redo load random image

app.py

@@ -5,6 +5,8 @@ import numpy as np
 from PIL import Image
 from transformers import CLIPProcessor, CLIPModel
 import pandas as pd
+from glob import glob
+from random import choice
 
 clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") 
 clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
@@ -40,20 +42,25 @@ def update_table_up(img, df, state):
 def update_table_down(img, df, state):
     return update_table(img, df, state, 0)
 
+def get_random_image(state):
+    idx = choice(len(state))
+    return state[idx], state[idx]
+
     
 with gr.Blocks(title="End-User Personalization") as demo:
-    train_images = gr.State(["dog.jpg", "colombia.jpg", "germany.jpg"])
+    images = gr.State(glob('/images/**/*.jpg'))
     # start_button = gr.Button(label="Start")
     image_display = gr.Image()
+    image_fname = gr.Textbox()
     # text_display = gr.Text()
     with gr.Row():
         upvote = gr.Button("👍")
         downvote = gr.Button("👎")
     annotated_samples = gr.Dataframe(headers=['image_name', 'label', 'image_embed'], row_count=(1, 'dynamic'), 
                                      col_count=(3, 'fixed'), label='Annotations', wrap=True)
-    upvote.click(update_table_up, inputs=[image_display, annotated_samples, train_images], outputs=[image_display, annotated_samples, train_images])
-    downvote.click(update_table_down, inputs=[image_display, annotated_samples, train_images], outputs=[image_display,annotated_samples, train_images])
-    examples = gr.Examples(examples=[["dog.jpg"], ["colombia.jpg"], ["germany.jpg"]], inputs=[image_display])
+    upvote.click(update_table_up, inputs=[image_display, annotated_samples, images], outputs=[image_display, annotated_samples, images])
+    downvote.click(update_table_down, inputs=[image_display, annotated_samples, images], outputs=[image_display,annotated_samples, images])
+    # examples = gr.Examples(examples=[["dog.jpg"], ["colombia.jpg"], ["germany.jpg"]], inputs=[image_display])
 
 # iface = gr.Interface(shot, 
 #                     ["image", "text"], 
@@ -63,5 +70,5 @@ with gr.Blocks(title="End-User Personalization") as demo:
 #                               ["colombia.jpg", "germany,belgium,colombia"]],
 #                     description="Add a picture and a list of labels separated by commas",
 #                     title="CLIP feature extractor")
-    demo.load(lambda: train_images.value[-1], inputs=[], outputs=image_display)
+    demo.load(get_random_image, inputs=[images], outputs=[image_display, image_fname])
 demo.launch(show_error=True, debug=True)