Update app.py

app.py

@@ -75,7 +75,7 @@ def train_stock_model(stock_symbol, start_date, end_date, feature_range=(10, 100
     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)
+    model = StockLlamaForForecasting.from_pretrained("StockLlama/StockLlama").to(device)
     config = LoraConfig(
         r=64,
         lora_alpha=32,
@@ -107,20 +107,20 @@ def train_stock_model(stock_symbol, start_date, end_date, feature_range=(10, 100
             weight_decay=0.01,
             lr_scheduler_type="linear",
             seed=3407,
-            output_dir=f"StockLlama-LoRA-{stock_symbol}-{start_date}_{end_date}",
+            output_dir=f"StockLlama/StockLlama-LoRA-{stock_symbol}-{start_date}_{end_date}",
         ),
     )
     
     trainer.train()
 
     model = model.merge_and_unload()
-    model.push_to_hub(f"Q-bert/StockLlama-tuned-{stock_symbol}-{start_date}_{end_date}")
+    model.push_to_hub(f"StockLlama/StockLlama-tuned-{stock_symbol}-{start_date}_{end_date}")
     scaler_path = "scaler.joblib"
     joblib.dump(scaler, scaler_path)
     upload_file(
         path_or_fileobj=scaler_path,
         path_in_repo=f"scalers/{scaler_path}",
-        repo_id=f"Q-bert/StockLlama-tuned-{stock_symbol}-{start_date}_{end_date}"
+        repo_id=f"StockLlama/StockLlama-tuned-{stock_symbol}-{start_date}_{end_date}"
     )
     return f"Training completed and model saved for {stock_symbol} from {start_date} to {end_date}."
 
@@ -142,7 +142,7 @@ def gradio_train_stock_model(stock_symbol, start_date, end_date, feature_range_m
 iface = gr.Interface(
     fn=gradio_train_stock_model,
     inputs=[
-        gr.Textbox(label="Stock Symbol", value="LUNC-USD"),
+        gr.Textbox(label="Stock Symbol", value="BTC-USD"),
         gr.Textbox(label="Start Date", value="2023-01-01"),
         gr.Textbox(label="End Date", value="2024-08-24"),
         gr.Slider(minimum=0, maximum=100, step=1, label="Feature Range Min", value=10),