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da37721 4ee7112 da37721 4ee7112 da37721 4ee7112 da37721 c84ff05 da37721 4ee7112 da37721 c84ff05 da37721 4ee7112 da37721 cca1081 da37721 4ee7112 8dd7bca da37721 cca1081 4ee7112 da37721 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | import numpy as np
import keras
from PIL import Image
import gradio as gr
MODEL_PATH = "small32cnn_mlbt_mmat.keras"
STATS_PATH = "jet_image_scale_and_stats.npz"
model = keras.models.load_model(MODEL_PATH, compile=False)
stats = np.load(STATS_PATH)
SCALE = float(stats["SCALE"])
MEAN = float(stats["MEAN"])
STD = float(stats["STD"])
print("Loaded SCALE/MEAN/STD:", SCALE, MEAN, STD, flush=True)
CLASS_NAMES = ["MMAT", "MLBT"]
# ---- Preprocessing: Image -> (1, 32, 32, 1) normalized ----
def preprocess(img: np.ndarray) -> np.ndarray:
if img.ndim == 3 and img.shape[2] == 3:
img_gray = np.dot(img[..., :3], [0.2989, 0.5870, 0.1140])
elif img.ndim == 3 and img.shape[2] == 1:
img_gray = img[..., 0]
elif img.ndim == 2:
img_gray = img
else:
img_gray = img[..., 0]
pil_img = Image.fromarray(img_gray.astype("uint8"))
pil_img = pil_img.resize((32, 32), Image.BILINEAR)
arr = np.array(pil_img).astype("float32")
# Invert the global scaling to approximate original X
arr_unscaled = arr / SCALE
# Now apply the same normalization as during training
arr_norm = (arr_unscaled - MEAN) / (STD + 1e-8)
arr_norm = arr_norm[None, ..., None]
return arr_norm
# ---- Prediction function for Gradio ----
def predict(img: np.ndarray):
x = preprocess(img)
raw = float(model.predict(x, verbose=0)[0, 0])
print("Raw model output:", raw, flush=True)
# raw ≈ P(MLBT) as in training
prob_mlbt = raw
prob_mmat = 1.0 - prob_mlbt
return {"MLBT": prob_mlbt, "MMAT": prob_mmat}
# ---- Gradio interface ----
input_component = gr.Image(
type="numpy",
label="Jet image (grayscale or RGB, any size ≥ 32x32)"
)
output_component = gr.Label(
num_top_classes=2,
label="Predicted probabilities"
)
examples = [] # you can drop some example jet PNGs here later
demo = gr.Interface(
fn=predict,
inputs=input_component,
outputs=output_component,
title="MLBT vs MMAT Jet Classifier (Small 32×32 CNN)",
description=(
"Upload a jet image (32×32 heatmap or larger) and this model "
"predicts whether it came from the MLBT or MMAT energy-loss module."
),
examples=examples
)
if __name__ == "__main__":
demo.launch()
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