+ caching model

+ displaying basic hist plot

app.py

@@ -1,38 +1,63 @@
+from functools import cache
 from pickle import load
 
 import gradio as gr
+import matplotlib.pyplot as plt
 import numpy as np
 import torch
 
 from scorer import build_model
 
 
+@cache
+def load_model(device):
+    return build_model(device=device)
+
+@cache
+def load_reference_scores(gmmdir='models'):
+    with np.load(f"{gmmdir}/refscores.npz", "rb") as f:
+        ref_nll = f["arr_0"]
+    return ref_nll
+
 def compute_gmm_likelihood(x_score, gmmdir='models'):
     with open(f"{gmmdir}/gmm.pkl", "rb") as f:
         clf = load(f)
         nll = -clf.score(x_score)
 
-    with np.load(f"{gmmdir}/refscores.npz", "wb") as f:
-        ref_nll = f["arr_0"]
-        percentile = (ref_nll < nll).mean() * 100
+    ref_nll = load_reference_scores(gmmdir)
+    percentile = (ref_nll < nll).mean() * 100
 
     return nll, percentile
 
-def run_inference(img):
+def plot_against_reference(nll):
+    ref_nll = load_reference_scores()
+    print(ref_nll.shape)
+    fig, ax = plt.subplots()
+    ax.hist(ref_nll)
+    ax.axvline(nll, label='Image Score', c='red', ls="--")
+    plt.legend()
+    fig.tight_layout()
+    return fig
+
+def run_inference(img, device='cuda'):
     img = torch.from_numpy(img).permute(2,0,1).unsqueeze(0)
     img = torch.nn.functional.interpolate(img, size=64, mode='bilinear')
-    model = build_model(device='cuda')
+    model = load_model(device=device)
     x = model(img.cuda())
     x = x.square().sum(dim=(2, 3, 4)) ** 0.5
     nll, pct = compute_gmm_likelihood(x.cpu())
 
-    return f"Image of shape: {img.shape} -> {nll:.3f}@{pct:.2f}"
+    plot = plot_against_reference(nll)
+    print(plot)
+    outstr = f"Anomaly score: {nll:.3f} -> {pct:.2f} percentile"
+    return outstr, plot
 
 
 demo = gr.Interface(
     fn=run_inference,
     inputs=["image"],
-    outputs=["text"],
+    outputs=["text", gr.Plot()],
 )
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()