trying pipeline

app.py

@@ -1,19 +1,15 @@
-from fastai.vision.all import *
-from huggingface_hub import push_to_hub_fastai, from_pretrained_fastai
 import gradio as gr
+from transformers import pipeline
 
-learn = from_pretrained_fastai("robinsk8a/exotic_cars_classifier")
+pipeline = pipeline(task="image-classification", model="robinsk8a/exotic_cars_classifier")
 
-categories = ('Dog', 'Cat''ferrari','lamborghini','mclaren','aston martin','koenigsegg','porsche','pagani')
+def predict(image):
+  predictions = pipeline(image)
+  return {p["label"]: p["score"] for p in predictions}
 
-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()
+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()