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import os
from typing import Iterator
import gradio as gr
from model import run
HF_PUBLIC = os.environ.get("HF_PUBLIC", False)
DEFAULT_SYSTEM_PROMPT = "You are Mistral. You are AI-assistant, you are polite, give only truthful information and are based on the Mistral-7B model from Mistral AI. You can communicate in different languages equally well."
MAX_MAX_NEW_TOKENS = 4096
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = 4000
DESCRIPTION = """
# Mistral-7B Chat
๐ป This Space demonstrates model [Mistral-7b-Instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) by Mistral AI, a Mistral-chat model with 7B parameters fine-tuned for chat instructions and specialized on many tasks. Feel free to play with it, or duplicate to run generations without a queue! If you want to run your own service, you can also [deploy the model on Inference Endpoints](https://huggingface.co/inference-endpoints).
๐ For more details about the Mistral family of models and how to use them with `transformers`, take a look [at our blog post](https://huggingface.co/blog/mistral).
๐๐ป Check out our [Playground](https://huggingface.co/spaces/osanseviero/mistral-super-fast) for a super-fast code completion demo that leverages a streaming [inference endpoint](https://huggingface.co/inference-endpoints).
"""
def clear_and_save_textbox(message: str) -> tuple[str, str]:
return '', message
def display_input(message: str,
history: list[tuple[str, str]]) -> list[tuple[str, str]]:
history.append((message, ''))
return history
def delete_prev_fn(
history: list[tuple[str, str]]) -> tuple[list[tuple[str, str]], str]:
try:
message, _ = history.pop()
except IndexError:
message = ''
return history, message or ''
def generate(
message: str,
history_with_input: list[tuple[str, str]],
system_prompt: str,
max_new_tokens: int,
temperature: float,
top_p: float,
top_k: int,
) -> Iterator[list[tuple[str, str]]]:
if max_new_tokens > MAX_MAX_NEW_TOKENS:
raise ValueError
history = history_with_input[:-1]
generator = run(message, history, system_prompt, max_new_tokens, temperature, top_p, top_k)
try:
first_response = next(generator)
yield history + [(message, first_response)]
except StopIteration:
yield history + [(message, '')]
for response in generator:
yield history + [(message, response)]
def process_example(message: str) -> tuple[str, list[tuple[str, str]]]:
generator = generate(message, [], DEFAULT_SYSTEM_PROMPT, 1024, 1, 0.95, 50)
for x in generator:
pass
return '', x
def check_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> None:
input_token_length = len(message) + len(chat_history)
if input_token_length > MAX_INPUT_TOKEN_LENGTH:
raise gr.Error(f'The accumulated input is too long ({input_token_length} > {MAX_INPUT_TOKEN_LENGTH}). Clear your chat history and try again.')
with gr.Blocks(css='style.css') as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(value='Duplicate Space for private use',
elem_id='duplicate-button')
with gr.Group():
chatbot = gr.Chatbot(label='Playground')
with gr.Row():
textbox = gr.Textbox(
container=False,
show_label=False,
placeholder='Hi, CodeLlama!',
scale=10,
)
submit_button = gr.Button('Submit',
variant='primary',
scale=1,
min_width=0)
with gr.Row():
retry_button = gr.Button('๐ Retry', variant='secondary')
undo_button = gr.Button('โฉ๏ธ Undo', variant='secondary')
clear_button = gr.Button('๐๏ธ Clear', variant='secondary')
saved_input = gr.State()
with gr.Accordion(label='โ๏ธ Advanced options', open=False):
system_prompt = gr.Textbox(label='System prompt',
value=DEFAULT_SYSTEM_PROMPT,
lines=5,
interactive=False)
max_new_tokens = gr.Slider(
label='Max new tokens',
minimum=1,
maximum=MAX_MAX_NEW_TOKENS,
step=1,
value=DEFAULT_MAX_NEW_TOKENS,
)
temperature = gr.Slider(
label='Temperature',
minimum=0.1,
maximum=4.0,
step=0.1,
value=0.1,
)
top_p = gr.Slider(
label='Top-p (nucleus sampling)',
minimum=0.05,
maximum=1.0,
step=0.05,
value=0.9,
)
top_k = gr.Slider(
label='Top-k',
minimum=1,
maximum=1000,
step=1,
value=10,
)
textbox.submit(
fn=clear_and_save_textbox,
inputs=textbox,
outputs=[textbox, saved_input],
api_name=False,
queue=False,
).then(
fn=display_input,
inputs=[saved_input, chatbot],
outputs=chatbot,
api_name=False,
queue=False,
).then(
fn=check_input_token_length,
inputs=[saved_input, chatbot, system_prompt],
api_name=False,
queue=False,
).success(
fn=generate,
inputs=[
saved_input,
chatbot,
system_prompt,
max_new_tokens,
temperature,
top_p,
top_k,
],
outputs=chatbot,
api_name=False,
)
button_event_preprocess = submit_button.click(
fn=clear_and_save_textbox,
inputs=textbox,
outputs=[textbox, saved_input],
api_name=False,
queue=False,
).then(
fn=display_input,
inputs=[saved_input, chatbot],
outputs=chatbot,
api_name=False,
queue=False,
).then(
fn=check_input_token_length,
inputs=[saved_input, chatbot, system_prompt],
api_name=False,
queue=False,
).success(
fn=generate,
inputs=[
saved_input,
chatbot,
system_prompt,
max_new_tokens,
temperature,
top_p,
top_k,
],
outputs=chatbot,
api_name=False,
)
retry_button.click(
fn=delete_prev_fn,
inputs=chatbot,
outputs=[chatbot, saved_input],
api_name=False,
queue=False,
).then(
fn=display_input,
inputs=[saved_input, chatbot],
outputs=chatbot,
api_name=False,
queue=False,
).then(
fn=generate,
inputs=[
saved_input,
chatbot,
system_prompt,
max_new_tokens,
temperature,
top_p,
top_k,
],
outputs=chatbot,
api_name=False,
)
undo_button.click(
fn=delete_prev_fn,
inputs=chatbot,
outputs=[chatbot, saved_input],
api_name=False,
queue=False,
).then(
fn=lambda x: x,
inputs=[saved_input],
outputs=textbox,
api_name=False,
queue=False,
)
clear_button.click(
fn=lambda: ([], ''),
outputs=[chatbot, saved_input],
queue=False,
api_name=False,
)
demo.queue(max_size=32).launch(share=HF_PUBLIC, show_api=False)
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