Update setup.py

setup.py

@@ -1,24 +1,74 @@
-from setuptools import setup, find_packages
+"""
+WFGY · Metrics & Visuals
+Pure-NumPy + Matplotlib helpers
+"""
 
-setup(
-    name="wfgy_sdk",
-    version="1.0.1",
-    description="WFGY 1.0 • Self-Healing LLM Framework SDK",
-    author="PSBigBig",
-    author_email="hello@onestardao.com",
-    url="https://github.com/onestardao/WFGY",
-    packages=find_packages(include=["wfgy_sdk", "wfgy_sdk.*"]),
-    python_requires=">=3.10",
-    install_requires=[
-        "numpy<2.0",
-        "torch==2.2.2",
-        "transformers==4.41.2",
-        "tabulate",
-        "matplotlib",
-    ],
-    entry_points={
-        "console_scripts": [
-            "wfgy=wfgy_sdk.cli:main",
-        ]
-    },
-)
+import numpy as np
+import matplotlib.pyplot as plt
+from tabulate import tabulate
+
+
+# ───────────────────────────────────────────────
+# Stats helpers
+# ───────────────────────────────────────────────
+
+def _safe_std(x: np.ndarray) -> float:
+    s = float(np.std(x))
+    return s if s > 0 else 1e-12
+
+
+def softmax(x: np.ndarray) -> np.ndarray:
+    z = x - x.max()
+    e = np.exp(z)
+    return e / e.sum()
+
+
+# ───────────────────────────────────────────────
+# Public API  ──  must return keys expected by CI
+# ───────────────────────────────────────────────
+
+def compare_logits(old: np.ndarray, new: np.ndarray) -> dict:
+    sr = _safe_std(new) / _safe_std(old)          # std ratio
+    var_drop = 1.0 - sr
+    p, q = softmax(old), softmax(new)
+    kl = float(np.sum(p * np.log((p + 1e-8) / (q + 1e-8))))  # KL divergence
+    top1_same = int(old.argmax() == new.argmax())
+
+    return {
+        "std_ratio": sr,
+        "var_drop": var_drop,
+        "kl_divergence": kl,   # <── CI & Space both want this exact key
+        "top1": top1_same,
+    }
+
+
+# ───────────────────────────────────────────────
+# Pretty print for CLI demo
+# ───────────────────────────────────────────────
+
+def pretty_print(m: dict) -> str:
+    tbl = tabulate(
+        [[f"{m['std_ratio']:.3f}",
+          f"{m['var_drop']*100:4.1f} %",
+          f"{m['kl_divergence']:.3f}",
+          "✔" if m['top1'] else "✘"]],
+        headers=["std_ratio", "▼ var", "KL", "Top-1"],
+        tablefmt="github",
+    )
+    return tbl
+
+
+# ───────────────────────────────────────────────
+# Histogram figure
+# ───────────────────────────────────────────────
+
+def plot_histogram(old: np.ndarray, new: np.ndarray, bins: int = 50) -> plt.Figure:
+    fig, ax = plt.subplots(figsize=(6, 3.5), dpi=110)
+    ax.hist(old, bins=bins, alpha=0.6, label="Raw", log=True)
+    ax.hist(new, bins=bins, alpha=0.6, label="WFGY", log=True)
+    ax.set_title("Logit Distribution (log-scale)")
+    ax.set_xlabel("logit value")
+    ax.set_ylabel("frequency")
+    ax.legend()
+    fig.tight_layout()
+    return fig