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import os
import gradio as gr
from huggingface_hub import Repository
from text_generation import Client
# from dialogues import DialogueTemplate
from share_btn import (community_icon_html, loading_icon_html, share_btn_css,
share_js)
HF_TOKEN = os.environ.get("HF_TOKEN", None)
API_TOKEN = os.environ.get("API_TOKEN", None)
API_URL = os.environ.get("API_URL", None)
API_URL = "https://api-inference.huggingface.co/models/timdettmers/guanaco-33b-merged"
client = Client(
API_URL,
headers={"Authorization": f"Bearer {API_TOKEN}"},
)
repo = None
def get_total_inputs(inputs, chatbot, preprompt, user_name, assistant_name, sep):
past = []
for data in chatbot:
user_data, model_data = data
if not user_data.startswith(user_name):
user_data = user_name + user_data
if not model_data.startswith(sep + assistant_name):
model_data = sep + assistant_name + model_data
past.append(user_data + model_data.rstrip() + sep)
if not inputs.startswith(user_name):
inputs = user_name + inputs
total_inputs = preprompt + "".join(past) + inputs + sep + assistant_name.rstrip()
return total_inputs
def has_no_history(chatbot, history):
return not chatbot and not history
header = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions."
prompt_template = "### Human: {query}\n### Assistant:{response}"
def generate(
user_message,
chatbot,
history,
temperature,
top_p,
max_new_tokens,
repetition_penalty,
):
# Don't return meaningless message when the input is empty
if not user_message:
print("Empty input")
history.append(user_message)
past_messages = []
for data in chatbot:
user_data, model_data = data
past_messages.extend(
[{"role": "user", "content": user_data}, {"role": "assistant", "content": model_data.rstrip()}]
)
if len(past_messages) < 1:
prompt = header + prompt_template.format(query=user_message, response="")
else:
prompt = header
for i in range(0, len(past_messages), 2):
intermediate_prompt = prompt_template.format(query=past_messages[i]["content"], response=past_messages[i+1]["content"])
print("intermediate: ", intermediate_prompt)
prompt = prompt + '\n' + intermediate_prompt
prompt = prompt + prompt_template.format(query=user_message, response="")
generate_kwargs = {
"temperature": temperature,
"top_p": top_p,
"max_new_tokens": max_new_tokens,
}
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
truncate=999,
seed=42,
)
stream = client.generate_stream(
prompt,
**generate_kwargs,
)
output = ""
for idx, response in enumerate(stream):
if response.token.text == '':
break
if response.token.special:
continue
output += response.token.text
if idx == 0:
history.append(" " + output)
else:
history[-1] = output
chat = [(history[i].strip(), history[i + 1].strip()) for i in range(0, len(history) - 1, 2)]
yield chat, history, user_message, ""
return chat, history, user_message, ""
examples = [
"A Llama entered in my garden, what should I do?"
]
def clear_chat():
return [], []
def process_example(args):
for [x, y] in generate(args):
pass
return [x, y]
title = """<h1 align="center">Guanaco Playground πŸ’¬</h1>"""
custom_css = """
#banner-image {
display: block;
margin-left: auto;
margin-right: auto;
}
#chat-message {
font-size: 14px;
min-height: 300px;
}
"""
with gr.Blocks(analytics_enabled=False, css=custom_css) as demo:
gr.HTML(title)
with gr.Row():
with gr.Column():
gr.Markdown(
"""
πŸ’» This demo showcases the Guanaco 33B model, released together with the paper [QLoRA](https://arxiv.org/abs/2305.14314)
"""
)
with gr.Row():
with gr.Box():
output = gr.Markdown()
chatbot = gr.Chatbot(elem_id="chat-message", label="Chat")
with gr.Row():
with gr.Column(scale=3):
user_message = gr.Textbox(placeholder="Enter your message here", show_label=False, elem_id="q-input")
with gr.Row():
send_button = gr.Button("Send", elem_id="send-btn", visible=True)
clear_chat_button = gr.Button("Clear chat", elem_id="clear-btn", visible=True)
with gr.Accordion(label="Parameters", open=False, elem_id="parameters-accordion"):
temperature = gr.Slider(
label="Temperature",
value=0.7,
minimum=0.0,
maximum=1.0,
step=0.1,
interactive=True,
info="Higher values produce more diverse outputs",
)
top_p = gr.Slider(
label="Top-p (nucleus sampling)",
value=0.9,
minimum=0.0,
maximum=1,
step=0.05,
interactive=True,
info="Higher values sample more low-probability tokens",
)
max_new_tokens = gr.Slider(
label="Max new tokens",
value=1024,
minimum=0,
maximum=2048,
step=4,
interactive=True,
info="The maximum numbers of new tokens",
)
repetition_penalty = gr.Slider(
label="Repetition Penalty",
value=1.2,
minimum=0.0,
maximum=10,
step=0.1,
interactive=True,
info="The parameter for repetition penalty. 1.0 means no penalty.",
)
with gr.Row():
gr.Examples(
examples=examples,
inputs=[user_message],
cache_examples=False,
fn=process_example,
outputs=[output],
)
with gr.Row():
gr.Markdown(
"Disclaimer: The model can produce factually incorrect output, and should not be relied on to produce "
"factually accurate information. The model was trained on various public datasets; while great efforts "
"have been taken to clean the pretraining data, it is possible that this model could generate lewd, "
"biased, or otherwise offensive outputs.",
elem_classes=["disclaimer"],
)
with gr.Row():
gr.Markdown(
"[Privacy policy](https://gist.github.com/samhavens/c29c68cdcd420a9aa0202d0839876dac)",
elem_classes=["disclaimer"],
)
history = gr.State([])
last_user_message = gr.State("")
user_message.submit(
generate,
inputs=[
user_message,
chatbot,
history,
temperature,
top_p,
max_new_tokens,
repetition_penalty,
],
outputs=[chatbot, history, last_user_message, user_message],
)
send_button.click(
generate,
inputs=[
user_message,
chatbot,
history,
temperature,
top_p,
max_new_tokens,
repetition_penalty,
],
outputs=[chatbot, history, last_user_message, user_message],
)
clear_chat_button.click(clear_chat, outputs=[chatbot, history])
demo.queue(concurrency_count=16).launch(debug=True)