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import os
import time
import torch
import gradio as gr
from strings import TITLE, ABSTRACT
from gen import get_pretrained_models, get_output, setup_model_parallel
os.environ["RANK"] = "0"
os.environ["WORLD_SIZE"] = "1"
os.environ["MASTER_ADDR"] = "127.0.0.1"
os.environ["MASTER_PORT"] = "50505"
local_rank, world_size = setup_model_parallel()
generator = get_pretrained_models("7B", "tokenizer", local_rank, world_size)
history = []
simple_history = []
def chat(user_input, top_p, temperature, max_gen_len):
bot_response = get_output(
generator=generator,
prompt=user_input,
max_gen_len=max_gen_len,
temperature=temperature,
top_p=top_p)
# remove the first phrase identical to user prompt
bot_response = bot_response[0][len(user_input):]
# trip the last phrase
try:
bot_response = bot_response[:bot_response.rfind(".")]
except:
pass
history.append({
"role": "user",
"content": user_input
})
history.append({
"role": "system",
"content": bot_response
})
simple_history.append((user_input, None))
response = ""
for word in bot_response.split(" "):
time.sleep(0.1)
response += word + " "
current_pair = (user_input, response)
simple_history[-1] = current_pair
yield simple_history
def reset_textbox():
return gr.update(value='')
with gr.Blocks(css = """#col_container {width: 95%; margin-left: auto; margin-right: auto;}
#chatbot {height: 400px; overflow: auto;}""") as demo:
with gr.Column(elem_id='col_container'):
gr.Markdown(f"## {TITLE}\n\n\n\n{ABSTRACT}")
chatbot = gr.Chatbot(elem_id='chatbot')
textbox = gr.Textbox(placeholder="Enter a prompt")
with gr.Accordion("Parameters", open=False):
max_gen_len = gr.Slider(minimum=20, maximum=512, value=256, step=1, interactive=True, label="Max Genenration Length",)
top_p = gr.Slider(minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
temperature = gr.Slider(minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
textbox.submit(chat, [textbox, top_p, temperature, max_gen_len], chatbot)
textbox.submit(reset_textbox, [], [textbox])
demo.queue(api_open=False).launch() |