Deploy free ZeroGPU FitTest with private responses

README.md

@@ -1,13 +1,15 @@
 ---
-title: FitTest 24
-emoji: 🦀
-colorFrom: red
-colorTo: gray
+title: FitTest
+emoji: 📐
+colorFrom: blue
+colorTo: indigo
 sdk: gradio
-sdk_version: 6.30.0
-python_version: '3.12'
 app_file: app.py
-pinned: false
 ---
+# FitTest
+24 visual fitting questions, one question at a time. Judge A, B, and C independently; at least one option fits.
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Hosted on the free ZeroGPU tier. This study does not run model inference or request GPU work.
+Submissions go to a private dataset via the server-side HF_TOKEN secret. RESPONSES_REPO identifies the dataset. No answer key or response-reading endpoint is included.
+
+Participants should complete the study in one session. Previous/Next retain answers during the session. Final Submit confirms successful storage. No names, emails, or IP addresses are recorded in the submission files.

app.py

@@ -0,0 +1,56 @@
+import io,json,os,threading,time,uuid
+from pathlib import Path
+from datetime import datetime,timezone
+import gradio as gr
+from huggingface_hub import HfApi
+ROOT=Path(__file__).parent
+QS=json.loads((ROOT/'questions.json').read_text())
+LOCK=threading.Lock()
+CRITERIA='''## Judging criteria
+**At least one option fits in every question.** Two or all three may also fit. For each image, choose **Fits** or **Does not fit**.
+
+- **Surface fit:** the object’s entire footprint must stay within the usable top surface.
+- **Container fit:** the entire rigid object must fit inside the container without protruding.
+'''
+def new_state():return {'id':str(uuid.uuid4()),'index':0,'answers':{},'start':time.time(),'submitted':False}
+def render(s):
+ q=QS[s['index']];a=s['answers'].get(q['id'],{})
+ return [s,f"### Question {s['index']+1} of 24 · {'Surface fit' if q['domain']=='area' else 'Container fit'}\n\n{q['question']}",*[str(ROOT/'images'/Path(q['images'][l]).name) for l in 'ABC'],*[gr.update(value=a.get(l),interactive=not s['submitted']) for l in 'ABC'],gr.update(interactive=s['index']>0 and not s['submitted']),gr.update(value='Submit' if s['index']==23 else 'Next',interactive=not s['submitted']), '']
+def init():return render(new_state())
+def move(s,a,b,c,direction):
+ if s is None:s=new_state()
+ if s['submitted']:raise gr.Error('Your responses have already been submitted.')
+ if direction==1 and any(v not in ['Fits','Does not fit'] for v in [a,b,c]):raise gr.Error('Please judge A, B, and C before continuing.')
+ s['answers'][QS[s['index']]['id']]=dict(zip('ABC',[a,b,c]))
+ if direction==1 and s['index']==23:
+  if len(s['answers'])!=24 or any(any(v not in ['Fits','Does not fit'] for v in r.values()) for r in s['answers'].values()):raise gr.Error('Please answer all options before submitting.')
+  token,repo=os.getenv('HF_TOKEN'),os.getenv('RESPONSES_REPO')
+  if not token or not repo:raise gr.Error('Response storage is not configured. Please contact the study owner.')
+  data={'participant_id':s['id'],'study_version':'fit-min1-24-20261009-v1','submitted_at':datetime.now(timezone.utc).isoformat(),'elapsed_seconds':int(time.time()-s['start']),'answers':{qid:{l:'Yes' if v=='Fits' else 'No' for l,v in row.items()} for qid,row in s['answers'].items()}}
+  try:
+   with LOCK:HfApi(token=token).upload_file(path_or_fileobj=io.BytesIO(json.dumps(data,indent=2).encode()),path_in_repo=f"responses/{s['id']}.json",repo_id=repo,repo_type='dataset',commit_message='Save FitTest submission')
+  except Exception:raise gr.Error('Your submission was not saved. Please press Submit again.')
+  s['submitted']=True;out=render(s);out[-1]=f"## Thank you!\nYour responses have been saved privately.\n\nReceipt: `{s['id']}`";return out
+ s['index']=max(0,min(23,s['index']+direction));return render(s)
+def previous(s,a,b,c):return move(s,a,b,c,-1)
+def next_question(s,a,b,c):return move(s,a,b,c,1)
+with gr.Blocks(title='FitTest') as demo:
+ gr.Markdown('# FitTest')
+ gr.Markdown(CRITERIA)
+ state=gr.State()
+ question=gr.Markdown()
+ imgs=[];radios=[]
+ with gr.Row():
+  for l in 'ABC':
+   with gr.Column():
+    imgs.append(gr.Image(label='Option '+l,interactive=False,show_download_button=False,show_fullscreen_button=True))
+    radios.append(gr.Radio(['Fits','Does not fit'],label=l,value=None))
+ with gr.Row():
+  prev=gr.Button('Previous');nxt=gr.Button('Next',variant='primary')
+ status=gr.Markdown()
+ outputs=[state,question,*imgs,*radios,prev,nxt,status]
+ demo.load(init,outputs=outputs,api_name=False)
+ prev.click(previous,inputs=[state,*radios],outputs=outputs,api_name=False)
+ nxt.click(next_question,inputs=[state,*radios],outputs=outputs,api_name=False)
+ gr.Markdown('Responses are visible only to the study owner. Please finish in one session; refreshing the page starts a new session.')
+if __name__=='__main__':demo.queue(default_concurrency_limit=8).launch()