added debug messages

app.py

@@ -7,7 +7,8 @@ Any new model should implement the following functions:
 
 import os
 import glob
-#import spaces
+
+# import spaces
 import gradio as gr
 from huggingface_hub import get_token
 from utils import (
@@ -46,20 +47,26 @@ h1 {
 }
 """
 
-model = load_model("experimental/ahoy6-MIX-1280-b1.onnx")  
+model = load_model("experimental/ahoy6-MIX-1280-b1.onnx")
 model.det_conf_thresh = 0.1
 model.hor_conf_thresh = 0.1
 
+
 # @spaces.GPU
 def inference(image):
     """Run inference on image and return annotated image."""
     results = model(image)
     return results.draw(image)
 
-def flag_img_input(image: gr.Image, flag_option: str = "misdetection", username: str = "anonymous"):
+
+def flag_img_input(
+    image: gr.Image, flag_option: str = "misdetection", username: str = "anonymous"
+):
     """Wrapper for flagging"""
+    print(f"{image=}, {flag_option=}, {username=}")
     return hf_writer.flag([image], flag_option=flag_option, username=username)
 
+
 # Flagging
 dataset_name = "SEA-AI/crowdsourced-sea-images"
 hf_writer = HuggingFaceDatasetSaver(get_token(), dataset_name)
@@ -74,7 +81,10 @@ with gr.Blocks(theme=theme, css=css, title="SEA.AI Vision Demo") as demo:
     with gr.Row():
         with gr.Column():
             img_input = gr.Image(
-                label="input", interactive=True, sources=["upload", "clipboard"]
+                label="input",
+                interactive=True,
+                sources=["upload", "clipboard"],
+                type="numpy",
             )
             img_url = gr.Textbox(
                 lines=1,
@@ -140,9 +150,12 @@ with gr.Blocks(theme=theme, css=css, title="SEA.AI Vision Demo") as demo:
         [],
         preprocess=False,
         show_api=True,
-        api_name="flag_misdetection"
+        api_name="flag_misdetection",
     ).then(
-        lambda: load_badges(flagged_counter.count()), [], badges, show_api=False,
+        lambda: load_badges(flagged_counter.count()),
+        [],
+        badges,
+        show_api=False,
     )
 
     # called during initial load in browser

flagging.py

@@ -319,7 +319,9 @@ 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):
+                print(f"Before dirty hack: {deserialized=}")
                 deserialized = json.loads(deserialized)["path"]  # dirty hack
+                print(f"After dirty hack: {deserialized=}")
 
             # Add deserialized object to row
             features[label] = {"dtype": "string", "_type": "Value"}