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"""
WFGY HuggingFace Space – deluxe demo
* Generates text before/after WFGY
* Shows variance, KL, top-1 shift
* Renders overlay histogram
"""
import base64, io, numpy as np, gradio as gr, wfgy_sdk as w
from wfgy_sdk.evaluator import compare_logits
from wfgy_sdk.visual import plot_histogram
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
MODEL = "sshleifer/tiny-gpt2" # 124-MB, runs on CPU in ~2 s
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(MODEL)
set_seed(42)
ENGINE = w.get_engine() # singleton
def gen_text(prompt, max_new_tokens=40):
ids = tokenizer(prompt, return_tensors="pt").input_ids
with torch.no_grad():
out = model.generate(ids, max_new_tokens=max_new_tokens, do_sample=False)
return tokenizer.decode(out[0, ids.shape[1]:], skip_special_tokens=True)
def wfgy_demo(prompt, enable_wfgy):
# ---- generate raw text & logits ----
ids = tokenizer(prompt, return_tensors="pt").input_ids
with torch.no_grad():
output = model(ids)
raw_logits = output.logits[0, -1].cpu().numpy()
# dummy semantic vectors for demo
G = np.random.randn(256); G /= np.linalg.norm(G)
I = G + np.random.normal(scale=0.05, size=256)
# run WFGY
if enable_wfgy:
mod_logits = ENGINE.run(input_vec=I, ground_vec=G, logits=raw_logits)
else:
mod_logits = raw_logits.copy()
# decode next-token text for both versions
next_raw = tokenizer.decode(int(raw_logits.argmax()))
next_mod = tokenizer.decode(int(mod_logits.argmax()))
raw_txt = prompt + next_raw
mod_txt = prompt + next_mod
# metrics
m = compare_logits(raw_logits, mod_logits)
badge = f"variance ↓ {(1-m['std_ratio'])*100:.0f}% | KL {m['kl_divergence']:.2f}"
top1 = "✔" if m["top1_shift"] else "✘"
badge += f" | top-1 changed {top1}"
# histogram
fig = plot_histogram(raw_logits, mod_logits, show=False)
buf = io.BytesIO(); fig.savefig(buf, format="png"); fig.clf()
img_b64 = "data:image/png;base64," + base64.b64encode(buf.getvalue()).decode()
return raw_txt, mod_txt, badge, img_b64
with gr.Blocks(title="WFGY variance gate") as demo:
gr.Markdown("## WFGY Live Demo — variance drop in real-time")
prompt = gr.Textbox(label="Prompt", placeholder="Ask anything…", lines=2)
enable = gr.Checkbox(label="Enable WFGY", value=True)
run_btn = gr.Button("Run")
with gr.Row():
raw_out = gr.Textbox(label="Raw GPT-2")
mod_out = gr.Textbox(label="After WFGY")
metrics = gr.HTML(label="Metrics")
hist = gr.Image(label="Logit distribution", elem_id="hist", width=450)
run_btn.click(wfgy_demo, [prompt, enable],
[raw_out, mod_out, metrics, hist])
gr.Markdown(
"⭐ If the variance drop looks magic, [**star the repo**]"
"(https://github.com/onestardao/WFGY) and help unlock WFGY 2.0!"
)
demo.launch()
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