Upload app.py

app.py

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+import gradio as gr
+from fastai.vision.all import *
+from PIL import Image
+
+import fastai.losses
+import fastai.layers
+
+fastai.layers.BaseLoss = fastai.losses.BaseLoss
+fastai.layers.CrossEntropyLossFlat = fastai.losses.CrossEntropyLossFlat
+fastai.layers.BCEWithLogitsLossFlat = fastai.losses.BCEWithLogitsLossFlat
+fastai.layers.BCELossFlat = fastai.losses.BCELossFlat
+fastai.layers.MSELossFlat = fastai.losses.MSELossFlat
+fastai.layers.L1LossFlat = fastai.losses.L1LossFlat
+fastai.layers.LabelSmoothingCrossEntropy = fastai.losses.LabelSmoothingCrossEntropy
+fastai.layers.LabelSmoothingCrossEntropyFlat = fastai.losses.LabelSmoothingCrossEntropyFlat
+
+model = load_learner("model.pkl")
+def predict(im):
+    image_file = PILImage(PILImage.create((255-im)))
+    pred,pred_idx,probs = model.predict(image_file)
+    vals, indx = torch.topk(probs,2)
+    
+    return {model.dls.vocab[i]: prob.item() for prob,i in zip(vals,indx)}
+
+input_widget = gr.inputs.Image(image_mode="L", source="canvas", shape=((224,224)), invert_colors=True)
+# 
+# <b><span style="color:#fab114">some *blue* text</span>.</b>
+classes = ",".join(model.dls.vocab)
+article = f'currently supports <b><span style="color:#fab114">{classes}</span>.</b>'
+interface = gr.Interface(predict, title="Quickdraw", inputs=input_widget, outputs='label', live=True,article=article)
+
+interface.launch(debug=True)