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#!/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() | |