app.py

# 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

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)
    classes = learn.dls.vocab
    return {c: float(probs[i]) for i, c in enumerate(classes)}

demo = gr.Interface(
    fn=classify_image,
    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"]
)

if __name__ == "__main__":
    demo.launch()