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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
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
model = AutoModelForCausalLM.from_pretrained("text-davinci-003")
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="text-davinci-003",
#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()
# write some HTML
html = "<div class='chatbot'>"
for m, msg in enumerate(response):
cls = "user" if m%2 == 0 else "bot"
html += "<div class='msg {}'> {}</div>".format(cls, msg)
html += "</div>"
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
css = """
.chatbox {display:flex;flex-direction:column}
.msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%}
.msg.user {background-color:cornflowerblue;color:white}
.msg.bot {background-color:lightgray;align-self:self-end}
.footer {display:none !important}
"""
gr.Interface(fn=predict,
theme="default",
inputs=[gr.inputs.Textbox(placeholder="I'm AI-DHD - ask me anything!"), "state"],
outputs=["html", "state"],
css=css).launch()
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