Fix image analysis error: scalar conversion and return value mismatch

- Use .item() instead of float(.numpy()) for pred_score extraction to
handle non-0-dimensional tensors correctly
- Fix predict() returning 3 values when image is None (Gradio expects 2)

https://claude.ai/code/session_01WE2rQavbE8N5j1XBM874K5

gradio_ui/handlers.py

@@ -60,10 +60,10 @@ def predict(image, model_name: str, category: str):
         category: Selected category
     
     Returns:
-        Tuple of (visualization_image, anomaly_score_text, status_text)
+        Tuple of (visualization_image, result_text)
     """
     if image is None:
-        return None, "⚠️ Please upload an image", ""
+        return None, "⚠️ Please upload an image"
     
     try:
         # Save temp image if needed

inference.py

@@ -62,7 +62,7 @@ def run_inference(image_path: str, category: str = "bottle", model_name: str = "
     results = {"image_path": image_path, "category": category, "model": model_name}
     
     for batch in predictions:
-        results["anomaly_score"] = float(batch.pred_score[0].cpu().numpy()) if batch.pred_score is not None else None
+        results["anomaly_score"] = batch.pred_score[0].cpu().item() if batch.pred_score is not None else None
         results["anomaly_map"] = batch.anomaly_map[0].cpu().numpy() if batch.anomaly_map is not None else None
         results["pred_mask"] = batch.pred_mask[0].cpu().numpy() if batch.pred_mask is not None else None
         break