Update app.py

app.py

@@ -1,69 +1,54 @@
 """
-HF Space · WFGY 1-click Variance Gate
+HF Space – WFGY 1-click Variance Gate
 """
-
-import gradio as gr
-import numpy as np
+import os, sys, numpy as np, gradio as gr
 import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoTokenizer, AutoModelForCausalLM
+# ensure repo root (so wfgy_sdk import works)
+sys.path.append(os.path.dirname(__file__))
+
 from wfgy_sdk import get_engine
 from wfgy_sdk.evaluator import compare_logits
 
-# ---------- tiny demo backbone ----------
-MODEL      = "sshleifer/tiny-gpt2"
-tokenizer  = AutoTokenizer.from_pretrained(MODEL)
-model      = AutoModelForCausalLM.from_pretrained(MODEL)
-ENGINE     = get_engine()
+MODEL  = "sshleifer/tiny-gpt2"
+tok    = AutoTokenizer.from_pretrained(MODEL)
+mdl    = AutoModelForCausalLM.from_pretrained(MODEL)
+ENGINE = get_engine()
 
-# ---------- core inference ----------
-def wfgy_run(prompt: str, enable: bool, boost: float):
+def run(prompt, enable, boost):
     if not prompt.strip():
-        return "—", "—", "Please enter a prompt."
+        return "-", "-", "⚠ Empty prompt"
 
-    # 1) raw logits from tiny-GPT-2
-    toks    = tokenizer(prompt, return_tensors="pt")
-    rawL    = model(**toks).logits[0, -1].detach().cpu().numpy()
+    toks   = tok(prompt, return_tensors="pt")
+    rawL   = mdl(**toks).logits[0, -1].detach().cpu().numpy()
 
-    # 2) dummy semantic vectors (demo only)
     G = np.random.randn(256).astype(np.float32)
-    I = (G + np.random.normal(scale=0.05, size=256).astype(np.float32)) * boost
+    I = G + np.random.normal(scale=0.05, size=256).astype(np.float32)
 
-    # 3) WFGY gate
     modL = ENGINE.run(I, G, rawL) if enable else rawL
 
-    # 4) text + metrics
-    raw_txt = prompt + tokenizer.decode(int(rawL.argmax()))
-    mod_txt = prompt + tokenizer.decode(int(modL.argmax()))
+    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 '✘'}"
-
+    headline = f"variance ▼ {int(m['var_drop']*100)} % | KL {m['kl']:.2f} | top-1 {'✔' if m['top1'] else '✘'}"
     return raw_txt, mod_txt, headline
 
-# ---------- UI ----------
-with gr.Blocks(title="WFGY 1-click Variance Gate", theme="soft") as demo:
-    gr.Markdown("## 🧠 **WFGY 1-click Variance Gate**  
-Turn GPT-2 into a calmer thinker. Move the slider → watch variance dive.\n")
-
-    prompt  = gr.Textbox(label="Prompt")
-    enable  = gr.Checkbox(value=True, label="Enable WFGY")
-    boost   = gr.Slider(0.5, 3.0, value=1.0, step=0.1,
-                        label="Demo Boost (higher → bigger effect)")
-
+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.")
+    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)")
     run_btn = gr.Button("Run", variant="primary")
-
     with gr.Row():
-        out_raw = gr.Textbox(label="Raw GPT-2")
-        out_mod = gr.Textbox(label="After WFGY")
-
-    headline = gr.Markdown("")
+        raw_box = gr.Textbox(label="Raw GPT-2")
+        mod_box = gr.Textbox(label="After WFGY")
+    headline = gr.Markdown()
 
-    run_btn.click(
-        wfgy_run,
-        inputs=[prompt, enable, boost],
-        outputs=[out_raw, out_mod, headline],
-    )
+    run_btn.click(run,
+                  inputs=[prompt, enable, boost],
+                  outputs=[raw_box, mod_box, headline])
 
-# ---------- launch ----------
 if __name__ == "__main__":
-    demo.queue()      # default concurrency = 2
-    demo.launch()
+    demo.queue().launch()     # no custom arguments