one more...

app.py

@@ -1,15 +1,21 @@
+from fastai.vision.all import *
+from huggingface_hub import push_to_hub_fastai, from_pretrained_fastai
 import gradio as gr
-from transformers import pipeline
 
-pipeline = pipeline(task="image-classification", model="robinsk8a/exotic_cars_classifier")
+repo_id = "robinsk8a/exotic_cars_classifier"
+learn = from_pretrained_fastai(repo_id)
 
-def predict(image):
-  predictions = pipeline(image)
-  return {p["label"]: p["score"] for p in predictions}
 
-gr.Interface(
-    predict,
-    inputs=gr.inputs.Image(label="Upload the car you want to guess", type="filepath"),
-    outputs=gr.outputs.Label(num_top_classes=3),
-    title="Exotic car classifier",
-).launch()
+categories = ('Dog', 'Cat''ferrari','lamborghini','mclaren','aston martin','koenigsegg','porsche','pagani')
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
+
+
+image = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label()
+examples=['bmw.jpg', 'lamborghini.jpg', 'koenigsegg.jpg'],
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch()