Create app.py

app.py

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+import gradio as gr
+from fastai.vision.all import *
+import skimage
+learn = load_learner("export.pkl")
+
+labels = learn.dls.vocab
+
+def classify_garbage(img):
+    img = PILImage.create(img)
+    pred,idx,probs  = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+image = gr.inputs.Image(shape = (224,224))
+label = gr.outputs.Label(num_top_classes=10)
+title = "Garbage Classifier"
+description = "A Garbage classifier trained with fastai. Created as a demo for Gradio and HuggingFace Spaces."
+interpretation='default'
+enable_queue=True
+
+iface = gr.Interface(fn=classify_garbage, inputs=image, outputs=label,title=title,description=description,interpretation=interpretation,enable_queue=enable_queue)
+iface.launch(inline=False)