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app.py

@@ -3,13 +3,8 @@ from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 import torch
 import gradio as gr
 import spaces
-from dotenv import load_dotenv
 
-load_dotenv()
 
-os.environ["HF_TOKEN"] = os.getenv("HF_TOKEN")
-
-torch.cuda.empty_cache()
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 model_name = "syubraj/MedicalChat-Phi-3.5-mini-instruct"
@@ -31,6 +26,8 @@ def generate_response(user_query: str, system_message: str = None, max_length: i
     :param max_length: Max tokens to generate.
     :return: Generated assistant response.
     """
+    torch.cuda.empty_cache()
+
     if not user_query.strip():
         return "Error: User query cannot be empty."