UX: add real-time status updates and queue for predict action

app.py

@@ -120,29 +120,37 @@ def _ensure_loaded():
 
 def predict(img: Image.Image):
     if img is None:
-        return "error: no image"
+        yield "error: no image", "status: please upload image first"
+        return
+    yield "", "status: starting"
     try:
+        if not _MODEL_READY:
+            yield "", "status: loading model (first run takes longer)"
         _ensure_loaded()
     except Exception:
-        return f"error: model load failed: {_MODEL_ERR}"
+        yield f"error: model load failed: {_MODEL_ERR}", "status: failed"
+        return
+    yield "", "status: model ready, running inference"
     if img.mode != "RGB":
         img = img.convert("RGB")
     proc = processor(images=img, return_tensors="pt")
-    with torch.inference_mode():
-        pred_01 = model(proc["pixel_values"]).item()
-    pred_01 = max(0.0, min(1.0, float(pred_01)))
-    pred_score = pred_01 * (score_max - score_min) + score_min
-    score_int = int(round(pred_score))
-    score_int = max(int(score_min), min(int(score_max), score_int))
-    return f"score_{score_int} (raw={pred_score:.4f})"
+    with torch.inference_mode():
+        pred_01 = model(proc["pixel_values"]).item()
+    pred_01 = max(0.0, min(1.0, float(pred_01)))
+    pred_score = pred_01 * (score_max - score_min) + score_min
+    score_int = int(round(pred_score))
+    score_int = max(int(score_min), min(int(score_max), score_int))
+    yield f"score_{score_int} (raw={pred_score:.4f})", "status: done"
 
 
 with gr.Blocks() as demo:
     gr.Markdown("# SigLIP2 Aesthetic Scorer Demo")
     inp = gr.Image(type="pil", label="Image")
     out = gr.Textbox(label="Result")
+    status = gr.Textbox(label="Status", value="status: idle")
     btn = gr.Button("Predict")
-    btn.click(fn=predict, inputs=[inp], outputs=[out])
+    btn.click(fn=predict, inputs=[inp], outputs=[out, status])
 
 
+demo.queue(default_concurrency_limit=1)
 demo.launch(server_name="0.0.0.0", server_port=7860)