Update app.py

app.py

@@ -56,11 +56,12 @@ def train_stock_model(stock_symbol, start_date, end_date, feature_range=(10, 100
     seq = [np.array(scaled_data[i:i + data_seq_length]) for i in range(len(scaled_data) - data_seq_length)]
     target = [np.array(scaled_data[i + data_seq_length:i + data_seq_length + 1]) for i in range(len(scaled_data) - data_seq_length)]
 
-    seq_tensors = [torch.tensor(s, dtype=torch.float32).unsqueeze(0) for s in seq]
+    seq_tensors = [torch.tensor(s, dtype=torch.float32) for s in seq]
     target_tensors = [t[0] for t in target]
 
     device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
     model = StockLlamaForForecasting.from_pretrained("Q-bert/StockLlama").to(device)
+    print("Model Installed.")
     config = LoraConfig(
         r=64,
         lora_alpha=32,
@@ -70,7 +71,7 @@ def train_stock_model(stock_symbol, start_date, end_date, feature_range=(10, 100
         task_type="CAUSAL_LM",
     )
     model = get_peft_model(model, config)
-
+    print("Model pefted.")
     login(token=HF_TOKEN)
     wandb.login(key=WANDB_TOKEN)
     dct = {"input_ids": seq_tensors, "label": target_tensors}