Spaces:
Runtime error
Runtime error
from transformers import AutoTokenizer, AutoModelForCausalLM | |
import torch | |
import gradio as gr | |
tokenizer = AutoTokenizer.from_pretrained("natdon/DialoGPT_Michael_Scott") | |
model = AutoModelForCausalLM.from_pretrained("natdon/DialoGPT_Michael_Scott") | |
chat_history_ids = None | |
step = 0 | |
def predict(input, chat_history_ids=chat_history_ids, step=step): | |
# encode the new user input, add the eos_token and return a tensor in Pytorch | |
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( | |
[chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids | |
# generated a response while limiting the total chat history to 1000 tokens, | |
chat_history_ids = model.generate( | |
bot_input_ids, max_length=1000, | |
pad_token_id=tokenizer.eos_token_id, | |
no_repeat_ngram_size=3, | |
do_sample=True, | |
top_k=100, | |
top_p=0.7, | |
temperature=0.8 | |
) | |
step = step + 1 | |
output = tokenizer.decode( | |
chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True) | |
return output | |
demo = gr.Blocks() | |
with demo: | |
gr.Markdown( | |
""" | |
<center> | |
<img src="https://media3.giphy.com/media/l0amJzVHIAfl7jMDos/giphy.gif" alt="dialog" width="250" height="250"> | |
## Speak with Michael by typing in the input box below. | |
</center> | |
""" | |
) | |
with gr.Row(): | |
with gr.Column(): | |
inp = gr.Textbox( | |
label="Enter text to converse with Michael here:", | |
lines=1, | |
max_lines=1, | |
value="Wow this is hard", | |
placeholder="What do you think of Toby?", | |
) | |
btn = gr.Button("Submit") | |
out = gr.Textbox(lines=3) | |
# btn = gr.Button("Submit") | |
inp.submit(fn=predict, inputs=inp, outputs=out) | |
btn.click(fn=predict, inputs=inp, outputs=out) | |
demo.launch() | |