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"""
HF Space · WFGY 1-click Variance Gate
"""
import gradio as gr, numpy as np, torch
from transformers import AutoModelForCausalLM, AutoTokenizer
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()
def run(prompt, enable, boost):
if not prompt.strip():
return gr.update(value="-", visible=True)
# raw logits
inputs = tok(prompt, return_tensors="pt")
rawL = mdl(**inputs).logits[0, -1].detach().cpu().float().numpy()
# demo-only fake semantic vectors
I = np.random.randn(256).astype(np.float32)
G = np.random.randn(256).astype(np.float32)
if enable:
modL = ENGINE.run(
logits = rawL,
input_vec = I,
ground_vec = G,
boost = boost,
)
else:
modL = rawL
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\nTurn GPT-2 into a calmer thinker. Move the slider → watch variance dive.")
prompt = gr.Textbox(label="Prompt")
enable = gr.Checkbox(value=True, label="Enable WFGY")
boost = gr.Slider(0.5, 3.0, value=1.0, label="Demo Boost (higher → bigger effect)")
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("")
run_btn.click(
run,
inputs=[prompt, enable, boost],
outputs=[out_raw, out_mod, headline],
)
demo.queue()
demo.launch()
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