Update app.py

app.py

@@ -1,6 +1,11 @@
 import gradio as gr
 from fastai.vision.all import *
 import skimage
+#Importing necessary libraries
+import gradio as gr
+#import scikit-learn as sklearn
+from fastai.vision.all import *
+from sklearn.metrics import roc_auc_score
 
 learn = load_learner('export.pkl')
 
@@ -12,9 +17,18 @@ def predict(img):
 
 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://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
-examples = ['siamese.jpg']
+
 interpretation='default'
 enable_queue=True
 
-gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(224, 224)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()
+examples = ['img1.jpg','img2.jpg','img3.jpg']
+
+#Launching the gradio application
+gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(224, 224)),
+             outputs=gr.outputs.Label(num_top_classes=1),
+             title=title,
+             description=description,article=article,
+             examples=examples,
+             enable_queue=enable_queue).launch(inline=False)
+
+#gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(224, 224)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()