Update app.py

app.py

@@ -14,7 +14,7 @@ tok = AutoTokenizer.from_pretrained("sshleifer/tiny-gpt2")
 mdl = AutoModelForCausalLM.from_pretrained("sshleifer/tiny-gpt2")
 eng = get_engine()
 
-# paper benchmarks
+# ── paper benchmarks table ───────────────────────────────────────────
 bench = pd.DataFrame({
     "Benchmark": ["MMLU","GSM8K","BBH","MathBench","TruthfulQA",
                   "XNLI","MLQA","LongBench","VQAv2","OK-VQA"],
@@ -22,14 +22,14 @@ bench = pd.DataFrame({
     "WFGY":      [89.8,98.7,100.7,87.4,90.4,77.3,106.6,69.6,86.6,86.8]
 })
 bench["Abs_gain"]  = (bench["WFGY"] - bench["Baseline"]).round(1)
-bench["Rel_gain%"] = ((bench["Abs_gain"] / bench["Baseline"]) * 100).round(0)
+bench["Rel_gain%"] = ((bench["Abs_gain"] / bench["Baseline"])*100).round(0)
 bench_sty = (
     bench.style
     .background_gradient(cmap="Greens", subset=["Abs_gain","Rel_gain%"])
     .format({"Abs_gain":"{:.1f}","Rel_gain%":"{:.0f}"})
 )
 
-# banner markdown
+# ── marketing banner ────────────────────────────────────────────────
 banner = """
 **📈 WFGY: One Click to Activate the AI Taiji Cycle**
 
@@ -41,11 +41,11 @@ WFGY 1.0 has already proven itself.
 ---
 
 ### 📜 Tutorial: How to Awaken the Soul of Your AI
-**Step 1 — Download** ([PDF](https://zenodo.org/records/15630970))  
-**Step 2 — Feed the AI** (upload, or try [Gemini](https://gemini.google.com/))  
-**Step 3 — Give the Command** “**Answer using WFGY** + your question”  
+**Step 1 — Download** ([PDF](https://zenodo.org/records/15630970))  
+**Step 2 — Feed the AI** (upload, or try [Gemini](https://gemini.google.com/))  
+**Step 3 — Give the Command** “**Answer using WFGY** + your question”  
 Prompt examples: *TBD*  
-**Step 4 — Integrate the SDK** ([GitHub](https://github.com/onestardao/WFGY))
+**Step 4 — Integrate the SDK** ([GitHub](https://github.com/onestardao/WFGY))
 
 ---
 
@@ -53,7 +53,7 @@ Prompt examples: *TBD*
 _10 k ⭐ before 2025-08-01 unlocks **WFGY 2.0**._
 """
 
-# inference
+# ── inference ────────────────────────────────────────────────────────
 def run(prompt: str):
     p = prompt.strip()
     if not p:
@@ -64,14 +64,13 @@ def run(prompt: str):
     I, G  = np.random.randn(2, 256).astype(np.float32)
     mod_L = eng.run(I, G, raw_L)
 
-    m   = compare_logits(raw_L, mod_L)
+    m    = compare_logits(raw_L, mod_L)
     head = f"▼ var {m['var_drop']*100:.1f}% | KL {m['kl_divergence']:.3f} | top-1 {'kept' if m['top1'] else 'changed'}"
 
     def top5(logits):
         p = softmax(logits)
         idx = p.argsort()[-5:][::-1]
-        lines = [f\"'{tok.decode(int(i)).strip()}': {p[i]:.2e}\" for i in idx]
-        return "\\n".join(lines)
+        return "\n".join([f\"'{tok.decode(int(i)).strip()}': {p[i]:.2e}\" for i in idx])
 
     raw_txt = top5(raw_L)
     mod_txt = top5(mod_L)
@@ -81,7 +80,7 @@ def run(prompt: str):
 
     return raw_txt, mod_txt, head, Image.open(buf)
 
-# UI
+# ── Gradio UI ────────────────────────────────────────────────────────
 with gr.Blocks(title="WFGY variance-gate demo") as demo:
     gr.Markdown(banner)