Update app.py

app.py

@@ -952,8 +952,14 @@ if menu == "📊 So sánh model":
         df_sym = df_train[df_train["symbol"] == symbol]
 
         series = df_sym["close"].astype(float).values
-        past_tensor = torch.tensor(series, dtype=torch.bfloat16).unsqueeze(0).to(model.device)
-        freq_tensor = torch.tensor([0], dtype=torch.long).to(model.device)
+
+        past_tensor = (
+            torch.from_numpy(series)
+            .to(dtype=torch.bfloat16, device=model.device)
+            .unsqueeze(0)
+        )
+
+        freq_tensor = torch.tensor([0], dtype=torch.long, device=model.device)
 
         with torch.no_grad():
             outputs = model(
@@ -978,6 +984,7 @@ if menu == "📊 So sánh model":
             "upper_95": hi
         })
 
+
     def forecast_timegpt(symbol, horizon):
         client = load_timegpt()
         df_sym = df_train[df_train["symbol"] == symbol].copy()
@@ -1791,8 +1798,14 @@ if menu == "🧪 Demo sản phẩm":
         df_sym = df_train[df_train["symbol"] == symbol]
 
         series = df_sym["close"].astype(float).values
-        past_tensor = torch.tensor(series, dtype=torch.bfloat16).unsqueeze(0).to(model.device)
-        freq_tensor = torch.tensor([0], dtype=torch.long).to(model.device)
+
+        past_tensor = (
+            torch.from_numpy(series)
+            .to(dtype=torch.bfloat16, device=model.device)
+            .unsqueeze(0)
+        )
+
+        freq_tensor = torch.tensor([0], dtype=torch.long, device=model.device)
 
         with torch.no_grad():
             outputs = model(