Upload 3 files

README.md

@@ -11,7 +11,7 @@ pinned: false
 
 # 🍦 Ice Cream Dataset + Counter — Gradio Space
 
-This is the **Gradio version** of the uploaded Ice Cream Counter project. It does **not** use Docker, FastAPI, Uvicorn, or a custom HTML frontend.
+This is the **Gradio + ZeroGPU-compatible** version of the uploaded Ice Cream Counter project. It does **not** use Docker, FastAPI, Uvicorn, or a custom HTML frontend.
 
 ## What it does
 
@@ -33,7 +33,9 @@ No YOLO is used.
 Create a new Space and choose:
 
 - **SDK:** Gradio
-- **Hardware:** GPU is strongly recommended for training
+- **Hardware:** ZeroGPU or a dedicated GPU is recommended for training
+
+The app uses `@spaces.GPU` for training and counting, so it also boots correctly when the Space hardware is **ZeroGPU**.
 
 Then upload these files/folders:
 

app.py

@@ -8,6 +8,7 @@ import threading
 from collections import Counter
 from pathlib import Path
 
+import spaces
 import gradio as gr
 import torch
 from PIL import Image, ImageDraw
@@ -304,6 +305,7 @@ def build_coco():
         )
 
 
+@spaces.GPU(duration=120)
 def run_training(epochs, batch_size, learning_rate):
     global _training, _model
     try:
@@ -344,6 +346,7 @@ def training_status():
     return json.dumps(_training, indent=2)
 
 
+@spaces.GPU(duration=60)
 def count_image(image):
     if image is None:
         return None, "Upload an image first.", {}
@@ -397,7 +400,7 @@ h1 { margin-bottom: 0.2rem !important; }
 .status { padding: 10px 14px; border-radius: 10px; }
 """
 
-with gr.Blocks(title="Ice Cream Dataset + Counter", css=CSS) as demo:
+with gr.Blocks(title="Ice Cream Dataset + Counter") as demo:
     gr.Markdown("# 🍦 Ice Cream Dataset + Counter\nUpload and annotate training images, train RT-DETR, then count ice creams in new images.")
     status = gr.Markdown(dataset_status(), elem_classes="status")
 
@@ -487,4 +490,8 @@ with gr.Blocks(title="Ice Cream Dataset + Counter", css=CSS) as demo:
               None, [status, image_select, ann_class])
 
 if __name__ == "__main__":
-    demo.queue().launch(server_name="0.0.0.0", server_port=int(os.getenv("PORT", "7860")))
+    demo.queue().launch(
+        server_name="0.0.0.0",
+        server_port=int(os.getenv("PORT", "7860")),
+        css=CSS,
+    )

requirements.txt

@@ -7,3 +7,5 @@ Pillow>=10.0
 numpy>=1.26
 tqdm>=4.66
 pycocotools>=2.0.8
+
+spaces>=0.40.0