Update app.py

app.py

@@ -9,12 +9,12 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 from wfgy_sdk import get_engine
 from wfgy_sdk.evaluator import compare_logits, plot_histogram, softmax
 
-# ── tiny model (CPU) ────────────────────────────────────────────────
+# tiny model (CPU)
 tok = AutoTokenizer.from_pretrained("sshleifer/tiny-gpt2")
 mdl = AutoModelForCausalLM.from_pretrained("sshleifer/tiny-gpt2")
 eng = get_engine()
 
-# ── paper benchmarks ────────────────────────────────────────────────
+# paper benchmarks
 bench = pd.DataFrame({
     "Benchmark": ["MMLU","GSM8K","BBH","MathBench","TruthfulQA",
                   "XNLI","MLQA","LongBench","VQAv2","OK-VQA"],
@@ -23,13 +23,13 @@ bench = pd.DataFrame({
 })
 bench["Abs_gain"]  = (bench["WFGY"] - bench["Baseline"]).round(1)
 bench["Rel_gain%"] = ((bench["Abs_gain"] / bench["Baseline"]) * 100).round(0)
-bench_style = (
+bench_sty = (
     bench.style
     .background_gradient(cmap="Greens", subset=["Abs_gain","Rel_gain%"])
     .format({"Abs_gain":"{:.1f}","Rel_gain%":"{:.0f}"})
 )
 
-# ── marketing banner ────────────────────────────────────────────────
+# banner markdown
 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))
 
 ---
 
@@ -53,24 +53,25 @@ Prompt examples: *TBD*
 _10 k ⭐ before 2025-08-01 unlocks **WFGY 2.0**._
 """
 
-# ── inference ───────────────────────────────────────────────────────
+# inference
 def run(prompt: str):
-    prompt = prompt.strip()
-    if not prompt:
+    p = prompt.strip()
+    if not p:
         return "", "", "-", None
 
-    ids   = tok(prompt, return_tensors="pt")
+    ids   = tok(p, return_tensors="pt")
     raw_L = mdl(**ids).logits[0, -1].detach().cpu().numpy()
     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)
-    hdr = f"▼ var {m['var_drop']*100:.1f}% | KL {m['kl_divergence']:.3f} | top-1 {'kept' if m['top1'] else 'changed'}"
+    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]
-        return "\n".join([f\"'{tok.decode(int(i)).strip()}': {p[i]:.2e}\" for i in idx])
+        lines = [f\"'{tok.decode(int(i)).strip()}': {p[i]:.2e}\" for i in idx]
+        return "\\n".join(lines)
 
     raw_txt = top5(raw_L)
     mod_txt = top5(mod_L)
@@ -78,9 +79,9 @@ 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, hdr, Image.open(buf)
+    return raw_txt, mod_txt, head, Image.open(buf)
 
-# ── Gradio UI ────────────────────────────────────────────────────────
+# UI
 with gr.Blocks(title="WFGY variance-gate demo") as demo:
     gr.Markdown(banner)
 
@@ -95,7 +96,7 @@ with gr.Blocks(title="WFGY variance-gate demo") as demo:
     img     = gr.Image(label="Logit histogram")
 
     gr.Markdown("### Paper benchmarks (fixed values from WFGY 1.0)")
-    gr.DataFrame(bench_style, interactive=False, wrap=True)
+    gr.DataFrame(bench_sty, interactive=False, wrap=True)
 
     btn.click(run, prompt, [raw_box, mod_box, metrics, img])