.decomposition import PCA
 import plotly.graph_objects as go
 import plotly.express as px
+import time
 
+# ======================
+# NL labels
+# ======================
 FEATURE_LABELS = {
     "age": "Leeftijd",
     "sex": "Geslacht",
@@ -24,109 +28,385 @@ FEATURE_LABELS = {
 }
 LABEL_TO_KEY = {v: k for k, v in FEATURE_LABELS.items()}
 
+MEDICAL_MD = """
+### Medisch nut  
+
+**Wat zien we hier?**  
+Ik heb een bestaande, anonieme gezondheidsdataset gebruikt die speciaal beschikbaar is gemaakt voor onderzoek en studie. In deze gegevens staan metingen van een grote groep patiënten, zoals **bloedwaarden, BMI, cholesterol en bloedsuiker**.  
+
+Zo'n enorme berg cijfers is voor artsen en ziekenhuizen bijna niet in één keer te overzien. Het is gewoon te veel om met het blote oog patronen uit te halen.  
+
+**Daar komt kunstmatige intelligentie om de hoek kijken.**  
+Met deze techniek (PCA) kan de computer de data slim samenvatten en patronen zichtbaar maken. Dit programma dat ik heb ontworpen laat live zien hoe die samenvatting werkt.  
+
+- Elke punt is één patiënt.  
+- De kleur laat zien hoe hoog of laag een bepaalde meting is (standaard: BMI).  
+- De pijlen (in de 2D-biplot) laten zien welke metingen het meeste invloed hebben.  
+- Links bovenin kun je kiezen welke meting je als uitgangspunt wilt nemen.  
+
+**En wat heb je hieraan?**  
+In de praktijk gebruiken artsen en onderzoekers zo'n plot om patronen en verbanden te ontdekken. 👉 Het is dus niet alleen een mooi plaatje, maar echt een manier om grote hoeveelheden data sneller en slimmer te begrijpen.  
+
+Met AI kunnen we patronen vinden die je met het blote oog nooit zou zien. Dat maakt dit niet alleen een mooie visualisatie, maar ook een knap stukje technologie met échte waarde voor onderzoek en zorg.  
+
+**Speel zelf de onderzoeker!**  
+Doe alsof je een arts bent en kies links bovenin een waarde, bijvoorbeeld **cholesterol**, **leeftijd** of **geslacht**. Klik daarna op *Update visualisaties* en ontdek je eigen patronen in de data.  
+"""
+
+# ======================
+# Data helpers
+# ======================
 def load_diabetes_df():
     d = datasets.load_diabetes()
-    X = pd.DataFrame(d.data, columns=d.feature_names)
+    X = pd.DataFrame(d.data, columns=d.feature_names)  # standardized features
     y = pd.Series(d.target, name="target")
     df = X.copy()
     df["target"] = y
     return df
 
-def compute_pca(df, n_components=10, standardize=True):
+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:
+        vals = df[k].dropna().values
+        if vals.size == 0:
+            continue
+        mean = float(vals.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),
+            "Gemiddelde (gestandaardiseerd)": round(mean, 3),
+            "% boven gemiddelde": round(pct_above, 1),
+            "% onder gemiddelde": round(pct_below, 1),
+        })
+    table = pd.DataFrame(rows)
+    note = (
+        "Let op: waarden in deze dataset zijn **gestandaardiseerd**. "
+        "`0` betekent ongeveer het **algemene gemiddelde**. "
+        "Positief = hoger dan gemiddeld, negatief = lager dan gemiddeld."
+    )
+    return table, note
+
+def compute_top_correlations(df: pd.DataFrame, top_n: int = 6):
+    feats = [c for c in df.columns if c != "target"]
+    corr = pd.DataFrame(df[feats]).corr()
+    pairs = []
+    for i, a in enumerate(feats):
+        for j, b in enumerate(feats):
+            if j <= i:
+                continue
+            pairs.append({
+                "Combinatie": f"{FEATURE_LABELS.get(a,a)} ↔ {FEATURE_LABELS.get(b,b)}",
+                "Correlatie": corr.loc[a, b]
+            })
+    out = pd.DataFrame(pairs)
+    out["Sterkte (|r|)"] = out["Correlatie"].abs()
+    out = out.sort_values("Sterkte (|r|)", ascending=False).head(top_n).reset_index(drop=True)
+    out["Correlatie"] = out["Correlatie"].round(3)
+    out["Sterkte (|r|)"] = out["Sterkte (|r|)"].round(3)
+    return out[["Combinatie", "Correlatie", "Sterkte (|r|)"]]
+
+# ======================
+# PCA helpers
+# ======================
+def compute_pca(df: pd.DataFrame, n_components: int, standardize: bool):
     feats = [c for c in df.columns if c != "target"]
     X = df[feats].values
     if standardize:
-        scaler = StandardScaler()
+        scaler = StandardScaler(with_mean=True, with_std=True)
         Xs = scaler.fit_transform(X)
     else:
         Xs = X
-    pca = PCA(n_components=min(n_components, Xs.shape[1]))
+    pca = PCA(n_components=min(int(n_components), Xs.shape[1]))
     Z = pca.fit_transform(Xs)
     loadings = pca.components_.T
     expl = pca.explained_variance_ratio_
     return feats, Xs, Z, loadings, expl
 
-def build_biplot(df, Z, loadings, feats, color_key):
+# ======================
+# Plot builders
+# ======================
+def build_biplot_plotly(df, Z, loadings, feats, color_key, arrow_scale=2.0):
+    # Hovertext met relevante velden
     hover_text = []
+    fields = ["bmi","bp","s1","s2","s3","s4","s5","s6","age","sex","target"]
     for idx in range(len(df)):
-        parts = [f"{FEATURE_LABELS.get(k,k)}: {df.iloc[idx][k]:.3f}" for k in df.columns]
+        parts = [f"{FEATURE_LABELS.get(k,k)}: {df.iloc[idx][k]:.3f}" for k in fields]
         hover_text.append("<br>".join(parts))
+
     fig = go.Figure()
-    fig.add_trace(go.Scatter(x=Z[:,0], y=Z[:,1], mode="markers",
-                             marker=dict(size=7, color=df[color_key].values),
-                             text=hover_text,
-                             hovertemplate="%{text}<extra></extra>"))
-    # arrows
-    scale=2.0
-    for i,key in enumerate(feats):
-        x = loadings[i,0]*scale; y = loadings[i,1]*scale
-        fig.add_annotation(x=x, y=y, ax=0, ay=0, showarrow=True, arrowhead=3)
-        fig.add_annotation(x=x*1.05, y=y*1.05, text=FEATURE_LABELS.get(key,key), showarrow=False, font=dict(size=10))
-    fig.update_layout(title="PCA-biplot (2D, hover)", xaxis_title="PC1", yaxis_title="PC2", height=500)
+    fig.add_trace(go.Scatter(
+        x=Z[:, 0], y=Z[:, 1],
+        mode="markers",
+        marker=dict(size=8, color=df[color_key].values),
+        text=hover_text,
+        hovertemplate="%{text}<extra></extra>"
+    ))
+
+    # Loading arrows (PC1/PC2)
+    for i, key in enumerate(feats):
+        x = loadings[i, 0] * arrow_scale
+        y = loadings[i, 1] * arrow_scale
+        fig.add_annotation(x=x, y=y, ax=0, ay=0,
+                           xref="x", yref="y", axref="x", ayref="y",
+                           showarrow=True, arrowhead=3)
+        fig.add_annotation(x=x*1.05, y=y*1.05, text=FEATURE_LABELS.get(key, key),
+                           showarrow=False, font=dict(size=10))
+
+    fig.update_layout(
+        title="PCA-biplot (2D, hover voor details)",
+        xaxis_title="PC1",
+        yaxis_title="PC2",
+        height=520,
+        margin=dict(l=0, r=0, t=40, b=0)
+    )
+    return fig
+
+def build_biplot_matplotlib(df, Z, loadings, feats, color_key, arrow_scale=2.0, point_size=32, alpha=0.85):
+    # Matplotlib variant voor PNG-export
+    fig = plt.figure(figsize=(7.8, 5.6))
+    ax = fig.add_subplot(111)
+    sc = ax.scatter(Z[:, 0], Z[:, 1], c=df[color_key].values, s=point_size, alpha=alpha)
+    cbar = plt.colorbar(sc, ax=ax, pad=0.02)
+    cbar.set_label(f"Kleur: {FEATURE_LABELS.get(color_key, color_key)}")
+    ax.set_xlabel("PC1"); ax.set_ylabel("PC2")
+    ax.set_title("PCA-biplot — punten + pijlen")
+    for i, key in enumerate(feats):
+        x = loadings[i, 0] * arrow_scale
+        y = loadings[i, 1] * arrow_scale
+        ax.arrow(0, 0, x, y, head_width=0.05, head_length=0.08, fc="k", ec="k", length_includes_head=True)
+        ax.text(x*1.08, y*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=":")
+    ax.grid(True, linestyle=":", linewidth=0.6)
+    plt.tight_layout()
     return fig
 
 def build_pca3d(Z3, color_vals):
-    fig = go.Figure(data=[go.Scatter3d(x=Z3[:,0], y=Z3[:,1], z=Z3[:,2], mode="markers",
-                                       marker=dict(size=4,color=color_vals,opacity=0.85))])
-    fig.update_layout(title="PCA 3D", scene=dict(xaxis_title="PC1", yaxis_title="PC2", zaxis_title="PC3"), height=500)
+    fig = go.Figure(data=[go.Scatter3d(
+        x=Z3[:, 0], y=Z3[:, 1], z=Z3[:, 2],
+        mode="markers",
+        marker=dict(size=4, color=color_vals, opacity=0.85)
+    )])
+    fig.update_layout(
+        title="PCA 3D — PC1 · PC2 · PC3 (sleep om te draaien)",
+        scene=dict(xaxis_title="PC1", yaxis_title="PC2", zaxis_title="PC3"),
+        margin=dict(l=0, r=0, t=40, b=0),
+        height=520
+    )
     return fig
 
 def build_variance_plot(expl):
-    fig = plt.figure()
+    fig = plt.figure(figsize=(7.8, 3.8))
     ax = fig.add_subplot(111)
-    xs = np.arange(1,len(expl)+1)
-    ax.bar(xs, expl); ax.plot(xs, np.cumsum(expl), marker="o")
-    ax.set_xlabel("PC"); ax.set_ylabel("Explained variance ratio")
-    fig.tight_layout()
+    xs = np.arange(1, len(expl) + 1)
+    ax.bar(xs, expl, width=0.8, align="center")
+    ax.plot(xs, np.cumsum(expl), marker="o")
+    ax.set_xticks(xs)
+    ax.set_xlabel("Principal Component")
+    ax.set_ylabel("Explained variance ratio")
+    ax.set_title("Uitlegvariantie per component (balken) + cumulatief (lijn)")
+    ax.grid(True, linestyle=":", linewidth=0.6)
+    plt.tight_layout()
     return fig
 
-def build_corr_heatmap(df):
+def build_corr_heatmap(df: pd.DataFrame):
     feats = [c for c in df.columns if c != "target"]
     corr = pd.DataFrame(df[feats]).corr()
     order = corr.abs().sum().sort_values(ascending=False).index.tolist()
     corr_sorted = corr.loc[order, order]
-    fig = px.imshow(corr_sorted, color_continuous_scale="RdBu", origin="lower", zmin=-1, zmax=1)
-    fig.update_layout(title="Correlatie-heatmap")
+    fig = px.imshow(corr_sorted, text_auto=False, aspect="auto",
+                    color_continuous_scale="RdBu", origin="lower", zmin=-1, zmax=1)
+    fig.update_layout(title="Correlatie-heatmap (gesorteerd op sterkte)",
+                      height=520, margin=dict(l=0, r=0, t=40, b=0))
     return fig
 
-def build_hist_box(df, color_key):
-    series = df[color_key]
+def build_hist_box(df: pd.DataFrame, color_key: str):
+    series = df[color_key].dropna()
     fig_hist = px.histogram(series, nbins=30, title=f"Histogram — {FEATURE_LABELS.get(color_key,color_key)}")
     fig_box = px.box(series, points="outliers", title=f"Boxplot — {FEATURE_LABELS.get(color_key,color_key)}")
     return fig_hist, fig_box
 
-def controller(color_label="BMI (Body Mass Index)", n_components=10, standardize=True):
+# ======================
+# Controllers
+# ======================
+def controller(color_label="BMI (Body Mass Index)", n_components=10, standardize=True, arrow_scale=2.0):
     df = load_diabetes_df()
     feats, Xs, Z, loadings, expl = compute_pca(df, n_components, standardize)
-    color_key = LABEL_TO_KEY[color_label]
-    fig2d = build_biplot(df, Z, loadings, feats, color_key)
+    color_key = LABEL_TO_KEY.get(color_label, "bmi")
+    color_vals = df[color_key].values
+    color_label_nl = FEATURE_LABELS.get(color_key, color_key)
+
+    # Plots
+    fig_biplot = build_biplot_plotly(df, Z, loadings, feats, color_key, arrow_scale=arrow_scale)
+    # 3D (zorg voor minstens 3 componenten)
     if Z.shape[1] < 3:
-        pca3 = PCA(n_components=3); Z3 = pca3.fit_transform(Xs)
+        pca3 = PCA(n_components=3)
+        Z3 = pca3.fit_transform(Xs)
     else:
-        Z3 = Z[:,:3]
-    fig3d = build_pca3d(Z3, df[color_key].values)
-    fig_var = build_variance_plot(expl)
-    fig_heat = build_corr_heatmap(df)
+        Z3 = Z[:, :3]
+    fig3d = build_pca3d(Z3, color_vals)
+    fig_variance = build_variance_plot(expl)
+    fig_heatmap = build_corr_heatmap(df)
     fig_hist, fig_box = build_hist_box(df, color_key)
-    return fig2d, fig3d, fig_var, fig_heat, fig_hist, fig_box
 
-with gr.Blocks() as demo:
-    gr.Markdown("# PCA Dashboard — Diabetes")
-    with gr.Row():
-        color_feat = gr.Dropdown(choices=list(LABEL_TO_KEY.keys()), value="BMI (Body Mass Index)", label="Kleur op meting")
-        n_components = gr.Slider(3,10,10,step=1,label="Aantal PCA-componenten")
-        standardize = gr.Checkbox(value=True,label="Standaardiseer")
-        run_btn = gr.Button("Update")
-    with gr.Row():
-        plot2d = gr.Plot(); plot3d = gr.Plot()
-    with gr.Row():
-        plot_var = gr.Plot(); plot_heat = gr.Plot()
+    # Tabel top-features
+    load_df = pd.DataFrame({
+        "feature_key": feats,
+        "PC1_loading": loadings[:, 0],
+        "PC2_loading": loadings[:, 1],
+        "PC1_abs": np.abs(loadings[:, 0]),
+        "PC2_abs": np.abs(loadings[:, 1]),
+    })
+    load_df["Feature (PC1)"] = load_df["feature_key"].map(lambda k: FEATURE_LABELS.get(k, k))
+    load_df["Feature (PC2)"] = load_df["feature_key"].map(lambda k: FEATURE_LABELS.get(k, k))
+    top_pc1 = load_df.sort_values("PC1_abs", ascending=False)[["Feature (PC1)", "PC1_loading"]].head(6).reset_index(drop=True)
+    top_pc2 = load_df.sort_values("PC2_abs", ascending=False)[["Feature (PC2)", "PC2_loading"]].head(6).reset_index(drop=True)
+    max_len = max(len(top_pc1), len(top_pc2))
+    top_pc1 = top_pc1.reindex(range(max_len)); top_pc2 = top_pc2.reindex(range(max_len))
+    table = pd.concat([top_pc1, top_pc2], axis=1)
+
+    overview_df, overview_note = compute_overview_table(df)
+
+    summary_md = f"""
+### Wat zie je hier?
+- **Hover** over punten voor exacte waarden (BMI, bloeddruk, cholesterol, glucose, leeftijd, geslacht, etc.).
+- **2D-biplot** met pijlen (belangrijkste metingen) en **3D-view** voor extra diepte.
+- **Uitlegvariantieplot**: laat zien hoeveel variatie elke component uitlegt.
+- **Correlatie-heatmap**: toont welke metingen samen bewegen (gesorteerd op sterkte).
+- **Histogram + boxplot**: verdeling en spreiding van de gekozen meting ({color_label_nl}).
+"""
+    return fig_biplot, fig3d, fig_variance, table, overview_df, overview_note, summary_md, fig_heatmap, fig_hist, fig_box
+
+def animate_pca(color_label="BMI (Body Mass Index)", point_size=32, alpha=0.85, n_components=10, standardize=True, frames=40, pause=0.0):
+    df = load_diabetes_df()
+    feats, Xs, Z, loadings, expl = compute_pca(df, n_components, standardize)
+    color_key = LABEL_TO_KEY.get(color_label, "bmi")
+    color_vals = df[color_key].values
+    for i in range(frames):
+        t = i / max(1, frames-1)
+        w1 = min(1.0, t * 2.0)
+        w2 = max(0.0, (t - 0.5) * 2.0)
+        coords = np.column_stack([Z[:, 0] * w1, Z[:, 1] * w2])
+        fig = plt.figure(figsize=(7.8, 5.6))
+        ax = fig.add_subplot(111)
+        ax.scatter(coords[:, 0], coords[:, 1], c=color_vals, s=point_size, alpha=alpha)
+        ax.set_xlabel("PC1 (opbouw)"); ax.set_ylabel("PC2 (opbouw)")
+        title = "PCA-projectie (animatie) — " + ("PC1 →" if w2 == 0 else "PC1 + PC2")
+        ax.set_title(f"{title} — frame {i+1}/{frames}")
+        ax.axhline(0, color="grey", linewidth=0.6, linestyle=":")
+        ax.axvline(0, color="grey", linewidth=0.6, linestyle=":")
+        ax.grid(True, linestyle=":", linewidth=0.6)
+        plt.tight_layout()
+        yield fig
+        if pause > 0:
+            time.sleep(pause)
+
+def export_biplot_png(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()
+    feats, Xs, Z, loadings, expl = compute_pca(df, n_components, standardize)
+    color_key = LABEL_TO_KEY.get(color_label, "bmi")
+    fig = build_biplot_matplotlib(df, Z, loadings, feats, color_key, arrow_scale=arrow_scale, point_size=point_size, alpha=alpha)
+    path = f"/mnt/data/biplot_{int(time.time())}.png"
+    fig.savefig(path, dpi=150, bbox_inches="tight")
+    plt.close(fig)
+    return path
+
+def export_variance_png(n_components=10, standardize=True):
+    df = load_diabetes_df()
+    feats, Xs, Z, loadings, expl = compute_pca(df, n_components, standardize)
+    fig = build_variance_plot(expl)
+    path = f"/mnt/data/variance_{int(time.time())}.png"
+    fig.savefig(path, dpi=150, bbox_inches="tight")
+    plt.close(fig)
+    return path
+
+# ======================
+# UI
+# ======================
+with gr.Blocks(title="PCA Dashboard — Diabetes (compleet)") as demo:
+    gr.HTML("""
+    <style>
+      .card {background:#fff; border:1px solid #e5e7eb; border-radius:12px; padding:14px; box-shadow: 0 1px 4px rgba(0,0,0,0.06);}
+      .callout {padding:12px 14px; border-left:4px solid #2563eb; background:#f1f5f9; border-radius:8px; margin: 8px 0 18px;}
+      .smallnote {font-size: 0.92em; opacity: 0.85;}
+    </style>
+    """)
+
+    gr.Markdown("# PCA Dashboard — Diabetes (compleet)")
+    gr.Markdown(MEDICAL_MD)
+
     with gr.Row():
-        plot_hist = gr.Plot(); plot_box = gr.Plot()
+        with gr.Column(scale=1):
+            with gr.Group():
+                gr.Markdown("### Instellingen")
+                color_choices = [FEATURE_LABELS[k] for k in ["bmi","bp","s1","s2","s3","s4","s5","s6","age","sex","target"]]
+                color_feat = gr.Dropdown(choices=color_choices, value=FEATURE_LABELS["bmi"], label="Kleur op meting")
+                n_components = gr.Slider(3, 10, value=10, step=1, label="Aantal PCA-componenten")
+                standardize = gr.Checkbox(value=True, label="Standaardiseer metingen (aanbevolen)")
+                arrow_scale = gr.Slider(0.5, 5.0, value=2.0, step=0.1, label="Pijl-schaal (2D-biplot)")
+                run_btn = gr.Button("Update visualisaties")
+                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.Group():
+                gr.Markdown("### Animatie")
+                animate_btn = gr.Button("▶ Animate PCA (PC1 → PC2)")
+                anim_plot = gr.Plot(label="Animatie van projectie")
+            with gr.Group():
+                gr.Markdown("### Downloads")
+                dl_biplot = gr.DownloadButton("Download biplot (PNG)")
+                dl_var = gr.DownloadButton("Download variatieplot (PNG)")
+
+        with gr.Column(scale=2):
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("### Biplot (2D, hover)")
+                    plot_biplot = gr.Plot()
+                with gr.Column():
+                    gr.Markdown("### 3D PCA (PC1–PC3)")
+                    plot3d = gr.Plot()
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("### Uitlegvariantie")
+                    plot_expl = gr.Plot()
+                with gr.Column():
+                    gr.Markdown("### Correlatie-heatmap")
+                    plot_heat = gr.Plot()
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("### Histogram")
+                    plot_hist = gr.Plot()
+                with gr.Column():
+                    gr.Markdown("### Boxplot")
+                    plot_box = gr.Plot()
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("### Top-features (PC1 / PC2)")
+                    table = gr.Dataframe(headers=["Feature (PC1)", "Loading PC1", "Feature (PC2)", "Loading PC2"], row_count=6)
+                with gr.Column():
+                    gr.Markdown("### Overzicht (gemiddelden & verdeling)")
+                    overview_tbl = gr.Dataframe(interactive=False)
+            summary = gr.Markdown()
+            overview_note_md = gr.Markdown()
+
+    inputs = [color_feat, n_components, standardize, arrow_scale]
+    run_btn.click(fn=controller, inputs=inputs,
+                  outputs=[plot_biplot, plot3d, plot_expl, table, overview_tbl, overview_note_md, summary, plot_heat, plot_hist, plot_box])
+    demo.load(fn=controller, inputs=inputs,
+              outputs=[plot_biplot, plot3d, plot_expl, table, overview_tbl, overview_note_md, summary, plot_heat, plot_hist, plot_box])
+
+    animate_btn.click(fn=animate_pca,
+                      inputs=[color_feat],
+                      outputs=anim_plot)
 
-    inputs=[color_feat,n_components,standardize]
-    run_btn.click(fn=controller, inputs=inputs, outputs=[plot2d,plot3d,plot_var,plot_heat,plot_hist,plot_box])
-    demo.load(fn=controller, inputs=inputs, outputs=[plot2d,plot3d,plot_var,plot_heat,plot_hist,plot_box])
+    # Downloads (PNG)
+    dl_biplot.click(fn=export_biplot_png,
+                    inputs=[color_feat, arrow_scale],
+                    outputs=[dl_biplot])
+    dl_var.click(fn=export_variance_png,
+                 inputs=[],
+                 outputs=[dl_var])
 
-if __name__=="__main__":
-    demo.queue().launch(server_name="0.0.0.0", server_port=7860)
+if __name__ == "__main__":
+    demo.queue().launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False, show_api=False)