Update app.py

app.py

@@ -26,11 +26,12 @@ def run_videomae(video):
         pred_id = outputs.logits.argmax(-1).item()
         return {
             "model": "VideoMAE",
+            "status": "ok",
             "class": vm_model.config.id2label[pred_id],
             "confidence": float(torch.softmax(outputs.logits, -1)[0, pred_id].item()),
         }
     except Exception as e:
-        return {"model": "VideoMAE", "error": str(e)}
+        return {"model": "VideoMAE", "status": "failed", "error": str(e)}
 
 # ======================
 # 2. LLaVA-Video-Llama-3.1-8B
@@ -38,7 +39,9 @@ def run_videomae(video):
 try:
     llava_model_id = "weizhiwang/LLaVA-Video-Llama-3.1-8B"
     llava_tokenizer = AutoTokenizer.from_pretrained(llava_model_id, trust_remote_code=True)
-    llava_model = AutoModelForCausalLM.from_pretrained(llava_model_id, trust_remote_code=True).half().cuda().eval()
+    llava_model = AutoModelForCausalLM.from_pretrained(
+        llava_model_id, trust_remote_code=True
+    ).half().cuda().eval()
 
     def run_llava(video, prompt):
         try:
@@ -46,16 +49,21 @@ try:
             output = llava_model.generate(**inputs, max_new_tokens=256)
             return {
                 "model": "LLaVA-Video-Llama-3.1-8B",
+                "status": "ok",
                 "output": llava_tokenizer.decode(output[0], skip_special_tokens=True),
             }
         except Exception as e:
-            return {"model": "LLaVA-Video-Llama-3.1-8B", "error": str(e)}
+            return {"model": "LLaVA-Video-Llama-3.1-8B", "status": "failed", "error": str(e)}
 
 except Exception as outer_error:
     llava_load_error = str(outer_error)
 
     def run_llava(video, prompt):
-        return {"model": "LLaVA-Video-Llama-3.1-8B", "error": f"LLaVA not available: {llava_load_error}"}
+        return {
+            "model": "LLaVA-Video-Llama-3.1-8B",
+            "status": "failed",
+            "error": f"LLaVA not available: {llava_load_error}",
+        }
 
 # ======================
 # Unified App