fixed other

app.py

@@ -1,24 +1,105 @@
-import gradio as gr
-from fastai.vision.all import *
-import skimage
 
+import torch, torch.nn as nn
+from fastai.vision.all import *
+import gradio as gr
 
+# ---------- 1. load the trained learner ----------
 learn = load_learner('bear_classifier.pkl')
+#embedding_model = nn.Sequential(*list(learn.model.children())[:-2])  # Match notebook logic here
+
+
+learn.model.eval()                 # inference mode
+device = default_device()          # GPU if available
+
+#embedding_model.to(device).eval()                  # move to GPU
+
+
+# ---------- 2. build the embedding extractor ----------
+embedding_model = nn.Sequential(
+    learn.model[0],                # CNN backbone
+    nn.AdaptiveAvgPool2d(1),       # [B, C, 1, 1]
+    nn.Flatten()                   # [B, 512]
+).to(device).eval()
+
+# ---------- 3. get or compute class-mean embeddings ----------
+try:
+    # if you saved them to disk:
+    cls_means = torch.load('class_means.pt', map_location='cpu', weights_only=False)
+    grizzly_mean = cls_means['grizzly']
+    black_mean   = cls_means['black']
+    teddy_mean   = cls_means['teddy']
+except FileNotFoundError:
+    # recompute from the learner’s training dataloader
+    print('class_means.pt not found – recomputing on the fly…')
+    embs, lbls = [], []
+    for xb, yb in learn.dls.train:
+        with torch.no_grad():
+            xb = xb.to(device)
+            feats = embedding_model(xb).cpu()
+        embs.append(feats)
+        lbls.append(yb.cpu())
+    embs  = torch.cat(embs)
+    lbls  = torch.cat(lbls)
+    grizzly_mean = embs[lbls == 0].mean(0)
+    black_mean   = embs[lbls == 1].mean(0)
+    teddy_mean   = embs[lbls == 2].mean(0)
+    torch.save({'grizzly': grizzly_mean,
+                'black'  : black_mean,
+                'teddy'  : teddy_mean}, 'class_means.pt')
+
+# ---------- 4. helper functions ----------
+DIST_THRESHOLD = 0.2                          # tune to taste
+
+def get_embedding_tensor(pil_img):
+    """Convert PIL → tensor → 512-d embedding"""
+    dl  = learn.dls.test_dl([pil_img])
+    xb  = dl.one_batch()[0].to(device)          # shape [1,3,H,W]
+    with torch.no_grad():
+        emb = embedding_model(xb)[0]            # shape [512]
+    return emb.cpu()
+
+
+
+def predict_embedding_ood_from_img(pil_img,
+                                   distance_threshold=DIST_THRESHOLD):
+    emb = get_embedding_tensor(pil_img)
+
+    d_grizzly = 1 - torch.nn.functional.cosine_similarity(
+        emb, grizzly_mean, dim=0)
+    d_black   = 1 - torch.nn.functional.cosine_similarity(
+        emb, black_mean, dim=0)
+    d_teddy   = 1 - torch.nn.functional.cosine_similarity(
+        emb, teddy_mean, dim=0)
 
-labels = learn.dls.vocab
+    distances = {'Grizzly': d_grizzly.item(),
+                 'Black'  : d_black.item(),
+                 'Teddy'  : d_teddy.item()}
 
-def predict(img):
-    img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    closest_class, closest_dist = min(distances.items(),
+                                      key=lambda kv: kv[1])
 
-title = "Bear Classifier"
-description = "A bear classifier trained on some images downloaded from ddg with fastai. Created as a demo for Gradio and HuggingFace Spaces."
-examples = ['grizzly.jpg', 'black.jpg', 'teddy.jpg', 'f1.jpg']
-interpretation='default'
-enable_queue=True
+    if closest_dist < distance_threshold:
+        return f"{closest_class} (the distance is {closest_dist:.2f})"
+    else:
+        return (f"This is an image of something other than a bear (grizzly distance {d_grizzly.item():.2f}, black distance {d_black.item():.2f}, teddy distance {d_teddy.item():.2f})")
+        #return "This is an image of something other than a bear"
 
-img = gr.Image()
-lbl = gr.Label()
+# ---------- 5. Gradio interface ----------
+demo = gr.Interface(
+    fn=predict_embedding_ood_from_img,
+    inputs=gr.Image(type='pil'),
+    outputs=gr.Textbox(),
+    title="Bear Classifier with OOD Detection",
+    description=("Predicts Grizzly / Black / Teddy bear, "
+                 "or returns Other if the image is far from all three "
+                 "class prototypes."),
+    examples=[
+        ['grizzly.jpg'],
+        ['black.jpg'],
+        ['teddy.jpg'],
+        ['f1.jpg']
+    ]
+)
 
-gr.Interface(fn=predict,inputs=img,outputs=lbl,title=title,description=description,examples=examples).launch()
+if __name__ == "__main__":
+    demo.launch()

bear_classifier.pkl

@@ -1,3 +1,3 @@
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-oid sha256:afd8cecb9d55e266e82492ca822d7627544fb6f22f2b6f55dc2fa3331fc9bc9a
-size 87482744
+oid sha256:7cf53ff828bb524fc62bf2c14c7810de19b48b090ad336954c500a537dfc0db3
+size 87478514

export.pkl → class_means.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:826b49fb79d63196975da000a660d1e8bc013e31be68c98e9efadd6cb223a6c2
-size 46978302
+oid sha256:ea80f1004a02a20d047bcab3790de1823671f13c513a410d90cb7406a004cae7
+size 7984

requirements.txt

@@ -5,5 +5,4 @@ torch
 torchvision
 fastcore<1.8,>=1.5.29
 fsspec<=2025.3.0,>=2023.1.0
-fasttransform
 cloudpickle