Update app.py

app.py

@@ -29,66 +29,46 @@ csv_btn = gr.File(label="Download CSV History")
 json_btn = gr.File(label="Download JSON History")
 
 # === Helper: Create Evolution Radar Plot ===
-def plot_radar(history):
-    import numpy as np
-
-    traits = ["layers", "attention_heads", "ffn_dim", "dropout", "memory"]
-    N = len(traits)
-    values = [history[-1].get(t, 0) for t in traits]
-    values = [int(v) if isinstance(v, bool) else v for v in values]
-
-    angles = np.linspace(0, 2 * np.pi, N, endpoint=False).tolist()
-    values += values[:1]
-    angles += angles[:1]
-
-    fig, ax = plt.subplots(figsize=(5, 5), subplot_kw=dict(polar=True))
-    ax.plot(angles, values, "o-", linewidth=2)
-    ax.fill(angles, values, alpha=0.25)
-    ax.set_thetagrids([a * 180 / np.pi for a in angles[:-1]], traits)
-    ax.set_title("Last Gen Trait Radar", fontsize=14)
-    plt.tight_layout()
-
-    tmp = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
-    plt.savefig(tmp.name)
-    plt.close()
-    return tmp.name
-
-radar_plot = gr.Image(label="Final Generation Trait Radar")
-
-# === Main Evolution Logic ===
 def evolve_and_display(gens):
-    model.evolve(generations=gens)
-    history = model.get_history()
-    eval_result = model.evaluate()
-
-    # Format summary
-    summary = "\n".join([f"{k}: {v}" for k, v in history[-1].items()])
-
-    # Fill up to 10 generations of history for display
-    history_txt = [json.dumps(h, indent=2) for h in history]
-    while len(history_txt) < 10:
-        history_txt.append("")
-
-    # Generate CSV + JSON
-    df = pd.DataFrame(history)
-    csv_path = tempfile.NamedTemporaryFile(suffix=".csv", delete=False).name
-    json_path = tempfile.NamedTemporaryFile(suffix=".json", delete=False).name
-    df.to_csv(csv_path, index=False)
-    with open(json_path, "w") as jf:
-        json.dump(history, jf, indent=2)
-
-    # Plot radar
-    radar_img = plot_radar(history)
-
-    return (
-        f"{eval_result['accuracy']*100:.2f}%",
-        f"{eval_result['params']:.2f}M params",
-        summary,
-        *history_txt,
-        radar_img,
-        csv_path,
-        json_path,
-    )
+    try:
+        model.evolve(generations=gens)
+        history = model.get_history()
+        eval_result = model.evaluate()
+
+        summary = "\n".join([f"{k}: {v}" for k, v in history[-1].items()])
+        history_txt = [json.dumps(h, indent=2) for h in history]
+        while len(history_txt) < 10:
+            history_txt.append("")
+
+        # Save CSV and JSON
+        df = pd.DataFrame(history)
+        csv_path = tempfile.NamedTemporaryFile(suffix=".csv", delete=False).name
+        json_path = tempfile.NamedTemporaryFile(suffix=".json", delete=False).name
+        df.to_csv(csv_path, index=False)
+        with open(json_path, "w") as jf:
+            json.dump(history, jf, indent=2)
+
+        radar_img = plot_radar(history)
+
+        return (
+            f"{eval_result['accuracy']*100:.2f}%",
+            f"{eval_result['params']:.2f}M params",
+            summary,
+            *history_txt,
+            radar_img,
+            csv_path,
+            json_path,
+        )
+    except Exception as e:
+        print("🚨 ERROR during evolution:", str(e))
+        import traceback
+        traceback.print_exc()
+
+        return (
+            "Error", "Error", "Error",
+            *["Error"] * 10,
+            None, None, None
+        )
 
 # === Interface Layout ===
 with gr.Blocks(title="EvoTransformer Demo") as demo: