The last try of the day

app.py

@@ -1,21 +1,21 @@
 from fastai.vision.all import *
-from huggingface_hub import push_to_hub_fastai, from_pretrained_fastai
 import gradio as gr
 
-repo_id = "robinsk8a/exotic_cars_classifier"
-learn = from_pretrained_fastai(repo_id)
+learn = load_learner('export.pkl')
 
 
-categories = ('Dog', 'Cat''ferrari','lamborghini','mclaren','aston martin','koenigsegg','porsche','pagani')
+labels = learn.dls.vocab
 
-def classify_image(img):
-    pred,idx,probs = learn.predict(img)
-    return dict(zip(categories, map(float,probs)))
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
 
 
-image = gr.inputs.Image(shape=(192, 192))
-label = gr.outputs.Label()
-examples=['bmw.jpg', 'lamborghini.jpg', 'koenigsegg.jpg'],
+title = "Car classifier"
+description = "A car classifier in which I've being spending so much time to make a working prototype online"
+examples = ['lamborghini.jpg', 'koenigsegg.jpg', '']
+interpretation='default'
+enable_queue=True
 
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
-intf.launch()
+gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()