updates app to more examples

app.py

@@ -24,14 +24,14 @@ The images are grouped into 12 classes.
 These classes represent common plant species in Danish agriculture at different growth stages.
  A pretrained ResNet34  model was fine-tuned using fastai vision library. The error rate achived on the validation dataset is 2.5% after 4 epochs.
 Currently the model does not generalize well on the out-of-domain images. Probably because of the specificity of the dataset. 
-The images in the dataset are of a single plant taken from the top and on a single background of a grit surface.
+The images in the dataset are of a single plant taken from the top and on a single background of a grit surface. Some images have labels that might be unique to the particular plant.
 <p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>
 </div>"""
 
 # %% ../weed_classifier.ipynb 6
 title = "Weed Classifier"
 description = "<h3 style='text-align: center'>A weed classifier trained on the Kaggle V2 Plant Seedling dataset with fastai.</h3>"
-examples = ['SugarBeet.png']
+examples = ['SugarBeet.png', 'SmallFloweredCranesBill.png', 'FatHen.png', 'LooseSilkyBent.png', 'CommonChickweed.png', 'MultipleSmallFloweredCranesBill.jpg', 'SmallFloweredCranesBill_02.png']
 interpretation='default'
 enable_queue=True