more examples

app.py

@@ -31,14 +31,18 @@ url = "https://upload.wikimedia.org/wikipedia/commons/thumb/6/63/Monarch_In_May.
 path_input = "./d_plexippus.jpg"
 urllib.request.urlretrieve(url, filename=path_input)
 
-url = "https://drive.google.com/uc?id=130VEfv--EYtVCVoHo4zt4lrKaWzP6w74"
-path_input = "./j_coenia.jpg"
+url = "https://drive.google.com/uc?id=1A7WgDrQ_RLO6JOQiYhkH_hj_EKcbpmOl"
+path_input = "./v_cardui.jpg"
 urllib.request.urlretrieve(url, filename=path_input)
 
 url = "https://drive.google.com/uc?id=1CiWShQYIm2N0fkVaWJpftlXZFqwjsXhA"
 path_input = "./p_cresphontes.jpg"
 urllib.request.urlretrieve(url, filename=path_input)
 
+url = "https://drive.google.com/uc?id=1r8rbkUwTSIZL0MQVgU-WjDGwvLXuwYPG"
+path_input = "./p_rapae.jpg"
+urllib.request.urlretrieve(url, filename=path_input)
+
 
 # normalisation
 data_transforms_test = transforms.Compose([
@@ -96,7 +100,7 @@ im = gr.inputs.Image(shape=(512, 512), image_mode='RGB',
 
 title = "Butterfly Classification Demo"
 description = "A pretrained ResNet18 CNN trained on the Leeds Butterfly Dataset. Libraries: PyTorch, Gradio."
-examples = [['./h_erato.jpg'],['d_plexippus.jpg'],['j_coenia.jpg'],['p_cresphontes.jpg']]
+examples = [['./h_erato.jpg'],['d_plexippus.jpg'],['v_cardui.jpg'],['p_cresphontes.jpg'],['p_rapae.jpg']]
 article_text = markdown.markdown('''
 
 <h1 style="color:white">PyTorch image classification - A pretrained ResNet18 CNN trained on the <a href="http://www.josiahwang.com/dataset/leedsbutterfly/">Leeds Butterfly Dataset</a></h1>