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#!/usr/bin/env python | |
# encoding: utf-8 | |
import spaces | |
import gradio as gr | |
from PIL import Image | |
import traceback | |
import re | |
import torch | |
import argparse | |
from transformers import AutoModel, AutoTokenizer | |
# README, How to run demo on different devices | |
# For Nvidia GPUs. | |
# python web_demo_2.5.py --device cuda | |
# For Mac with MPS (Apple silicon or AMD GPUs). | |
# PYTORCH_ENABLE_MPS_FALLBACK=1 python web_demo_2.5.py --device mps | |
# Argparser | |
parser = argparse.ArgumentParser(description='demo') | |
parser.add_argument('--device', type=str, default='cuda', help='cuda or mps') | |
args = parser.parse_args() | |
device = args.device | |
assert device in ['cuda', 'mps'] | |
# Load model | |
model_path = 'openbmb/MiniCPM-Llama3-V-2_5' | |
if 'int4' in model_path: | |
if device == 'mps': | |
print('Error: running int4 model with bitsandbytes on Mac is not supported right now.') | |
exit() | |
model = AutoModel.from_pretrained(model_path, trust_remote_code=True) | |
else: | |
model = AutoModel.from_pretrained(model_path, trust_remote_code=True).to(dtype=torch.float16) | |
model = model.to(device=device) | |
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) | |
model.eval() | |
ERROR_MSG = "Error, please retry" | |
model_name = 'MiniCPM-Llama3-V 2.5' | |
form_radio = { | |
'choices': ['Beam Search', 'Sampling'], | |
#'value': 'Beam Search', | |
'value': 'Sampling', | |
'interactive': True, | |
'label': 'Tipe Decode' | |
} | |
# Beam Form | |
num_beams_slider = { | |
'minimum': 0, | |
'maximum': 5, | |
'value': 3, | |
'step': 1, | |
'interactive': True, | |
'label': 'Jumlah Beams' | |
} | |
repetition_penalty_slider = { | |
'minimum': 0, | |
'maximum': 3, | |
'value': 1.2, | |
'step': 0.01, | |
'interactive': True, | |
'label': 'Repetition Penalty' | |
} | |
repetition_penalty_slider2 = { | |
'minimum': 0, | |
'maximum': 3, | |
'value': 1.05, | |
'step': 0.01, | |
'interactive': True, | |
'label': 'Repetition Penalty' | |
} | |
max_new_tokens_slider = { | |
'minimum': 1, | |
'maximum': 4096, | |
'value': 1024, | |
'step': 1, | |
'interactive': True, | |
'label': 'Maks Token Baru' | |
} | |
top_p_slider = { | |
'minimum': 0, | |
'maximum': 1, | |
'value': 0.8, | |
'step': 0.05, | |
'interactive': True, | |
'label': 'Top P' | |
} | |
top_k_slider = { | |
'minimum': 0, | |
'maximum': 200, | |
'value': 100, | |
'step': 1, | |
'interactive': True, | |
'label': 'Top K' | |
} | |
temperature_slider = { | |
'minimum': 0, | |
'maximum': 2, | |
'value': 0.7, | |
'step': 0.05, | |
'interactive': True, | |
'label': 'Temperature' | |
} | |
def create_component(params, comp='Slider'): | |
if comp == 'Slider': | |
return gr.Slider( | |
minimum=params['minimum'], | |
maximum=params['maximum'], | |
value=params['value'], | |
step=params['step'], | |
interactive=params['interactive'], | |
label=params['label'] | |
) | |
elif comp == 'Radio': | |
return gr.Radio( | |
choices=params['choices'], | |
value=params['value'], | |
interactive=params['interactive'], | |
label=params['label'] | |
) | |
elif comp == 'Button': | |
return gr.Button( | |
value=params['value'], | |
interactive=True | |
) | |
def chat(img, msgs, ctx, params=None, vision_hidden_states=None): | |
default_params = {"stream": False, "sampling": False, "num_beams":3, "repetition_penalty": 1.2, "max_new_tokens": 1024} | |
if params is None: | |
params = default_params | |
if img is None: | |
yield "Kesalaham, tidak berhasil mengunggah gambar" | |
else: | |
try: | |
image = img.convert('RGB') | |
answer = model.chat( | |
image=image, | |
msgs=msgs, | |
tokenizer=tokenizer, | |
**params | |
) | |
# if params['stream'] is False: | |
# res = re.sub(r'(<box>.*</box>)', '', answer) | |
# res = res.replace('<ref>', '') | |
# res = res.replace('</ref>', '') | |
# res = res.replace('<box>', '') | |
# answer = res.replace('</box>', '') | |
# else: | |
for char in answer: | |
yield char | |
except Exception as err: | |
print(err) | |
traceback.print_exc() | |
yield ERROR_MSG | |
def upload_img(image, _chatbot, _app_session): | |
image = Image.fromarray(image) | |
_app_session['sts']=None | |
_app_session['ctx']=[] | |
_app_session['img']=image | |
_chatbot.append(('', 'Gambar sudah berhasil diunggah, silakan tuliskan pertanyaan anda untuk memulai percakapan')) | |
return _chatbot, _app_session | |
def respond(_chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature): | |
_question = _chat_bot[-1][0] | |
print('<Question>:', _question) | |
if _app_cfg.get('ctx', None) is None: | |
_chat_bot[-1][1] = 'Silakan unggal gambar untuk memulai' | |
yield (_chat_bot, _app_cfg) | |
else: | |
_context = _app_cfg['ctx'].copy() | |
if _context: | |
_context.append({"role": "user", "content": _question}) | |
else: | |
_context = [{"role": "user", "content": _question}] | |
if params_form == 'Beam Search': | |
params = { | |
'sampling': False, | |
'stream': False, | |
'num_beams': num_beams, | |
'repetition_penalty': repetition_penalty, | |
"max_new_tokens": 896 | |
} | |
else: | |
params = { | |
'sampling': True, | |
'stream': True, | |
'top_p': top_p, | |
'top_k': top_k, | |
'temperature': temperature, | |
'repetition_penalty': repetition_penalty_2, | |
"max_new_tokens": 896 | |
} | |
gen = chat(_app_cfg['img'], _context, None, params) | |
_chat_bot[-1][1] = "" | |
for _char in gen: | |
_chat_bot[-1][1] += _char | |
_context[-1]["content"] += _char | |
yield (_chat_bot, _app_cfg) | |
def request(_question, _chat_bot, _app_cfg): | |
_chat_bot.append((_question, None)) | |
return '', _chat_bot, _app_cfg | |
def regenerate_button_clicked(_question, _chat_bot, _app_cfg): | |
if len(_chat_bot) <= 1: | |
_chat_bot.append(('Regenerate', 'No question for regeneration.')) | |
return '', _chat_bot, _app_cfg | |
elif _chat_bot[-1][0] == 'Regenerate': | |
return '', _chat_bot, _app_cfg | |
else: | |
_question = _chat_bot[-1][0] | |
_chat_bot = _chat_bot[:-1] | |
_app_cfg['ctx'] = _app_cfg['ctx'][:-2] | |
return request(_question, _chat_bot, _app_cfg) | |
# return respond(_chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature) | |
def clear_button_clicked(_question, _chat_bot, _app_cfg, _bt_pic): | |
_chat_bot.clear() | |
_app_cfg['sts'] = None | |
_app_cfg['ctx'] = None | |
_app_cfg['img'] = None | |
_bt_pic = None | |
return '', _chat_bot, _app_cfg, _bt_pic | |
with gr.Blocks() as demo: | |
with gr.Row(): | |
with gr.Column(scale=1, min_width=300): | |
params_form = create_component(form_radio, comp='Radio') | |
with gr.Accordion("Beam Search") as beams_according: | |
num_beams = create_component(num_beams_slider) | |
repetition_penalty = create_component(repetition_penalty_slider) | |
with gr.Accordion("Sampling") as sampling_according: | |
top_p = create_component(top_p_slider) | |
top_k = create_component(top_k_slider) | |
temperature = create_component(temperature_slider) | |
repetition_penalty_2 = create_component(repetition_penalty_slider2) | |
regenerate = create_component({'value': 'Regenerate'}, comp='Button') | |
clear = create_component({'value': 'Clear'}, comp='Button') | |
with gr.Column(scale=3, min_width=500): | |
app_session = gr.State({'sts':None,'ctx':None,'img':None}) | |
bt_pic = gr.Image(label="Unggah gambar untuk memulai") | |
chat_bot = gr.Chatbot(label=f"Chat with {model_name}") | |
txt_message = gr.Textbox(label="Masukkan Teks") | |
clear.click( | |
clear_button_clicked, | |
[txt_message, chat_bot, app_session, bt_pic], | |
[txt_message, chat_bot, app_session, bt_pic], | |
queue=False | |
) | |
txt_message.submit( | |
request, | |
#[txt_message, chat_bot, app_session, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature], | |
[txt_message, chat_bot, app_session], | |
[txt_message, chat_bot, app_session], | |
queue=False | |
).then( | |
respond, | |
[chat_bot, app_session, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature], | |
[chat_bot, app_session] | |
) | |
regenerate.click( | |
regenerate_button_clicked, | |
[txt_message, chat_bot, app_session], | |
[txt_message, chat_bot, app_session], | |
queue=False | |
).then( | |
respond, | |
[chat_bot, app_session, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature], | |
[chat_bot, app_session] | |
) | |
bt_pic.upload(lambda: None, None, chat_bot, queue=False).then(upload_img, inputs=[bt_pic,chat_bot,app_session], outputs=[chat_bot,app_session]) | |
# launch | |
#demo.launch(share=False, debug=True, show_api=False, server_port=8080, server_name="0.0.0.0") | |
demo.queue() | |
demo.launch() | |