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#!/usr/bin/env python
# encoding: utf-8
import timm
import spaces
import gradio as gr
from PIL import Image
import traceback
import re
import torch
import argparse
from transformers import AutoModel, AutoTokenizer
from chat import OmniLMM12B
# Load model
model_path = 'openbmb/RLAIF-V-12B'
model = OmniLMM12B(model_path)
ERROR_MSG = "Error, please retry"
model_name = 'RLAIF-V-12B'
form_radio = {
'choices': ['Beam Search', 'Sampling'],
#'value': 'Beam Search',
'value': 'Sampling',
'interactive': True,
'label': 'Decode Type'
}
# Beam Form
num_beams_slider = {
'minimum': 0,
'maximum': 5,
'value': 3,
'step': 1,
'interactive': True,
'label': 'Num 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': 'Max New Tokens'
}
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', visible=False):
if comp == 'Slider':
return gr.Slider(
visible=visible,
minimum=params['minimum'],
maximum=params['maximum'],
value=params['value'],
step=params['step'],
interactive=params['interactive'],
label=params['label']
)
elif comp == 'Radio':
return gr.Radio(
visible=visible,
choices=params['choices'],
value=params['value'],
interactive=params['interactive'],
label=params['label']
)
elif comp == 'Button':
return gr.Button(
visible=visible,
value=params['value'],
interactive=True
)
@spaces.GPU(duration=120)
def chat(img, msgs, ctx, params=None, vision_hidden_states=None):
if img is None:
return -1, "Error, invalid image, please upload a new image", None, None
try:
image = img.convert('RGB')
answer = model.chat(
image=image,
msgs=msgs,
)
return 0, answer, None, None
except Exception as err:
print(err)
traceback.print_exc()
return -1, ERROR_MSG, None, None
def upload_img(image, _chatbot, _app_session):
image = Image.fromarray(image)
_app_session['sts']=None
_app_session['ctx']=[]
_app_session['img']=image
_chatbot.append(('', 'Image uploaded successfully, you can talk to me now'))
return _chatbot, _app_session
def respond(_question, _chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature):
if _app_cfg.get('ctx', None) is None:
_chat_bot.append((_question, 'Please upload an image to start'))
return '', _chat_bot, _app_cfg
_context = _app_cfg['ctx'].copy()
if _context:
_context.append({"role": "user", "content": _question})
else:
_context = [{"role": "user", "content": _question}]
print('<User>:', _question)
if params_form == 'Beam Search':
params = {
'sampling': False,
'num_beams': num_beams,
'repetition_penalty': repetition_penalty,
"max_new_tokens": 896
}
else:
params = {
'sampling': True,
'top_p': top_p,
'top_k': top_k,
'temperature': temperature,
'repetition_penalty': repetition_penalty_2,
"max_new_tokens": 896
}
print('msgs:', _context)
code, _answer, _, sts = chat(_app_cfg['img'], _context, None, params)
print('<Assistant>:', _answer)
_context.append({"role": "assistant", "content": _answer})
_chat_bot.append((_question, _answer))
if code == 0:
_app_cfg['ctx']=_context
_app_cfg['sts']=sts
return '', _chat_bot, _app_cfg
def regenerate_button_clicked(_question, _chat_bot, _app_cfg, params_form, num_beams, repetition_penalty, repetition_penalty_2, top_p, top_k, temperature):
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 respond(_question, _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'] = []
_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", visible=False) as beams_according:
num_beams = create_component(num_beams_slider)
repetition_penalty = create_component(repetition_penalty_slider)
with gr.Accordion("Sampling", visible=False) 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)
with gr.Column(scale=3, min_width=500):
app_session = gr.State({'sts':None,'ctx':None,'img':None})
bt_pic = gr.Image(label="Upload an image to start")
chat_bot = gr.Chatbot(label=f"Chat with {model_name}")
txt_message = gr.Textbox(label="Input text")
with gr.Row():
regenerate = create_component({'value': 'Regenerate'}, comp='Button', visible=True)
clear = create_component({'value': 'Clear'}, comp='Button', visible=True)
clear.click(
clear_button_clicked,
[txt_message, chat_bot, app_session, bt_pic],
[txt_message, chat_bot, app_session, bt_pic],
queue=False
)
regenerate.click(
regenerate_button_clicked,
[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.submit(
respond,
[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]
)
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.launch()
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