updated examples loading

app.py

@@ -1,21 +1,18 @@
 import gradio as gr
 from utils.predict import predict, predict_batch
 import os
-
+import glob
 inputs_list = []
 
 
 demo = gr.Blocks()
+example_list = glob.glob("examples/set2/*")
+example_list = list(map(lambda el:[el], example_list))
+sample_image = gr.Image(type='filepath')
+examples = gr.components.Dataset(components=[sample_image], samples=example_list, type='values')
 
-# sample_1 = ['examples/ship.png']
-# sample_2 = ['examples/deer.jpg']
-# sample_image = gr.Image(type='filepath')
-# examples = gr.components.Dataset(components=[sample_image], samples=[sample_1, sample_2], type='values')
-# with gr.Column():
-            #     examples.render()
-            #     examples.click(load_example, examples, input_images)
-# def load_example(image):
-#     return image[0]
+def load_example(image):
+    return image[0]
     
 with demo:
     
@@ -58,8 +55,12 @@ with demo:
             gr.Markdown("**Examples:**")
             gr.Markdown("The model is trained to classify images belonging to the following classes:")
 
+            # with gr.Column():
+            #     gr.Examples("examples/set2", [input_image], output_label, predict, cache_examples=True)
+
             with gr.Column():
-                gr.Examples("examples/set2", [input_image], output_label, predict, cache_examples=True)
+                examples.render()
+                examples.click(load_example, examples, input_image)
         
     
     compute_button.click(predict_batch, inputs=input_image, outputs=[output_plot,output_df])