Update app.py

app.py

@@ -1,15 +1,10 @@
 import os
 import openai
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
 
 openai.api_key = os.getenv("OPENAI_API_KEY")
 
-def predict(input, history=[]):
-    
-    new_user_input_ids = input
-    response = openai.Completion.create(
+response = openai.Completion.create(
     model="davinci:ft-placeholder-2022-12-10-04-13-26",
     prompt=input
     temperature=0.13,
@@ -17,16 +12,10 @@ def predict(input, history=[]):
     top_p=1,
     frequency_penalty=0.36,
     presence_penalty=1.25
-    )
+)
 
-# generate a response 
-    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
 
-# convert the tokens to text, and then split the responses into lines
-    response = tokenizer.decode(history[0]).split("<|endoftext|>")
-    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
-    return response, history
 
 gr.Interface(fn=predict,
              inputs=["text", "state"],
-             outputs=["chatbot", "state"]).launch()
+             outputs=["response", "state"]).launch()