Update app.py

app.py

@@ -22,6 +22,11 @@ split_dataset = dataset['train'].train_test_split(test_size=0.2, seed=42)  # 80%
 dataset['train'] = split_dataset['train']
 dataset['test'] = split_dataset['test']
 
+# Example images
+example_images = {
+    "Example 1": "examples/example_1.jpg",
+}
+
 def create_url_from_title(title: str, timestamp: int):
     video_urls = load_dataset("eybro/video_urls")
     df = video_urls['train'].to_pandas()
@@ -90,7 +95,6 @@ def inference(image):
     result_image = get_image(top4[0])
     url = create_url_from_title(result_image['label'], result_image['timestamp'])
     result = f"{result_image['label']} {result_image['timestamp']} \n{url}"
-    #result =  f"[This is a link to the video]({url})"
     
     n=2
     plt.figure(figsize=(8, 8))
@@ -111,18 +115,46 @@ def inference(image):
         ax.get_yaxis().set_visible(False)
     
     return result
+
+def load_example(example_name):
+    if example_name in example_images:
+        image_path = example_images[example_name]
+        image = Image.open(image_path)
+        return image
+    return None
     
 with gr.Blocks() as demo:
     gr.Markdown(
-    """
-    # Image to Video App
-    Upload an image from a Gordon Ramsay show to find the corresponding youtube video
-    """)
-    with gr.Column(scale=1):
-        inp = gr.Image(label='Upload image')
-    with gr.Column(scale=1):
+        """
+        # Image to Video App
+        Find your favorite Gordon Ramasay scene by uploading an image from the scene, the app will thereafter find a corresponding youtube video for that scene. 
+        Or try one of our examples (unseen data for the model).
+        """
+    )
+    with gr.Row():
+        with gr.Column():
+            inp_image = gr.Image(label="Upload Image")
+        with gr.Column():
+            example_selection = gr.Gallery(
+                value=list(example_images.values()),
+                label="Click an Example Image",
+            ).style(grid=[1], height="auto")
+
+    with gr.Row():
         out = gr.Markdown()
-    inp.change(inference, inp, out)
+
+    def handle_selection(user_image, selected_example):
+        if user_image is not None:
+            return inference(user_image)
+        elif selected_example is not None:
+            image = load_example(selected_example)
+            return inference(image)
+        else:
+            return "Please upload an image or select an example image."
+
+    inputs = [inp_image, example_selection]
+    outputs = out
+    example_selection.select(handle_selection, inputs, outputs)
 
 if __name__ == "__main__":
     demo.launch()