Update app.py

app.py

@@ -9,13 +9,19 @@ 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))}
+title = "Skin Lesion Classifier [RESNET 50]"
+description = "A skin lesion classifier trained on the ISIC2019 dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
+article="<p style='text-align: center'><a href='https://challenge.isic-archive.com/data/' target='_blank'>Link to ISIC Dataset</a></p>"
+interpretation='default'
+enable_queue=True
+
 
 gr.Interface(
              fn=predict
             ,inputs=gr.inputs.Image(shape=(512, 512))
             ,outputs=gr.outputs.Label(num_top_classes=3)
             ,examples=['img1.jpg','img2.jpg','img3.jpg']
-            ).launch(share=False)
+            ,title=title,description=description,article=article,examples=['img1.jpg','img2.jpg','img3.jpg'],interpretation=interpretation,enable_queue=enable_queue).launch()
 
 
 
@@ -49,12 +55,6 @@ gr.Interface(
 #     pred,pred_idx,probs = learn.predict(img)
 #     return {labels[i]: float(probs[i]) for i in range(len(labels))}
 
-# title = "Skin Lesion Classifier [RESNET 50]"
-# description = "A skin lesion classifier trained on the ISIC2019 dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
-# article="<p style='text-align: center'><a href='https://challenge.isic-archive.com/data/' target='_blank'>Link to ISIC Dataset</a></p>"
-
-# interpretation='default'
-# enable_queue=True
 
 # examples = ['img1.jpg','img2.jpg','img3.jpg']