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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from peft import PeftModel
model = AutoModelForCausalLM.from_pretrained("DAMO-NLP-MT/polylm-1.7b")
model = PeftModel.from_pretrained(model, "fadliaulawi/polylm-1.7b-finetuned")
tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-MT/polylm-1.7b")
def user(message, history):
return "", history + [[message, None]]
def bot(history,temperature, max_length, top_p,top_k):
user_message = history[-1][0]
new_user_input_ids = tokenizer.encode(
user_message + tokenizer.eos_token, return_tensors="pt"
)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([torch.LongTensor([]), new_user_input_ids], dim=-1)
# generate a response
response = model.generate(
bot_input_ids,
pad_token_id=tokenizer.eos_token_id,
temperature = float(temperature),
max_length=max_length,
top_p=float(top_p),
top_k=top_k,
do_sample=True
).tolist()
# convert the tokens to text, and then split the responses into lines
response = tokenizer.decode(response[0]).split("<|endoftext|>")
response = [
(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
] # convert to tuples of list
history[-1] = response[0]
return history
with gr.Blocks() as demo:
temperature = gr.Slider(0, 5, value=0.8, step=0.1, label='Temperature')
max_length = gr.Slider(0, 8192, value=256, step=1, label='Max Length')
top_p = gr.Slider(0, 1, value=0.8, step=0.1, label='Top P')
top_k = gr.Slider(0, 50, value=50, step=1, label='Top K')
chatbot = gr.Chatbot()
msg = gr.Textbox()
submit = gr.Button("Submit")
clear = gr.Button("Clear")
examples = gr.Examples(examples=["Hi Doctor"],inputs=[msg])
#submit.click(bot,[msg,chatbot,temperature, max_length, top_p,top_k],chatbot)
submit.click(user, [msg, chatbot], [msg, chatbot], queue=False).then(
bot, [chatbot,temperature,max_length,top_p,top_k], chatbot
)
clear.click(lambda: None, None, chatbot, queue=False)
demo.launch() |