app.py

import io, numpy as np, matplotlib
matplotlib.use("Agg")

from PIL import Image
import pandas as pd, plotly.express as px, gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from wfgy_sdk import get_engine
from wfgy_sdk.evaluator import compare_logits, plot_histogram

# tiny model (CPU-friendly demo)
tok = AutoTokenizer.from_pretrained("sshleifer/tiny-gpt2")
mdl = AutoModelForCausalLM.from_pretrained("sshleifer/tiny-gpt2")
eng = get_engine()

# history buffer
history = {"step": [0], "var": [0.0], "kl": [0.0]}

# paper table
paper_df = pd.DataFrame({
    "Benchmark": ["MMLU","GSM8K","BBH","MathBench","TruthfulQA",
                  "XNLI","MLQA","LongBench","VQAv2","OK-VQA"],
    "Baseline":  [61.0,78.0,79.3,72.2,62.4,59.5,78.1,51.4,69.1,65.7],
    "WFGY":      [89.8,98.7,100.7,87.4,90.4,77.3,106.6,69.6,86.6,86.8]
})
paper_df["Abs_gain"]  = (paper_df["WFGY"]-paper_df["Baseline"]).round(1)
paper_df["Rel_gain%"] = ((paper_df["Abs_gain"]/paper_df["Baseline"])*100).round(0)

styled_df = (
    paper_df.style
    .background_gradient(cmap="Greens", subset=["Abs_gain","Rel_gain%"])
    .format({"Abs_gain":"{:.1f}","Rel_gain%":"{:.0f}"})
)

paper_bar = px.bar(
    paper_df, x="Benchmark", y="Rel_gain%",
    title="Relative gain (%)", color="Rel_gain%",
    color_continuous_scale="Greens", height=300
)

# helpers
def top5(logits: np.ndarray):
    p = torch.softmax(torch.tensor(logits), dim=0).numpy()
    idx = p.argsort()[-5:][::-1]
    return "\n".join([f"{tok.decode(int(i))!r}: {p[i]:.2e}" for i in idx])

def hist_plot():
    df = pd.DataFrame(history)
    return px.line(df, x="step", y=["var","kl"],
                   labels={"value":"metric","step":"call"},
                   title="history (var% ↓  &  KL)").update_layout(height=260)

def clear_hist():
    history["step"][:] = [0]; history["var"][:]=[0.0]; history["kl"][:]=[0.0]
    return hist_plot()

def run(prompt: str):
    p = prompt.strip()
    if not p:
        return "", "", "", "", None, hist_plot()

    ids  = tok(p, return_tensors="pt").input_ids
    rawL = mdl(ids).logits[0,-1].detach().cpu().numpy()
    G    = np.random.randn(256).astype(np.float32)
    I    = G + np.random.normal(scale=0.05,size=256).astype(np.float32)
    modL = eng.run(I,G,rawL)

    m = compare_logits(rawL,modL)
    n  = len(history["step"])
    history["step"].append(n); history["var"].append(m["var_drop"]*100); history["kl"].append(m["kl"])

    fig = plot_histogram(rawL,modL); buf=io.BytesIO(); fig.savefig(buf,format="png"); buf.seek(0)

    head = f"▼ var {m['var_drop']*100:4.1f}% | KL {m['kl']:.3f} | top-1 {'kept' if m['top1'] else 'changed'}"
    return top5(rawL), top5(modL), head, Image.open(buf), hist_plot()

# UI
with gr.Blocks(title="WFGY variance gate demo") as demo:
    gr.Markdown("# 🧠 WFGY simulation demo")
    prompt = gr.Textbox(label="Prompt", value="Explain Schrödinger's cat")
    run_b  = gr.Button("🚀 Run")

    with gr.Row():
        raw_box = gr.Textbox(label="Raw top-5 tokens", lines=6)
        mod_box = gr.Textbox(label="WFGY top-5 tokens", lines=6)

    headline = gr.Markdown()
    hist_img = gr.Image(type="pil", label="Logit histogram")
    hist_p   = gr.Plot()
    clr_b    = gr.Button("Clear history")

    with gr.Accordion("Paper benchmarks", open=False):
        gr.DataFrame(styled_df, interactive=False, wrap=True)
        gr.Plot(paper_bar)

    gr.Markdown("---\n⭐ **10 k GitHub stars before 2025-08-01 unlock WFGY 2.0**")

    run_b.click(run, prompt, [raw_box,mod_box,headline,hist_img,hist_p])
    clr_b.click(clear_hist, None, hist_p)

if __name__ == "__main__":
    demo.queue().launch()