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import transformers
import gradio as gr
import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer
model_name = 'microsoft/DialoGPT-large'
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)
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
#response.remove("")
# 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>"
return response, history
#return html, 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;margin-right:10px}
.msg.bot {background-color:lightgray;align-self:self-end;margin-left:10px}
.footer {display:none !important}
"""
gr.Interface(fn=predict,
theme="grass",
title="DialoGPT-large",
inputs=[gr.inputs.Textbox(placeholder="How are you?"), "state"],
outputs=["html", "state"],
#css=css
).launch()
'''
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
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).tolist()
# convert the tokens to text, and then split the responses into lines
response = tokenizer.decode(history[0]).split("<|endoftext|>")
response.remove("")
# 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>"
return html, history
import gradio as gr
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="How are you?"), "state"],
outputs=["html", "state"],
css=css).launch()
'''
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