Upload app.py

app.py

@@ -29,15 +29,17 @@ cls_model_id = DEFAULT_CLS_MODEL_ID
 def get_examples(task):
     examples = []
     Path(EXAMPLE_IMAGE_DIR).mkdir(parents=True, exist_ok=True)
+    image_ind = 0
     for model_id in task_to_model_ids[task]:
         dataset_id = get_dataset_id_from_model_id(model_id)
         ds = load_dataset(dataset_id, name="mini")["validation"]
         for ind in range(min(2, len(ds))):
             jpeg_image_file = ds[ind]["image"]
-            image_file_path = str(Path(EXAMPLE_IMAGE_DIR) / f"{task}_example_{ind}.jpg")
+            image_file_path = str(Path(EXAMPLE_IMAGE_DIR) / f"{task}_example_{image_ind}.jpg")
             jpeg_image_file.save(image_file_path, format='JPEG', quality=100)
             image_path = os.path.abspath(image_file_path)
             examples.append([image_path, model_id, 0.25])
+            image_ind += 1
     return examples
 
 
@@ -124,13 +126,23 @@ with gr.Blocks() as demo:
             with gr.Column():
                 detect_output = gr.Image(label="Predictions:", interactive=False)
         with gr.Row():
-            detect_examples = gr.Examples(
-                    det_examples,
-                    inputs=[detect_input, detect_model_id, detect_threshold],
-                    outputs=detect_output,
-                    fn=predict,
-                    cache_examples=False,
-                )
+            half_ind = int(len(det_examples) / 2)
+            with gr.Column():
+                detect_examples = gr.Examples(
+                        det_examples[:half_ind],
+                        inputs=[detect_input, detect_model_id, detect_threshold],
+                        outputs=detect_output,
+                        fn=predict,
+                        cache_examples=False,
+                    )
+            with gr.Column():
+                detect_examples = gr.Examples(
+                        det_examples[:half_ind],
+                        inputs=[detect_input, detect_model_id, detect_threshold],
+                        outputs=detect_output,
+                        fn=predict,
+                        cache_examples=False,
+                    )
     with gr.Tab("Segmentation"):
         with gr.Row():
             with gr.Column():
@@ -141,13 +153,23 @@ with gr.Blocks() as demo:
             with gr.Column():
                 segment_output = gr.Image(label="Predictions:", interactive=False)
         with gr.Row():
-            segment_examples = gr.Examples(
-                    seg_examples,
-                    inputs=[segment_input, segment_model_id, segment_threshold],
-                    outputs=segment_output,
-                    fn=predict,
-                    cache_examples=False,
-                )
+            half_ind = int(len(det_examples) / 2)
+            with gr.Column():
+                segment_examples = gr.Examples(
+                        seg_examples[:half_ind],
+                        inputs=[segment_input, segment_model_id, segment_threshold],
+                        outputs=segment_output,
+                        fn=predict,
+                        cache_examples=False,
+                    )
+            with gr.Column():
+                segment_examples = gr.Examples(
+                        seg_examples[:half_ind],
+                        inputs=[segment_input, segment_model_id, segment_threshold],
+                        outputs=segment_output,
+                        fn=predict,
+                        cache_examples=False,
+                    )
     with gr.Tab("Classification"):
         with gr.Row():
             with gr.Column():
@@ -160,13 +182,23 @@ with gr.Blocks() as demo:
                     label="Predictions:", show_label=True, num_top_classes=5
                 )
         with gr.Row():
-            classify_examples = gr.Examples(
-                    cls_examples,
-                    inputs=[classify_input, classify_model_id, classify_threshold],
-                    outputs=classify_output,
-                    fn=predict,
-                    cache_examples=False,
-                )
+            half_ind = int(len(det_examples) / 2)
+            with gr.Column():
+                classify_examples = gr.Examples(
+                        cls_examples[half_ind:],
+                        inputs=[classify_input, classify_model_id, classify_threshold],
+                        outputs=classify_output,
+                        fn=predict,
+                        cache_examples=False,
+                    )
+            with gr.Column():
+                classify_examples = gr.Examples(
+                        cls_examples[:half_ind],
+                        inputs=[classify_input, classify_model_id, classify_threshold],
+                        outputs=classify_output,
+                        fn=predict,
+                        cache_examples=False,
+                    )
 
     detect_button.click(
         predict, inputs=[detect_input, detect_model_id, detect_threshold], outputs=detect_output