hide flagging option when displaying examples

app.py

@@ -1,14 +1,20 @@
+"""
+Main application for RGB detection demo.
+Any new model should implement the following functions:
+- load_model(model_path, img_size=640)
+- inference(model, image)
+"""
+import os
 import glob
 import gradio as gr
 from huggingface_hub import get_token
 from utils import (
-    load_model,
     load_image_from_url,
-    inference,
     load_badges,
     FlaggedCounter,
 )
 from flagging import myHuggingFaceDatasetSaver
+from model_yolov5 import load_model, inference
 
 
 TITLE = """
@@ -69,7 +75,7 @@ with gr.Blocks(theme=theme, css=css) as demo:
             flag = gr.Button("Flag", visible=False)
             notice = gr.Markdown(value=NOTICE, visible=False)
 
-    gr.Examples(
+    examples = gr.Examples(
         examples=glob.glob("examples/*.jpg"),
         inputs=img_input,
         outputs=img_output,
@@ -90,11 +96,17 @@ with gr.Blocks(theme=theme, css=css) as demo:
     )
 
     # event listeners with decorators
-    @img_output.change(inputs=[img_output], outputs=[flag, notice], show_api=False)
-    def show_hide(_img_ouput):
-        visible = _img_ouput is not None
+    @img_output.change(
+        inputs=[img_input, img_output],
+        outputs=[flag, notice],
+        show_api=False,
+        preprocess=False,
+        show_progress="hidden",
+    )
+    def _show_hide_flagging(_img_input, _img_output):
+        visible = _img_output and _img_input["orig_name"] not in os.listdir("examples")
         return {
-            flag: gr.Button("Flag", visible=visible, interactive=True),
+            flag: gr.Button("Flag", interactive=True, visible=visible),
             notice: gr.Markdown(value=NOTICE, visible=visible),
         }
 
@@ -102,7 +114,7 @@ with gr.Blocks(theme=theme, css=css) as demo:
     hf_writer.setup([img_input], "flagged")
 
     # Sequential logic when flag button is clicked
-    flag.click(lambda: gr.Info("Thank you for contributing!"), show_api=False).then(
+    flag.click(lambda: gr.Info("Thank you for contributing!")).then(
         lambda: {flag: gr.Button("Flag", interactive=False)}, [], [flag], show_api=False
     ).then(
         lambda *args: hf_writer.flag(args),
@@ -118,4 +130,4 @@ with gr.Blocks(theme=theme, css=css) as demo:
     demo.load(lambda: load_badges(flagged_counter.count()), [], badges, show_api=False)
 
 if __name__ == "__main__":
-    demo.queue().launch(share=True)  # show_api=False)
+    demo.queue().launch()

flagging.py

@@ -1,3 +1,4 @@
+import os
 import json
 from collections import OrderedDict
 from pathlib import Path
@@ -6,6 +7,7 @@ import gradio as gr
 from gradio.flagging import HuggingFaceDatasetSaver, client_utils
 import huggingface_hub
 
+
 class myHuggingFaceDatasetSaver(HuggingFaceDatasetSaver):
     """
     Custom HuggingFaceDatasetSaver to save images/audio to disk.
@@ -14,7 +16,7 @@ class myHuggingFaceDatasetSaver(HuggingFaceDatasetSaver):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-    
+
     def _deserialize_components(
         self,
         data_dir: Path,
@@ -39,7 +41,7 @@ class myHuggingFaceDatasetSaver(HuggingFaceDatasetSaver):
             save_dir.mkdir(exist_ok=True, parents=True)
             deserialized = component.flag(sample, save_dir)
             if isinstance(component, gr.Image) and isinstance(sample, dict):
-                deserialized = json.loads(deserialized)['path']  # dirty hack
+                deserialized = json.loads(deserialized)["path"]  # dirty hack
 
             # Add deserialized object to row
             features[label] = {"dtype": "string", "_type": "Value"}
@@ -58,7 +60,8 @@ class myHuggingFaceDatasetSaver(HuggingFaceDatasetSaver):
                         features[label + " file"] = {"_type": _type}
                         break
                 if deserialized:
-                    path_in_repo = str(  # returned filepath is absolute, we want it relative to compute URL
+                    path_in_repo = str(
+                        # returned filepath is absolute, we want it relative to compute URL
                         Path(deserialized).relative_to(self.dataset_dir)
                     ).replace("\\", "/")
                     row.append(
@@ -74,4 +77,4 @@ class myHuggingFaceDatasetSaver(HuggingFaceDatasetSaver):
         features["username"] = {"dtype": "string", "_type": "Value"}
         row.append(flag_option)
         row.append(username)
-        return features, row
+        return features, row

model_yolov5.py

@@ -0,0 +1,22 @@
+import numpy as np
+import yolov5
+from yolov5.utils.plots import Annotator, colors
+from huggingface_hub import get_token
+
+
+def load_model(model_path, img_size=640):
+    """Load model from HuggingFace Hub."""
+    model = yolov5.load(model_path, hf_token=get_token())
+    model.img_size = img_size  # add img_size attribute
+    return model
+
+
+def inference(model, image):
+    """Run inference on image and return annotated image."""
+    results = model(image, size=model.img_size)
+    annotator = Annotator(np.asarray(image))
+    for *box, _, cls in reversed(results.pred[0]):
+        # label = f'{model.names[int(cls)]} {conf:.2f}'
+        # print(f'{cls} {conf:.2f} {box}')
+        annotator.box_label(box, "", color=colors(cls, True))
+    return annotator.im

utils.py

@@ -2,22 +2,12 @@ import time
 import requests
 from io import BytesIO
 from dataclasses import dataclass
-import numpy as np
 import pandas as pd
 from PIL import Image
-import yolov5
-from yolov5.utils.plots import Annotator, colors
 import gradio as gr
 from huggingface_hub import get_token
 
 
-def load_model(model_path, img_size=640):
-    """Load model from HuggingFace Hub."""
-    model = yolov5.load(model_path, hf_token=get_token())
-    model.img_size = img_size  # add img_size attribute
-    return model
-
-
 def load_image_from_url(url):
     """Load image from URL."""
     if not url:  # empty or None
@@ -30,17 +20,6 @@ def load_image_from_url(url):
     return image.convert("RGB")
 
 
-def inference(model, image):
-    """Run inference on image and return annotated image."""
-    results = model(image, size=model.img_size)
-    annotator = Annotator(np.asarray(image))
-    for *box, _, cls in reversed(results.pred[0]):
-        # label = f'{model.names[int(cls)]} {conf:.2f}'
-        # print(f'{cls} {conf:.2f} {box}')
-        annotator.box_label(box, "", color=colors(cls, True))
-    return annotator.im
-
-
 def load_badges(n):
     """Load badges."""
     return f"""
@@ -80,7 +59,7 @@ class FlaggedCounter:
                 if "error" not in data and data["size"]["dataset"]["num_rows"] > 0:
                     print(f"[{i+1}/{self.trials}] {data}")
                     return data["size"]["dataset"]["num_rows"]
-            except Exception:
+            except requests.exceptions.RequestException:
                 pass
             print(f"[{i+1}/{self.trials}] {data}")
             time.sleep(5)