Update app.py

app.py

@@ -1,58 +1,66 @@
 import io, traceback, numpy as np, gradio as gr, matplotlib
 matplotlib.use("Agg")
-
 from PIL import Image
 from transformers import AutoTokenizer, AutoModelForCausalLM
 from wfgy_sdk import get_engine
 from wfgy_sdk.evaluator import compare_logits, plot_histogram
+from tabulate import tabulate
 
 MODEL = "sshleifer/tiny-gpt2"
-tok = AutoTokenizer.from_pretrained(MODEL)
-mdl = AutoModelForCausalLM.from_pretrained(MODEL)
-eng = get_engine()
-
+tok   = AutoTokenizer.from_pretrained(MODEL)
+mdl   = AutoModelForCausalLM.from_pretrained(MODEL)
+eng   = get_engine()
 
 def run(prompt: str):
     prompt = prompt.strip()
     if not prompt:
-        return "", "", "no prompt – nothing to show", None
+        return "", "", "", None, None
     try:
-        ids = tok(prompt, return_tensors="pt").input_ids
-        raw = mdl(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)
-        mod = eng.run(I, G, raw)
-        m = compare_logits(raw, mod)
-        headline = f"▼ var {m['var_drop']*100:4.1f}% | KL {m['kl']:.3f}"
-
-        fig = plot_histogram(raw, mod)
-        buf = io.BytesIO()
-        fig.savefig(buf, format="png")
-        buf.seek(0)
+        ids  = tok(prompt, return_tensors="pt").input_ids
+        rawL = mdl(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 = eng.run(I, G, rawL)
+
+        m = compare_logits(rawL, modL)
+        tbl = tabulate(
+            [[f"{m['std_ratio']:.3f}",
+              f"{m['var_drop']*100:4.1f} %",
+              f"{m['kl']:.3f}",
+              "✔" if m['top1'] else "✘"]],
+            headers=["std_ratio", "▼ var", "KL", "top-1"],
+            tablefmt="github")
+        headline = f"▼ var {m['var_drop']*100:4.1f} % | KL {m['kl']:.3f}"
+
+        fig = plot_histogram(rawL, modL)
+        buf = io.BytesIO(); fig.savefig(buf, format="png"); buf.seek(0)
         img = Image.open(buf)
 
-        raw_txt = prompt + tok.decode(int(raw.argmax()))
-        mod_txt = prompt + tok.decode(int(mod.argmax()))
-        return raw_txt, mod_txt, headline, img
+        raw_txt = prompt + tok.decode(int(rawL.argmax()))
+        mod_txt = prompt + tok.decode(int(modL.argmax()))
+        return raw_txt, mod_txt, headline, tbl, img
     except Exception:
         tb = traceback.format_exc()
-        return "runtime error", tb, "runtime error", None
-
+        return "runtime error", tb, "runtime error", "", None
 
 with gr.Blocks(title="WFGY variance gate") as demo:
     gr.Markdown("# 🧠 WFGY simulation demo")
     prompt = gr.Textbox(label="Prompt", value="Explain Schrödinger's cat")
-    btn = gr.Button("🚀 Run")
+    btn    = gr.Button("🚀 Run")
 
     with gr.Row():
         raw_box = gr.Textbox(label="Raw GPT-2")
         mod_box = gr.Textbox(label="After WFGY")
+
     headline = gr.Markdown()
-    img = gr.Image(label="Logit histogram", type="pil")
+    metrics  = gr.Markdown()           #  ← 新增數值表
+    img      = gr.Image(label="Logit histogram", type="pil")
 
-    btn.click(run, prompt, [raw_box, mod_box, headline, img])
+    btn.click(run, prompt,
+              [raw_box, mod_box, headline, metrics, img])
 
-    gr.Markdown("---\n### ⭐ 10 000 stars → unlock **WFGY 2.0** by 2025-08-01")
+    gr.Markdown("---\n"
+                "### ⭐ 10 000 stars → unlock **WFGY 2.0** by 2025-08-01")
 
 if __name__ == "__main__":
     demo.queue().launch()