test2.0

app.py

@@ -1,10 +1,6 @@
 import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-# Load the models and tokenizers
-gpt35_model = AutoModelForCausalLM.from_pretrained("gpt-3.5-turbo-0613")
-gpt35_tokenizer = AutoTokenizer.from_pretrained("gpt-3.5-turbo-0613")
-
 vicuna_model = AutoModelForCausalLM.from_pretrained("lmsys/vicuna-7b-v1.3")
 vicuna_tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-7b-v1.3")
 
@@ -20,11 +16,10 @@ def generate_response(model, tokenizer, prompt):
 
 # Define the Gradio interface
 def chatbot_interface(prompt):
-    gpt35_response = generate_response(gpt35_model, gpt35_tokenizer, prompt)
     vicuna_response = generate_response(vicuna_model, vicuna_tokenizer, prompt)
     llama_response = generate_response(llama_model, llama_tokenizer, prompt)
 
-    return {"GPT-3.5": gpt35_response, "Vicuna-7B": vicuna_response, "Llama-7B": llama_response}
+    return {"Vicuna-7B": vicuna_response, "Llama-7B": llama_response}
 
 iface = gr.Interface(fn=chatbot_interface, 
                      inputs="text",