Update app.py

app.py

@@ -6,35 +6,34 @@ WFGY Space – tiny-GPT-2 variance-gate demo
 import io, numpy as np, pandas as pd, gradio as gr
 from PIL import Image
 from transformers import AutoTokenizer, AutoModelForCausalLM
-
 from wfgy_sdk import get_engine
 from wfgy_sdk.evaluator import compare_logits, plot_histogram, softmax
 
-# tiny model for free-CPU Space
+# ── tiny model (CPU) ────────────────────────────────────────────────
 tok = AutoTokenizer.from_pretrained("sshleifer/tiny-gpt2")
 mdl = AutoModelForCausalLM.from_pretrained("sshleifer/tiny-gpt2")
 eng = get_engine()
 
-# paper benchmarks table
+# ── paper benchmarks ────────────────────────────────────────────────
 bench = pd.DataFrame({
     "Benchmark": ["MMLU","GSM8K","BBH","MathBench","TruthfulQA",
                   "XNLI","MLQA","LongBench","VQAv2","OK-VQA"],
     "Baseline":  [61,78,79.3,72.2,62.4,59.5,78.1,51.4,69.1,65.7],
     "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["Abs_gain"]  = (bench["WFGY"] - bench["Baseline"]).round(1)
+bench["Rel_gain%"] = ((bench["Abs_gain"] / bench["Baseline"]) * 100).round(0)
 bench_style = (
     bench.style
     .background_gradient(cmap="Greens", subset=["Abs_gain","Rel_gain%"])
     .format({"Abs_gain":"{:.1f}","Rel_gain%":"{:.0f}"})
 )
 
-# marketing banner
-banner_md = """
+# ── marketing banner ────────────────────────────────────────────────
+banner = """
 **📈 WFGY: One Click to Activate the AI Taiji Cycle**
 
-**📊 Semantic Accuracy ↑ 22.4 % | Reasoning Success ↑ 42.1 % | Stability ↑ 3.6 ×**
+**📊 Semantic Accuracy ↑ 22.4 % | Reasoning Success ↑ 42.1 % | Stability ↑ 3.6 ×**
 
 _No beliefs. Only experiments._  
 WFGY 1.0 has already proven itself.
@@ -51,10 +50,10 @@ Prompt examples: *TBD*
 ---
 
 🌟 **Star Reminder** → [Star the repo](https://github.com/onestardao/WFGY)  
-_10 k ⭐ before 2025-08-01 unlocks WFGY 2.0._
+_10 k ⭐ before 2025-08-01 unlocks **WFGY 2.0**._
 """
 
-# run once
+# ── inference ───────────────────────────────────────────────────────
 def run(prompt: str):
     prompt = prompt.strip()
     if not prompt:
@@ -65,8 +64,8 @@ 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)
-    head = f"▼ var {m['var_drop']*100:.1f}% | KL {m['kl_divergence']:.3f} | top-1 {'kept' if m['top1'] else 'changed'}"
+    m   = compare_logits(raw_L, mod_L)
+    hdr = 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)
@@ -79,11 +78,11 @@ def run(prompt: str):
     fig = plot_histogram(raw_L, mod_L)
     buf = io.BytesIO(); fig.savefig(buf, format="png"); buf.seek(0)
 
-    return raw_txt, mod_txt, head, Image.open(buf)
+    return raw_txt, mod_txt, hdr, Image.open(buf)
 
-# UI
+# ── Gradio UI ────────────────────────────────────────────────────────
 with gr.Blocks(title="WFGY variance-gate demo") as demo:
-    gr.Markdown(banner_md)
+    gr.Markdown(banner)
 
     prompt = gr.Textbox(label="Prompt", value="Explain Schrödinger's cat")
     btn    = gr.Button("🚀 Run")