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import os
from typing import Iterator
import gradio as gr
from dotenv import load_dotenv
from distutils.util import strtobool
from llama2_wrapper import LLAMA2_WRAPPER
load_dotenv()
DEFAULT_SYSTEM_PROMPT = os.getenv("DEFAULT_SYSTEM_PROMPT", "")
MAX_MAX_NEW_TOKENS = int(os.getenv("MAX_MAX_NEW_TOKENS", 2048))
DEFAULT_MAX_NEW_TOKENS = int(os.getenv("DEFAULT_MAX_NEW_TOKENS", 1024))
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", 4000))
MODEL_PATH = os.getenv("MODEL_PATH")
assert MODEL_PATH is not None, f"MODEL_PATH is required, got: {MODEL_PATH}"
BACKEND_TYPE = os.getenv("BACKEND_TYPE")
assert BACKEND_TYPE is not None, f"BACKEND_TYPE is required, got: {BACKEND_TYPE}"
LOAD_IN_8BIT = bool(strtobool(os.getenv("LOAD_IN_8BIT", "True")))
llama2_wrapper = LLAMA2_WRAPPER(
model_path=MODEL_PATH,
backend_type=BACKEND_TYPE,
max_tokens=MAX_INPUT_TOKEN_LENGTH,
load_in_8bit=LOAD_IN_8BIT,
# verbose=True,
)
DESCRIPTION = """
# llama2-webui
This is a chatbot based on Llama-2.
- Supporting models: [Llama-2-7b](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML)/[13b](https://huggingface.co/llamaste/Llama-2-13b-chat-hf)/[70b](https://huggingface.co/llamaste/Llama-2-70b-chat-hf), all [Llama-2-GPTQ](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GPTQ), all [Llama-2-GGML](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML) ...
- Supporting model backends: [tranformers](https://github.com/huggingface/transformers), [bitsandbytes(8-bit inference)](https://github.com/TimDettmers/bitsandbytes), [AutoGPTQ(4-bit inference)](https://github.com/PanQiWei/AutoGPTQ), [llama.cpp](https://github.com/ggerganov/llama.cpp)
"""
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 = llama2_wrapper.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 = llama2_wrapper.get_input_token_length(
message, chat_history, system_prompt
)
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)
with gr.Group():
chatbot = gr.Chatbot(label="Chatbot")
with gr.Row():
textbox = gr.Textbox(
container=False,
show_label=False,
placeholder="Type a message...",
scale=10,
)
submit_button = gr.Button("Submit", variant="primary", scale=1, min_width=0)
with gr.Row():
retry_button = gr.Button("🔄 Retry", variant="secondary")
undo_button = gr.Button("↩️ Undo", variant="secondary")
clear_button = gr.Button("🗑️ Clear", variant="secondary")
saved_input = gr.State()
with gr.Accordion(label="Advanced options", open=False):
system_prompt = gr.Textbox(
label="System prompt", value=DEFAULT_SYSTEM_PROMPT, lines=6
)
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=1.0,
)
top_p = gr.Slider(
label="Top-p (nucleus sampling)",
minimum=0.05,
maximum=1.0,
step=0.05,
value=0.95,
)
top_k = gr.Slider(
label="Top-k",
minimum=1,
maximum=1000,
step=1,
value=50,
)
gr.Examples(
examples=[
"Hello there! How are you doing?",
"Can you explain briefly to me what is the Python programming language?",
"Explain the plot of Cinderella in a sentence.",
"How many hours does it take a man to eat a Helicopter?",
"Write a 100-word article on 'Benefits of Open-Source in AI research'",
],
inputs=textbox,
outputs=[textbox, chatbot],
fn=process_example,
cache_examples=True,
)
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=20).launch(share=True)