Create app.py

app.py

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+import gradio as gr
+from fastai.vision.all import *
+from huggingface_hub import from_pretrained_fastai
+import torch, os
+import traceback
+
+
+os.environ.setdefault("OMP_NUM_THREADS", "1")
+torch.set_num_threads(1)
+
+learn = from_pretrained_fastai("Pablogps/kedar-200-birds-10-per-cent")
+try:
+    learn.to_fp32()
+except:
+    pass
+labels = learn.dls.vocab
+
+def predict(img):
+    try:
+        # img = PILImage.create(img)
+        pred, pred_idx, probs = learn.predict(img)
+        return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    except Exception as e:
+        tb = traceback.format_exc()
+        print(tb, flush=True)
+        raise gr.Error(f"{type(e).__name__}: {e}")
+
+title = "Bad castle predictor"
+description = "A bad model that tries to identify the type of castle."
+examples = ['american_crow.jpg', 'barn_swallow.jpg', 'pied_kingfisher.jpg']
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title=title,
+    description=description,
+    # examples=examples,
+    cache_examples=False,       # <-- don’t pre-run at startup
+)
+
+demo.queue(max_size=8).launch(show_error=True, debug=True)