fixed paths

app.py

@@ -19,22 +19,22 @@ wandb.login(key=wandb_api_key)
 
 # Define function to load model and pipeline dynamically
 def load_pipeline(model_name, fine_tuned=False):
-    # Set model paths for pre-trained and fine-tuned versions
+    # Use the Hugging Face repo paths for each model
     paths = {
-        "gpt2": ("gpt2-medium", "path/to/finetuned_gpt2"),
-        "gpt_neo": ("EleutherAI/gpt-neo-1.3B", "path/to/finetuned_gpt_neo"),
-        "gpt_j": ("EleutherAI/gpt-j-6B", "path/to/finetuned_gpt_j")
+        "gpt2": ("gpt2-medium", "sbicy/finetuned-gpt2"),
+        "gpt_neo": ("EleutherAI/gpt-neo-1.3B", "sbicy/finetuned-gpt-neo"),
+        "gpt_j": ("EleutherAI/gpt-j-6B", "sbicy/finetuned-gpt-j")
     }
 
-    pretrained_model_name, finetuned_model_path = paths[model_name]
-    model_path = finetuned_model_path if fine_tuned else pretrained_model_name
-
-    # Load model and tokenizer
+    pretrained_model_name, finetuned_model_repo = paths[model_name]
+    model_path = finetuned_model_repo if fine_tuned else pretrained_model_name
+    
+    # Load model and tokenizer from Hugging Face Hub
     model = AutoModelForCausalLM.from_pretrained(model_path)
     tokenizer = AutoTokenizer.from_pretrained(model_path)
     tokenizer.pad_token = tokenizer.eos_token
-
-    # Set up pipeline with GPU
+    
+    # Set up pipeline with the specified device
     return pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
 
 # Add the GPU decorator to the generate function