app.py

#!/usr/bin/env python
# coding: utf-8

# In[ ]:


import os
import openai
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

openai.organization = "org-orRhfBkKOfOuNACbjPyWKbUt"
openai.api_key = "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"

def predict(input, history=[]):

    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
    
    # tokenize the new input sentence
    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')

    # append the new user input tokens to the chat history
    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)

    # generate a response 
    response = openai.Completion.create(
    model="davinci:ft-placeholder:ai-dhd-2022-12-07-10-09-37",
    prompt= input,
    temperature=0.09,
    max_tokens=608,
    top_p=1,
    frequency_penalty=0,
    presence_penalty=0).tolist()

    history = response[Completion]
    
    # 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()