Update app.py

app.py

@@ -19,8 +19,8 @@ embeddings = torch.tensor(df['embedding'].tolist(), device=device)
 model = SentenceTransformer('all-MiniLM-L6-v2', device=device)
 
 # Load the ai model for response generation
-ai_tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
-ai_model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2").to(device)
+ai_tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2-large")
+ai_model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2-large").to(device)
 
 # Define the function to find the most relevant document
 @spaces.GPU(duration=120)