more changes

app.py

@@ -1,23 +1,47 @@
-from fastai.vision.all import *
+# app.py
+import os, shutil
+
+# keep all caches ephemeral
+os.environ["HF_HOME"]="/tmp/hf"
+os.environ["TRANSFORMERS_CACHE"]="/tmp/hf/transformers"
+os.environ["TORCH_HOME"]="/tmp/torch"
+os.environ["PIP_NO_CACHE_DIR"]="1"
+for d in ["/home/user/.cache", "/home/user/.fastai", "/home/user/.torch"]:
+    shutil.rmtree(d, ignore_errors=True)
+
+from functools import lru_cache
+from huggingface_hub import hf_hub_download
+from fastai.vision.all import load_learner
 import gradio as gr
 
-learn = load_learner('model.pkl')
-categories = ('Black', 'Grizzly', 'Teddy')
+REPO_ID = "daleef/my-fastai-bear"   # your model repo
+FILENAME = "model.pkl"
+
+@lru_cache(maxsize=1)
+def get_learner():
+    pkl_path = hf_hub_download(
+        repo_id=REPO_ID,
+        filename=FILENAME,
+        local_dir="/tmp/model",
+        local_dir_use_symlinks=False
+    )
+    return load_learner(pkl_path)
 
 def classify_image(img):
+    learn = get_learner()
     pred, idx, probs = learn.predict(img)
-    return dict(zip(categories, map(float, probs)))
-
-# Modern API (Gradio 4.x)
-image = gr.Image(type="pil", height=192, width=192, label="Upload an Image")
-label = gr.Label()
-examples = ['black.jpg', 'bear.jpg', 'teddy.jpg']
+    classes = learn.dls.vocab
+    return {c: float(probs[i]) for i, c in enumerate(classes)}
 
-intf = gr.Interface(
+demo = gr.Interface(
     fn=classify_image,
-    inputs=image,
-    outputs=label,
-    examples=examples
+    inputs=gr.Image(type="pil", height=192, width=192, label="Upload an image"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Fastai Bear Classifier",
+    description="Upload a bear image to classify."
+    # Tip: remove examples unless those files exist in the repo
+    # examples=["black.jpg","bear.jpg","teddy.jpg"]
 )
 
-intf.launch()
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -1,2 +1,3 @@
 fastai
-gradio
+gradio
+huggingface_hub