Update app.py

app.py

@@ -2,12 +2,12 @@ import gradio as gr
 from transformers import ViTFeatureExtractor, ViTForImageClassification
 from PIL import Image
 import requests
-ts_url = 'https://static.promediateknologi.id/crop/0x0:0x0/0x0/webp/photo/p2/01/2023/08/10/taylor-swift-3169402579.png'
-ts_image = Image.open(requests.get(ts_url, stream=True).raw)
-pg_url = "https://media.vogue.co.uk/photos/60f888a382e60565201c7cf4/2:3/w_2560%2Cc_limit/prince-Grorge-eigth-birthday-portrait.jpg"
-pg_image = Image.open(requests.get(pg_url, stream=True).raw)
-img_url = "https://thumbs.dreamstime.com/b/old-man-20313005.jpg"
-img_image = Image.open(requests.get(img_url, stream=True).raw)
+img1_url = 'https://static3.depositphotos.com/1003410/160/i/450/depositphotos_1607848-stock-photo-portrait-of-old-man.jpg'
+img1 = Image.open(requests.get(img1_url, stream=True).raw)
+img2_url = "https://img.freepik.com/free-photo/excited-screaming-young-woman-standing-isolated-yellow_176420-39645.jpg"
+img2 = Image.open(requests.get(img2_url, stream=True).raw)
+img3_url = "https://img.freepik.com/free-photo/cute-adorable-boy-studio_58702-7629.jpg"
+img3 = Image.open(requests.get(img3_url, stream=True).raw)
 
 def age_emot_classifier(input_image):
 
@@ -43,6 +43,6 @@ output_emotion = gr.Label(num_top_classes=8, label="Emotion Prediction")
 
 with gr.Blocks(theme=gr.themes.Glass()) as demo:
     gr.Interface(fn=age_emot_classifier, inputs="image", outputs=[output_age, output_emotion],
-                 examples=[ts_image, pg_image, img_image])
+                 examples=[img1, img2, img3])
 
 demo.launch()