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from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = 'armandnlp/gpt2-TOD_finetuned_SGD'
tokenizer_TOD = AutoTokenizer.from_pretrained(model_name)
model_TOD = AutoModelForCausalLM.from_pretrained(model_name)
def generate_response(prompt):
input_ids = tokenizer_TOD(prompt, return_tensors="pt").input_ids
outputs = model_TOD.generate(input_ids,
do_sample=False,
max_length=1024,
eos_token_id=50262)
return tokenizer_TOD.batch_decode(outputs)[0]
def chat(message, history):
history = history or []
output = generate_response(message)
context, response = output.split('<|endofcontext|>')
history.append((context+'<|endofcontext|>', response))
return history
import gradio as gr
chatbot = gr.Chatbot(color_map=("gray", "blue"))
iface = gr.Interface(chat,
["text", "state"],
[chatbot, "state"],
allow_screenshot=False,
allow_flagging="never",
)
"""
iface = gr.Interface(fn=generate_response,
inputs="text",
outputs="text",
title="gpt2-TOD",
examples=[["<|context|> <|user|> I'm super hungry ! I want to go to the restaurant.<|endofcontext|>"]],
description="Passing in a task-oriented dialogue context generates a belief state, actions to take and a response based on those actions",
)
"""
iface.launch()
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