Update app.py

app.py

@@ -51,7 +51,7 @@ def check_existing_model(stock_symbol, start_date, end_date):
     
 @spaces.GPU(duration=300)
 def train_stock_model(stock_symbol, start_date, end_date, feature_range=(10, 100), data_seq_length=256, epochs=10, batch_size=16, learning_rate=2e-4):
-    repo_id = f"Q-bert/StockLlama-tuned-{stock_symbol}-{stock_symbol}-{start_date}_{end_date}"
+    repo_id = f"Q-bert/StockLlama-tuned{stock_symbol}-{start_date}_{end_date}"
 
     if check_existing_model(stock_symbol, start_date, end_date):
         return f"Model for {stock_symbol} from {start_date} to {end_date} already exists."
@@ -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}-{stock_symbol}-{start_date}_{end_date}",
+            output_dir=f"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}-{stock_symbol}-{start_date}_{end_date}")
+    model.push_to_hub(f"Q-bert/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}-{stock_symbol}-{start_date}_{end_date}"
+        repo_id=f"Q-bert/StockLlama-tuned-{stock_symbol}-{start_date}_{end_date}"
     )
     return f"Training completed and model saved for {stock_symbol} from {start_date} to {end_date}."