Update app.py

app.py

@@ -1,4 +1,4 @@
-# app.py — Minimal, pro UI with big transparent sphere and clean hover
+# app.py — Minimal, pro UI with big transparent sphere, accuracy, and robust rendering
 import math, json, random, time, threading
 from dataclasses import dataclass, asdict
 from typing import List, Tuple, Dict, Any, Optional
@@ -23,7 +23,7 @@ CUSTOM_CSS = """
 .gradio-container { max-width: 1180px !important; }
 #header { border-radius: var(--radius); padding: 8px 6px; }
 h1, h2, h3, .gr-markdown { color: var(--fg); }
-.gr-button { border-radius: 10px; }
+.gr-button { border-radius: 10px }
 .controls .gr-group, .panel { border: 1px solid var(--line); border-radius: var(--radius); }
 .panel { padding: 10px; }
 #stats { font-weight: 300; color: var(--fg); }
@@ -109,15 +109,24 @@ class TinyMLP(nn.Module):
 
 @lru_cache(maxsize=4)
 def _cached_dataset(name: str):
-    if name.startswith("PIQA"): return load_piqa(subset=800, seed=42)
-    if name.startswith("HellaSwag"): return load_hellaswag(subset=800, seed=42)
+    # Defensive: if loading fails (e.g., datasets version / no internet), return None
+    try:
+        if name.startswith("PIQA"): return load_piqa(subset=800, seed=42)
+        if name.startswith("HellaSwag"): return load_hellaswag(subset=800, seed=42)
+    except Exception:
+        return None
     return None
 
 def _train_eval_proxy(genome: Genome, dataset_name: str, explore: float, device: str = "cpu"):
     data = _cached_dataset(dataset_name)
     if data is None:
+        # Fallback to surrogate to keep the UI alive
         v = genome.vector() * 2 - 1
-        return float(rastrigin(v)), None
+        base = rastrigin(v)
+        parsimony = 0.001 * (genome.d_model + 50*genome.n_layers + 20*genome.n_heads + 100*genome.memory_tokens)
+        noise = np.random.normal(scale=0.05 * max(0.0, min(1.0, explore)))
+        return float(base + parsimony + noise), None
+
     Xtr_txt, ytr, Xva_txt, yva = data
     nfeat = 4096
     Xtr = hash_vectorize(Xtr_txt, n_features=nfeat, seed=1234)
@@ -173,6 +182,7 @@ def evaluate_genome(genome: Genome, dataset: str, explore: float):
         return _train_eval_proxy(genome, "PIQA", explore)
     if dataset.startswith("HellaSwag"):
         return _train_eval_proxy(genome, "HellaSwag", explore)
+    # Fallback
     v = genome.vector() * 2 - 1
     return float(rastrigin(v)), None
 
@@ -188,11 +198,7 @@ def sphere_project(points: np.ndarray) -> np.ndarray:
     return (Y / norms) * 1.2
 
 def _species_colors(species: np.ndarray) -> list:
-    colors = []
-    for s in species:
-        c = PALETTE[int(s) % len(PALETTE)]
-        colors.append(c)
-    return colors
+    return [PALETTE[int(s) % len(PALETTE)] for s in species]
 
 def make_sphere_figure(points3d: np.ndarray, genomes: List[Genome], gen_idx: int) -> go.Figure:
     species = np.array([g.species for g in genomes])
@@ -264,8 +270,13 @@ def make_history_figure(history: List[Tuple[int,float,float]], metric: str) -> g
     return fig
 
 def fig_to_html(fig: go.Figure) -> str:
-    # Robust Plotly rendering inside Gradio
-    return pio.to_html(fig, include_plotlyjs="cdn", full_html=False, config=dict(displaylogo=False))
+    # Inline Plotly JS to avoid any CDN/network dependency in Spaces
+    return pio.to_html(
+        fig,
+        include_plotlyjs=True,   # IMPORTANT: inline JS so the sphere always renders
+        full_html=False,
+        config=dict(displaylogo=False)
+    )
 
 def approx_params(g: Genome) -> int:
     per_layer = (4.0 + 2.0 * float(g.ffn_mult)) * (g.d_model ** 2)