Update app.py

app.py

@@ -1,25 +1,23 @@
 """
-WFGY Space – interactive variance-gate demo (tiny-GPT-2 version)
-
-★  Help us reach 10 k GitHub stars before 2025-08-01 to unlock WFGY 2.0 ★
+WFGY Space – tiny-GPT-2 variance-gate demo
+★ 10 k GitHub ⭐ before 2025-08-01 unlocks WFGY 2.0 ★
 """
 
 import io, numpy as np, matplotlib
 matplotlib.use("Agg")
 
 from PIL import Image
-import pandas as pd, plotly.express as px, gradio as gr
-import torch
+import pandas as pd, plotly.express as px, gradio as gr, torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 from wfgy_sdk import get_engine
 from wfgy_sdk.evaluator import compare_logits, plot_histogram
 
-# ── tiny model keeps HF Space free-CPU build fast ──
+# tiny free-CPU model
 TOKENIZER = AutoTokenizer.from_pretrained("sshleifer/tiny-gpt2")
 MODEL     = AutoModelForCausalLM.from_pretrained("sshleifer/tiny-gpt2")
 ENGINE    = get_engine()
 
-# ── benchmark table from the WFGY 1.0 paper ──
+# paper numbers
 paper = pd.DataFrame({
     "Benchmark": ["MMLU","GSM8K","BBH","MathBench","TruthfulQA",
                   "XNLI","MLQA","LongBench","VQAv2","OK-VQA"],
@@ -27,35 +25,39 @@ paper = pd.DataFrame({
     "WFGY":      [89.8,98.7,100.7,87.4,90.4,77.3,106.6,69.6,86.6,86.8]
 })
 paper["Abs_gain"]  = (paper["WFGY"] - paper["Baseline"]).round(1)
-paper["Rel_gain%"] = ((paper["Abs_gain"] / paper["Baseline"]) * 100).round(0)
+paper["Rel_gain%"] = ((paper["Abs_gain"] / paper["Baseline"])*100).round(0)
+
+# color bars on gains
 paper_style = (
     paper.style
-    .background_gradient(cmap="Greens", subset=["Abs_gain","Rel_gain%"])
+    .format({"Baseline":"{:.1f}","WFGY":"{:.1f}"})
     .format({"Abs_gain":"{:.1f}","Rel_gain%":"{:.0f}"})
+    .background_gradient(cmap="Greens", subset=["Abs_gain","Rel_gain%"])
 )
 
-# ── helpers ────────────────────────────────────────────────────────────
-def top5(txt_logits: np.ndarray) -> str:
-    probs = torch.softmax(torch.tensor(txt_logits), dim=0).numpy()
-    idx   = probs.argsort()[-5:][::-1]
-    rows  = [f"{TOKENIZER.decode(int(i)).strip()!r}: {probs[i]:.2e}" for i in idx]
+# helpers ───────────────────────────────────────────────────────────────
+def top5(logits: np.ndarray) -> str:
+    p = torch.softmax(torch.tensor(logits), 0).numpy()
+    idx = p.argsort()[-5:][::-1]
+    rows = [f"{TOKENIZER.decode(int(i)).strip()!r}: {p[i]:.2e}" for i in idx]
     return "\n".join(rows)
 
-def history_plot(hist):
-    if not hist["step"]:
+def hist_plot(state):
+    if not state["step"]:
         return gr.Plot()
-    df = pd.DataFrame(hist)
-    return px.line(
-        df, x="step", y=["var","kl"],
-        labels={"value":"metric","step":"call"},
-        title="History (variance ↓ & KL)",
-        height=270
-    )
+    df = pd.DataFrame(state)
+    return px.line(df, x="step", y=["var","kl"],
+                   labels={"value":"metric","step":"call"},
+                   height=270)
+
+def clear(state):            # reset history
+    state["step"].clear(); state["var"].clear(); state["kl"].clear()
+    return hist_plot(state)
 
-def run(prompt, hist):
+def run(prompt: str, state):
     prompt = prompt.strip()
     if not prompt:
-        return "", "", " ", None, history_plot(hist)
+        return "", "", " ", None, hist_plot(state)
 
     ids   = TOKENIZER(prompt, return_tensors="pt").input_ids
     rawL  = MODEL(ids).logits[0, -1].detach().cpu().numpy()
@@ -63,30 +65,25 @@ def run(prompt, hist):
     I     = G + np.random.normal(scale=0.05, size=256).astype(np.float32)
     modL  = ENGINE.run(I, G, rawL)
 
-    met   = compare_logits(rawL, modL)
-    n     = len(hist["step"])
-    hist["step"].append(n)
-    hist["var"].append(met["var_drop"]*100)
-    hist["kl"].append(met["kl_divergence"])
+    m = compare_logits(rawL, modL)
+    step = len(state["step"])
+    state["step"].append(step); state["var"].append(m["var_drop"]*100)
+    state["kl"].append(m["kl_divergence"])
 
-    fig   = plot_histogram(rawL, modL)
-    buf   = io.BytesIO(); fig.savefig(buf, format="png"); buf.seek(0)
-    head  = f"▼ var {met['var_drop']*100:4.1f}% | KL {met['kl_divergence']:.3f} | top-1 {'kept' if met['top1'] else 'changed'}"
+    buf = io.BytesIO()
+    plot_histogram(rawL, modL).savefig(buf, format="png"); buf.seek(0)
+    head = f"▼ var {m['var_drop']*100:4.1f}% | KL {m['kl_divergence']:.3f} | top-1 {'kept' if m['top1'] else 'changed'}"
 
-    return top5(rawL), top5(modL), head, Image.open(buf), history_plot(hist)
+    return top5(rawL), top5(modL), head, Image.open(buf), hist_plot(state)
 
-def clear(hist):
-    hist["step"].clear(); hist["var"].clear(); hist["kl"].clear()
-    return history_plot(hist)
-
-# ── UI ─────────────────────────────────────────────────────────────────
+# UI ────────────────────────────────────────────────────────────────────
 with gr.Blocks(title="WFGY variance gate demo") as demo:
     gr.Markdown(
         """
-# 🧠 **WFGY simulation demo**  
-Tiny GPT-2 + variance-gate. Type any prompt and watch logits collapse.
+> 📈 **萬法歸一，一鍵啟動 AI 太極循環**  
+> 📊 *Semantic accuracy ↑ 22 %  Reasoning pass ↑ 42 %  Stability × 3.6*
 
-[⭐ Star the repo](https://github.com/onestardao/WFGY) – 10 k ⭐ before **2025-08-01** unlocks WFGY 2.0.  
+[⭐ Star the repo](https://github.com/onestardao/WFGY) – **10 k ⭐ before 2025-08-01 unlocks WFGY 2.0**  
 [📄 PDF](https://doi.org/10.5281/zenodo.15630970) • [GitHub](https://github.com/onestardao/WFGY)
 """)
 
@@ -97,26 +94,32 @@ Tiny GPT-2 + variance-gate. Type any prompt and watch logits collapse.
         raw_box = gr.Textbox(label="Raw top-5 tokens", lines=6)
         mod_box = gr.Textbox(label="WFGY top-5 tokens", lines=6)
 
-    headline = gr.Markdown()
-    hist_img = gr.Image(type="pil", label="Logit histogram")
-    hist_plot = gr.Plot()
+    headline  = gr.Markdown()
+    hist_img  = gr.Image(type="pil", label="Logit histogram")
+    hist_line = gr.Plot()
     clear_btn = gr.Button("Clear history")
+    hist_state = gr.State({"step": [], "var": [], "kl": []})
 
     with gr.Accordion("Paper benchmarks (fixed values from WFGY 1.0)", open=False):
         gr.DataFrame(paper_style, interactive=False, wrap=True)
         gr.Markdown(
-            "These numbers come from the paper; run your own benchmarks with "
-            "`WFGY/examples/example_08_big_model.py` on a larger LLM.")
+            "Numbers are taken from the paper. Run your own benchmarks with "
+            "`examples/example_08_big_model.py` on a larger model."
+        )
 
-    gr.Markdown("---\n*Free CPU demo = modest effect.* "
-                "Try a bigger model locally for dramatic variance drop & KL gain.")
-
-    # state for history
-    hist_state = gr.State({"step": [], "var": [], "kl": []})
+    gr.Markdown(
+        "---\n"
+        "Free-CPU demo uses a tiny GPT-2, so KL is small and top-1 often kept. "
+        "Run WFGY on a ≥ 7 B LLM for dramatic variance drop & token shifts."
+    )
 
     run_btn.click(run, [prompt, hist_state],
-                  [raw_box, mod_box, headline, hist_img, hist_plot])
-    clear_btn.click(clear, hist_state, hist_plot)
+                  [raw_box, mod_box, headline, hist_img, hist_line])
+    clear_btn.click(clear, hist_state, hist_line)
+
+    gr.Markdown(
+        "\n\n⭐ **Remember to star the repo – every click pushes WFGY 2.0 closer!**"
+    )
 
 if __name__ == "__main__":
     demo.queue().launch()