Update app.py

app.py

@@ -1,54 +1,61 @@
 """
-HF Space – WFGY 1-click Variance Gate
+HF Space · WFGY 1-click Variance Gate
 """
-import os, sys, numpy as np, gradio as gr
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-# ensure repo root (so wfgy_sdk import works)
-sys.path.append(os.path.dirname(__file__))
 
+import gradio as gr, numpy as np
+from transformers import AutoTokenizer, AutoModelForCausalLM
 from wfgy_sdk import get_engine
 from wfgy_sdk.evaluator import compare_logits
 
 MODEL  = "sshleifer/tiny-gpt2"
 tok    = AutoTokenizer.from_pretrained(MODEL)
 mdl    = AutoModelForCausalLM.from_pretrained(MODEL)
-ENGINE = get_engine()
+ENG    = get_engine()
 
 def run(prompt, enable, boost):
     if not prompt.strip():
-        return "-", "-", "⚠ Empty prompt"
+        return "-", "-", "Please enter a prompt."
 
-    toks   = tok(prompt, return_tensors="pt")
-    rawL   = mdl(**toks).logits[0, -1].detach().cpu().numpy()
+    inputs = tok(prompt, return_tensors="pt")
+    rawL   = mdl(**inputs).logits[0, -1].detach().cpu().numpy()
 
+    # demo-only fake semantic vectors
+    I = np.random.randn(256).astype(np.float32)
     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) if enable else rawL
+    modL = ENG.run(I, G, rawL) if enable else rawL
+    mets = compare_logits(rawL, modL)
+
+    headline = f"variance ▼ {int(mets['var_drop']*100)} % | " \
+               f"KL {mets['kl']:.2f} | " \
+               f"top-1 {'✔' if mets['top1'] else '✘'}"
+
+    return (
+        prompt + tok.decode(int(rawL.argmax())),
+        prompt + tok.decode(int(modL.argmax())),
+        headline,
+    )
 
-    raw_txt = prompt + tok.decode(int(rawL.argmax()))
-    mod_txt = prompt + tok.decode(int(modL.argmax()))
-    m       = compare_logits(rawL, modL)
-    headline = f"variance ▼ {int(m['var_drop']*100)} % | KL {m['kl']:.2f} | top-1 {'✔' if m['top1'] else '✘'}"
-    return raw_txt, mod_txt, headline
 
 with gr.Blocks(title="WFGY 1-click Variance Gate") as demo:
     gr.Markdown("## 🧠 WFGY 1-click Variance Gate\n"
-                "Turn GPT-2 into a calmer thinker. Move the slider → watch variance dive.")
+                "Turn GPT-2 into a calmer thinker. "
+                "Move the slider → watch variance dive.")
+
     prompt = gr.Textbox(label="Prompt")
     enable = gr.Checkbox(True, label="Enable WFGY")
-    boost  = gr.Slider(0.5, 3.0, 1.0, step=0.1,
-                       label="Demo Boost (placeholder – effect fixed in tiny demo)")
+    boost  = gr.Slider(0.5, 3.0, 1.0, label="Demo Boost (visual only)")
+
     run_btn = gr.Button("Run", variant="primary")
+
     with gr.Row():
-        raw_box = gr.Textbox(label="Raw GPT-2")
-        mod_box = gr.Textbox(label="After WFGY")
+        out_raw = gr.Textbox(label="Raw GPT-2")
+        out_mod = gr.Textbox(label="After WFGY")
+
     headline = gr.Markdown()
 
-    run_btn.click(run,
-                  inputs=[prompt, enable, boost],
-                  outputs=[raw_box, mod_box, headline])
+    run_btn.click(run, [prompt, enable, boost],
+                  [out_raw, out_mod, headline])
 
 if __name__ == "__main__":
-    demo.queue().launch()     # no custom arguments
+    demo.queue().launch()