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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()
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