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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
MAX_HISTORY = 7
MODEL_PATH = 'llongpre/DialoGPT-small-miles'
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForCausalLM.from_pretrained(MODEL_PATH)
# def predict(input, history=[]):
# # 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
# history = model.generate(
# bot_input_ids,
# max_length=1000,
# pad_token_id=tokenizer.eos_token_id,
# no_repeat_ngram_size=3,
# top_p = 0.92,
# top_k = 50
# ).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
#
# from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger("transformers")
logger.info("INFO")
def generate_answer(input, history=[]):
new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
history = history.append(input)
logger.info(history)
if len(history) > MAX_HISTORY:
history = history[-MAX_HISTORY:]
bot_input_ids = torch.cat(history, dim=-1)
chat_history_ids = model.generate(
bot_input_ids,
pad_token_id=tokenizer.pad_token_id,
max_length=1000,
do_sample=True,
# top_k=150, # sample from the top k words sorted descending by probability
top_p=0.7, # choose smallest possible words whose cumulative probability exceeds p
temperature = 0.95, # 0 greedy, inf is random
no_repeat_ngram_size=3,
)
response = chat_history_ids[:, bot_input_ids.shape[-1]:]
output = tokenizer.decode(response[0], skip_special_tokens=True)
history.append(output)
return output, history
gr.Interface(
fn=generate_answer,
title="DialoGPT-large",
inputs=["text", "state"],
outputs=["chatbot", "state"],
).launch()
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