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import gradio as gr
import numpy as np
import mplhep as hep
import matplotlib.pyplot as plt
import tensorflow as tf
from huggingface_hub import from_pretrained_keras
model_v1 = from_pretrained_keras("cicada-project/cicada-v1.1")
model_v2 = from_pretrained_keras("cicada-project/cicada-v2.1")
hep.style.use("CMS")
def parse_input(et):
if not et:
raise gr.Error("Provide the input")
et = [e.split(",") for e in et.split("\n")]
et = np.array(et)
et = et.astype(np.float32)
if et.shape != (18, 14):
raise gr.Error("The input shape has to be 18 rows and 14 columns")
if np.any(et < 0) or np.any(et > 1023):
raise gr.Error("The input has to be in a range (0, 1023)!")
return et.reshape(1, 252)
def saliency(input_, version):
x = tf.constant(input_)
with tf.GradientTape() as tape:
tape.watch(x)
if version == "v1":
predictions = model_v1(x)
elif version == "v2":
predictions = model_v2(x)
gradient = tape.gradient(predictions, x)
gradient = gradient.numpy()
min_val, max_val = np.min(gradient), np.max(gradient)
gradient = (gradient - min_val) / (max_val - min_val + tf.keras.backend.epsilon())
fig_s = plt.figure()
im = plt.imshow(gradient.reshape(18, 14), vmin=0., vmax=1., cmap="Greys")
ax = plt.gca()
cbar = ax.figure.colorbar(im, ax=ax)
cbar.ax.set_ylabel(r"Calorimeter Saliency (a.u.)")
plt.xticks(np.arange(14), labels=np.arange(4, 18))
plt.yticks(
np.arange(18),
labels=np.arange(18)[::-1],
rotation=90,
va="center",
)
plt.xlabel(r"i$\eta$")
plt.ylabel(r"i$\phi$")
return fig_s
def draw_input(input_):
fig_i = plt.figure()
im = plt.imshow(input_.reshape(18, 14), vmin=0, vmax=input_.max(), cmap="Purples")
ax = plt.gca()
cbar = ax.figure.colorbar(im, ax=ax)
cbar.ax.set_ylabel(r"Calorimeter E$_T$ deposit (GeV)")
plt.xticks(np.arange(14), labels=np.arange(4, 18))
plt.yticks(
np.arange(18),
labels=np.arange(18)[::-1],
rotation=90,
va="center",
)
plt.xlabel(r"i$\eta$")
plt.ylabel(r"i$\phi$")
return fig_i
def inference(input_, version):
if version == "v1":
return model_v1.predict(input_)[0][0]
elif version == "v2":
return model_v2.predict(input_)[0][0]
def generate():
matrix = np.clip(np.random.zipf(2, 252) - 1, 0, 1023)
matrix = matrix.reshape(18, 14).astype(str)
rows = [",".join(row) for row in matrix]
return "\n".join(rows)
def process_request(input_):
input_ = parse_input(input_)
return (
inference(input_, "v1"),
inference(input_, "v2"),
draw_input(input_),
saliency(input_, "v1"),
saliency(input_, "v2"),
)
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
input_ = gr.Textbox(
label="Calo Deposits",
lines=18,
placeholder="\n".join([",".join(["0"] * 14)] * 18),
)
with gr.Row():
generate_input = gr.Button("Generate random input")
magic = gr.Button("Do CICADA inference")
with gr.Column():
with gr.Row():
label_v1 = gr.Number(label="CICADA Anomaly Score for CICADA v1")
with gr.Row():
label_v2 = gr.Number(label="CICADA Anomaly Score for CICADA v2")
with gr.Row():
with gr.Tabs():
with gr.TabItem("Calorimeter Input"):
input_plot = gr.Plot()
with gr.TabItem("Saliency Map for CICADAv1"):
interpretation_plot_v1 = gr.Plot()
with gr.TabItem("Saliency Map for CICADAv2"):
interpretation_plot_v2 = gr.Plot()
generate_input.click(generate, None, input_)
magic.click(
process_request,
input_,
[
label_v1,
label_v2,
input_plot,
interpretation_plot_v1,
interpretation_plot_v2,
],
)
demo.launch()
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