typo

app.py

@@ -19,10 +19,10 @@ def predict(img):
     return {labels[i]: float(probs[i]) for i in range(len(labels))}
 
 title = "Northern EU Mushroom Classifier"
-description = "<p style='text-align: center, font-size:14px'>A North EU mushroom image classifier trained on a kaggle dataset with fastai. The dataset consist of 9 different folders that contains from 300 to 1500 selected images of mushrooms genuses.</p>"
-article="<p style='text-align: center, font-size:14px'><a href='https://www.kaggle.com/datasets/maysee/mushrooms-classification-common-genuss-images' target='_blank'>Data Source</a></h4>"
+description = "<p style='text-align: center; font-size:14px'>A North EU mushroom image classifier trained on a kaggle dataset with fastai. The dataset consist of 9 different folders that contains from 300 to 1500 selected images of mushrooms genuses.</p>"
+article="<p style='text-align: center; font-size:14px'><a href='https://www.kaggle.com/datasets/maysee/mushrooms-classification-common-genuss-images' target='_blank'>Data Source</a></h4>"
 interpretation='default'
 enable_queue=True
 inputs = gr.Image(shape=(224, 224))
 
-gr.Interface(fn=predict, inputs=inputs, outputs=gr.Label(num_top_classes=3), title=title, description=description, article=article, interpretation=interpretation, examples=examples).launch(inline=False, enable_queue=enable_queue)
+gr.Interface(fn=predict, inputs=inputs, outputs=gr.Label(num_top_classes=3), title=title, description=description, article=article, interpretation=interpretation, examples=examples).launch(inline=False, share=True, enable_queue=enable_queue)