dev(narugo): add gradio demo for classifiers

docs/source/api_doc/generic/classify.rst

@@ -11,7 +11,7 @@ ClassifyModel
 -----------------------------------------
 
 .. autoclass:: ClassifyModel
-    :members: __init__, predict_score, predict, clear
+    :members: __init__, predict_score, predict, clear, make_ui, launch_demo
 
 
 

imgutils/generic/classify.py

@@ -23,19 +23,37 @@
 
 import numpy as np
 from PIL import Image
+from hfutils.operate import get_hf_client
+from hfutils.repository import hf_hub_repo_url
 from hfutils.utils import hf_fs_path, hf_normpath
 from huggingface_hub import hf_hub_download, HfFileSystem
 
 from ..data import rgb_encode, ImageTyping, load_image
 from ..utils import open_onnx_model
 
+try:
+    import gradio as gr
+except (ImportError, ModuleNotFoundError):
+    gr = None
+
 __all__ = [
     'ClassifyModel',
     'classify_predict_score',
     'classify_predict',
 ]
 
 
+def _check_gradio_env():
+    """
+    Check if the Gradio library is installed and available.
+
+    :raises EnvironmentError: If Gradio is not installed.
+    """
+    if gr is None:
+        raise EnvironmentError(f'Gradio required for launching webui-based demo.\n'
+                               f'Please install it with `pip install dghs-imgutils[demo]`.')
+
+
 def _img_encode(image: Image.Image, size: Tuple[int, int] = (384, 384),
                 normalize: Optional[Tuple[float, float]] = (0.5, 0.5)):
     """
@@ -287,6 +305,61 @@ def clear(self):
         self._models.clear()
         self._labels.clear()
 
+    def make_ui(self, default_model_name: Optional[str] = None):
+        _check_gradio_env()
+        model_list = self.model_names
+        if not default_model_name:
+            hf_client = get_hf_client(hf_token=self._get_hf_token())
+            selected_model_name, selected_time = None, None
+            for fileitem in hf_client.get_paths_info(
+                    repo_id=self.repo_id,
+                    repo_type='model',
+                    paths=[f'{model_name}/model.onnx' for model_name in model_list],
+                    expand=True,
+            ):
+                if not selected_time or fileitem.last_commit.date > selected_time:
+                    selected_model_name = os.path.dirname(fileitem.path)
+                    selected_time = fileitem.last_commit.date
+            default_model_name = selected_model_name
+
+        with gr.Row():
+            with gr.Column():
+                gr_input_image = gr.Image(type='pil', label='Original Image')
+                gr_model = gr.Dropdown(model_list, value=default_model_name, label='Model')
+                gr_submit = gr.Button(value='Submit', variant='primary')
+
+            with gr.Column():
+                gr_output = gr.Label(label='Prediction')
+
+            gr_submit.click(
+                self.predict_score,
+                inputs=[
+                    gr_input_image,
+                    gr_model,
+                ],
+                outputs=[gr_output],
+            )
+
+    def launch_demo(self, default_model_name: Optional[str] = None,
+                    server_name: Optional[str] = None, server_port: Optional[int] = None, **kwargs):
+        _check_gradio_env()
+        with gr.Blocks() as demo:
+            with gr.Row():
+                with gr.Column():
+                    repo_url = hf_hub_repo_url(repo_id=self.repo_id, repo_type='model')
+                    gr.HTML(f'<h2 style="text-align: center;">Classifier Demo For {self.repo_id}</h2>')
+                    gr.Markdown(f'This is the quick demo for classifier model [{self.repo_id}]({repo_url}). '
+                                f'Powered by `dghs-imgutils`\'s quick demo module.')
+
+            with gr.Row():
+                self.make_ui(default_model_name=default_model_name)
+
+        demo.launch(
+            server_name=server_name,
+            server_port=server_port,
+            **kwargs,
+        )
+
 
 @lru_cache()
 def _open_models_for_repo_id(repo_id: str, hf_token: Optional[str] = None) -> ClassifyModel: