Update app.py

app.py

@@ -1,125 +1,102 @@
 """
-WFGY Space – tiny-GPT-2 variance-gate demo
-★ 10 k GitHub ⭐ before 2025-08-01 unlocks WFGY 2.0 ★
+HF Space · WFGY simulation demo
+(complete file – paste/replace your current app.py)
 """
 
-import io, numpy as np, matplotlib
-matplotlib.use("Agg")
-
-from PIL import Image
-import pandas as pd, plotly.express as px, gradio as gr, torch
+import io
+import numpy as np
+import gradio as gr
+import matplotlib.pyplot as plt
 from transformers import AutoTokenizer, AutoModelForCausalLM
-from wfgy_sdk import get_engine
-from wfgy_sdk.evaluator import compare_logits, plot_histogram
-
-# tiny free-CPU model
-TOKENIZER = AutoTokenizer.from_pretrained("sshleifer/tiny-gpt2")
-MODEL     = AutoModelForCausalLM.from_pretrained("sshleifer/tiny-gpt2")
-ENGINE    = get_engine()
-
-# paper numbers
-paper = pd.DataFrame({
-    "Benchmark": ["MMLU","GSM8K","BBH","MathBench","TruthfulQA",
-                  "XNLI","MLQA","LongBench","VQAv2","OK-VQA"],
-    "Baseline":  [61.0,78.0,79.3,72.2,62.4,59.5,78.1,51.4,69.1,65.7],
-    "WFGY":      [89.8,98.7,100.7,87.4,90.4,77.3,106.6,69.6,86.6,86.8]
-})
-paper["Abs_gain"]  = (paper["WFGY"] - paper["Baseline"]).round(1)
-paper["Rel_gain%"] = ((paper["Abs_gain"] / paper["Baseline"])*100).round(0)
-
-# color bars on gains
-paper_style = (
-    paper.style
-    .format({"Baseline":"{:.1f}","WFGY":"{:.1f}"})
-    .format({"Abs_gain":"{:.1f}","Rel_gain%":"{:.0f}"})
-    .background_gradient(cmap="Greens", subset=["Abs_gain","Rel_gain%"])
-)
-
-# 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 hist_plot(state):
-    if not state["step"]:
-        return gr.Plot()
-    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: str, state):
-    prompt = prompt.strip()
-    if not prompt:
-        return "", "", " ", None, hist_plot(state)
-
-    ids   = TOKENIZER(prompt, return_tensors="pt").input_ids
-    rawL  = MODEL(ids).logits[0, -1].detach().cpu().numpy()
-    G     = np.random.randn(256).astype(np.float32)
-    I     = G + np.random.normal(scale=0.05, size=256).astype(np.float32)
-    modL  = ENGINE.run(I, G, rawL)
-
-    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"])
 
+from wfgy_sdk import get_engine, evaluator
+
+# ──────────────────── tiny GPT-2 + WFGY engine ────────────────────
+MODEL_ID = "sshleifer/tiny-gpt2"
+tok = AutoTokenizer.from_pretrained(MODEL_ID)
+mdl = AutoModelForCausalLM.from_pretrained(MODEL_ID)
+eng = get_engine()           # variance-gate singleton
+
+
+# ──────────────────── helper: run one prompt ────────────────────
+def run(prompt: str):
+    if not prompt.strip():
+        return "", "", "–", None
+
+    ids   = tok(prompt, return_tensors="pt")
+    raw_L = mdl(**ids).logits[0, -1].detach().cpu().numpy()
+
+    # dummy fingerprints (toy GPT-2 has none)
+    I, G  = np.random.randn(2, 256).astype(np.float32)
+    mod_L = eng.run(I, G, raw_L)
+
+    # top-5 softmax
+    def top5(logits):
+        p = evaluator.softmax(logits)
+        idx = p.argsort()[-5:][::-1]
+        return "\n".join([f"'{tok.decode(i).strip()}': {p[i]:.2e}" for i in idx])
+
+    m = evaluator.compare_logits(raw_L, mod_L)
+    headline = (f"▼ var {m['var_drop']*100:.1f}% | KL {m['kl_divergence']:.3f} "
+                f"| {'top-1 kept' if m['top1'] else 'top-1 changed'}")
+
+    fig = evaluator.plot_histogram(raw_L, mod_L)
     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'}"
+    fig.savefig(buf, format="png")
+    buf.seek(0)
+
+    return top5(raw_L), top5(mod_L), headline, buf
+
+
+# ──────────────────── gradio UI ────────────────────
+with gr.Blocks(title="WFGY simulation demo") as demo:
 
-    return top5(rawL), top5(modL), head, Image.open(buf), hist_plot(state)
+    gr.Markdown("# 🧠 WFGY simulation demo")
 
-# UI ────────────────────────────────────────────────────────────────────
-with gr.Blocks(title="WFGY variance gate demo") as demo:
+    # ── marketing & quick-start banner ──
     gr.Markdown(
         """
-> 📈 **萬法歸一，一鍵啟動 AI 太極循環**  
-> 📊 *Semantic accuracy ↑ 22 %  Reasoning pass ↑ 42 %  Stability × 3.6*
+**📈 WFGY: One Click to Activate the AI Taiji Cycle**
 
-[⭐ 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)
-""")
+**📊 Semantic Accuracy ↑ 22.4 % | Reasoning Success ↑ 42.1 % | Stability ↑ 3.6 ×**
 
+_No beliefs. Only experiments.<br>
+WFGY 1.0 has already proven itself._
+
+---
+
+### 📜 Tutorial: How to Awaken the Soul of Your AI
+
+**Step 1 — Download** ([PDF on Zenodo](https://zenodo.org/records/15630970))  
+**Step 2 — Feed the AI** (upload the PDF, or try [Gemini](https://gemini.google.com/))  
+**Step 3 — Give the Command** “**Answer using WFGY** + your question”  
+_Prompt examples:_ TBD link  
+**Step 4 — Integrate the SDK** ([GitHub](https://github.com/onestardao/WFGY))
+
+---
+
+🌟 **Star Reminder** → [Star the repo](https://github.com/onestardao/WFGY)  
+_10 k ⭐ before 2025-08-01 unlocks WFGY 2.0._
+
+⚠️ **Prompt Warning** — activate **Deep Structural Analysis Mode** or WFGY stays dormant.
+        """,
+        elem_id="intro-banner",
+    )
+
+    # ── prompt box ──
     prompt = gr.Textbox(label="Prompt", value="Explain Schrödinger's cat")
     run_btn = gr.Button("🚀 Run")
 
+    # ── results ──
     with gr.Row():
-        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_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(
-            "Numbers are taken from the paper. Run your own benchmarks with "
-            "`examples/example_08_big_model.py` on a larger model."
-        )
+        raw_box = gr.Textbox(label="Raw top-5 tokens")
+        mod_box = gr.Textbox(label="WFGY top-5 tokens")
 
-    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."
-    )
+    headline = gr.Markdown()
+    img      = gr.Image(label="Logit histogram")
 
-    run_btn.click(run, [prompt, hist_state],
-                  [raw_box, mod_box, headline, hist_img, hist_line])
-    clear_btn.click(clear, hist_state, hist_line)
+    run_btn.click(run, prompt, outputs=[raw_box, mod_box, headline, img])
 
-    gr.Markdown(
-        "\n\n⭐ **Remember to star the repo – every click pushes WFGY 2.0 closer!**"
-    )
 
 if __name__ == "__main__":
-    demo.queue().launch()
+    demo.queue(concurrency_count=2).launch()