Refine Gradio app startup and layout

Switch the UI from `gr.Interface` to a `gr.Blocks` layout with an explicit Run button, lazy-cached examples, and serialized queueing to avoid startup/download issues and PyTorch concurrency problems. The MegaDetector selector is now shown only for non-PyTorch backends, display settings are grouped under an accordion, and the bundled examples were cleaned up and expanded.

app.py

@@ -13,8 +13,7 @@ import dlclibrary
 import dlclive
 # import transformers
 
-from PIL import Image, ImageColor, ImageFont, ImageDraw 
-import requests
+from PIL import Image, ImageColor, ImageFont, ImageDraw
 
 from viz_utils import save_results_as_json, draw_keypoints_on_image, draw_bbox_w_text, save_results_only_dlc, save_results_pytorch
 from detection_utils import predict_md, crop_animal_detections
@@ -35,10 +34,6 @@ from dlclive import DLCLive, Processor
 #train_dir = 'DLC_models/sa-tvm'
 #download_huggingface_model(model, train_dir)
 
-# grab demo data cooco cat:
-url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-image = Image.open(requests.get(url, stream=True).raw)
-
 # megadetector and dlc model look up
 MD_models_dict = {'md_v5a': "MD_models/md_v5a.0.0.pt", # 
                   'md_v5b': "MD_models/md_v5b.0.0.pt"}
@@ -228,22 +223,36 @@ def predict_pipeline(img_input,
 
 #########################################################
 # Define user interface and launch
-inputs = gradio_inputs_for_MD_DLC(BACKENDS,
-                                  list(MD_models_dict.keys()),
-                                  list(DLC_models_dict.keys()))
-outputs = gradio_outputs_for_MD_DLC()                                    
-[gr_title, 
- gr_description, 
+[gr_title,
+ gr_description,
  examples] = gradio_description_and_examples()
 
-# launch
-demo = gr.Interface(predict_pipeline, 
-                    inputs=inputs,
-                    outputs=outputs, 
-                    title=gr_title, 
-                    description=gr_description,
-                    examples=examples,
-                    )
-
-demo.queue()
+with gr.Blocks(title=gr_title) as demo:
+    gr.Markdown(f"# {gr_title}\n{gr_description}")
+    with gr.Row():
+        with gr.Column():
+            inputs = gradio_inputs_for_MD_DLC(BACKENDS,
+                                              list(MD_models_dict.keys()),
+                                              list(DLC_models_dict.keys()))
+            run_button = gr.Button("Run", variant="primary")
+        with gr.Column():
+            outputs = gradio_outputs_for_MD_DLC()
+
+    # the MegaDetector choice only applies to the TensorFlow (legacy) backend
+    gr_backend_input, gr_mega_model_input = inputs[1], inputs[2]
+    gr_backend_input.change(lambda backend: gr.update(visible=backend != "PyTorch"),
+                            inputs=gr_backend_input,
+                            outputs=gr_mega_model_input)
+
+    run_button.click(predict_pipeline, inputs=inputs, outputs=outputs, api_name="predict")
+
+    # cached on first click, so a failing download cannot block startup
+    gr.Examples(examples,
+                inputs=inputs,
+                outputs=outputs,
+                fn=predict_pipeline,
+                cache_examples=True,
+                cache_mode="lazy")
+
+demo.queue(default_concurrency_limit=1)  # PyTorch runners are not thread-safe
 demo.launch(theme=gr.themes.Default())

ui_utils.py

@@ -17,6 +17,7 @@ def gradio_inputs_for_MD_DLC(backends_list, md_models_list, dlc_models_list):
         value="md_v5a",
         type="value",
         label="Select Detector model (TensorFlow legacy only)",
+        visible=gr_backend_input.value != "PyTorch",
     )
 
     gr_dlc_model_input = gr.Dropdown(
@@ -32,11 +33,6 @@ def gradio_inputs_for_MD_DLC(backends_list, md_models_list, dlc_models_list):
         label="Run DeepLabCut only, directly on input image?",
     )
 
-    gr_str_labels_checkbox = gr.Checkbox(
-        value=True,
-        label="Show bodypart labels?",
-    )
-
     # Gradio Slider signature is (minimum, maximum, value, step, ...)
     gr_slider_conf_bboxes = gr.Slider(
         minimum=0,
@@ -55,33 +51,39 @@ def gradio_inputs_for_MD_DLC(backends_list, md_models_list, dlc_models_list):
     )
 
     # Data viz
-    gr_keypt_color = gr.ColorPicker(
-        value="#862db7",
-        label="Choose color for keypoint label",
-    )
-
-    gr_labels_font_style = gr.Dropdown(
-        choices=["amiko", "animals", "nature", "painter", "zen"],
-        value="amiko",
-        type="value",
-        label="Select keypoint label font",
-    )
-
-    gr_slider_font_size = gr.Slider(
-        minimum=5,
-        maximum=30,
-        value=8,
-        step=1,
-        label="Set font size",
-    )
-
-    gr_slider_marker_size = gr.Slider(
-        minimum=1,
-        maximum=20,
-        value=9,
-        step=1,
-        label="Set marker size",
-    )
+    with gr.Accordion("Display options", open=False):
+        gr_str_labels_checkbox = gr.Checkbox(
+            value=True,
+            label="Show bodypart labels?",
+        )
+
+        gr_keypt_color = gr.ColorPicker(
+            value="#862db7",
+            label="Choose color for keypoint label",
+        )
+
+        gr_labels_font_style = gr.Dropdown(
+            choices=["amiko", "animals", "nature", "painter", "zen"],
+            value="amiko",
+            type="value",
+            label="Select keypoint label font",
+        )
+
+        gr_slider_font_size = gr.Slider(
+            minimum=5,
+            maximum=30,
+            value=8,
+            step=1,
+            label="Set font size",
+        )
+
+        gr_slider_marker_size = gr.Slider(
+            minimum=1,
+            maximum=20,
+            value=9,
+            step=1,
+            label="Set marker size",
+        )
 
     return [
         gr_image_input,
@@ -116,19 +118,9 @@ def gradio_description_and_examples():
         "<a href='http://www.mackenziemathislab.org/dlc-modelzoo'>DeepLabCut ModelZoo</a>."
     )
 
-    examples = [[
-        "examples/dog.jpeg",
-        "PyTorch",
-        "md_v5a",
-        "superanimal_quadruped",
-        False,
-        True,
-        0.5,
-        0.0,
-        "amiko",
-        9,
-        "#ff0000",
-        3,
-    ]]
+    examples = [
+        [image, "PyTorch", "md_v5a", "superanimal_quadruped", False, True, 0.5, 0.4, "amiko", 10, "#ff0000", 5]
+        for image in ("examples/dog.jpeg", "examples/cat.jpg")
+    ]
 
     return [title, description, examples]