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web_demo.py
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web_demo.py
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#!/usr/bin/env python
import gradio as gr
from PIL import Image
import os
import json
from model import is_chinese, generate_input, chat
import torch
import argparse
from transformers import AutoTokenizer
from model import VisualGLMModel, chat
from finetune_XrayGLM import FineTuneVisualGLMModel
from sat.model import AutoModel
from sat.model.mixins import CachedAutoregressiveMixin
from sat.quantization.kernels import quantize
def generate_text_with_image(input_text, image, history=[], request_data=dict(), is_zh=True):
input_para = {
"max_length": 2048,
"min_length": 50,
"temperature": 0.8,
"top_p": 0.4,
"top_k": 100,
"repetition_penalty": 1.2
}
input_para.update(request_data)
input_data = generate_input(input_text, image, history, input_para, image_is_encoded=False)
input_image, gen_kwargs = input_data['input_image'], input_data['gen_kwargs']
with torch.no_grad():
answer, history, _ = chat(None, model, tokenizer, input_text, history=history, image=input_image, \
max_length=gen_kwargs['max_length'], top_p=gen_kwargs['top_p'], \
top_k = gen_kwargs['top_k'], temperature=gen_kwargs['temperature'], english=not is_zh)
return answer
def request_model(input_text, temperature, top_p, image_prompt, result_previous):
result_text = [(ele[0], ele[1]) for ele in result_previous]
for i in range(len(result_text)-1, -1, -1):
if result_text[i][0] == "" or result_text[i][1] == "":
del result_text[i]
print(f"history {result_text}")
is_zh = is_chinese(input_text)
if image_prompt is None:
if is_zh:
result_text.append((input_text, '图片为空！请上传图片并重试。'))
else:
result_text.append((input_text, 'Image empty! Please upload a image and retry.'))
return input_text, result_text
elif input_text == "":
result_text.append((input_text, 'Text empty! Please enter text and retry.'))
return "", result_text
request_para = {"temperature": temperature, "top_p": top_p}
image = Image.open(image_prompt)
try:
answer = generate_text_with_image(input_text, image, result_text.copy(), request_para, is_zh)
except Exception as e:
print(f"error: {e}")
if is_zh:
result_text.append((input_text, '超时！请稍等几分钟再重试。'))
else:
result_text.append((input_text, 'Timeout! Please wait a few minutes and retry.'))
return "", result_text
result_text.append((input_text, answer))
print(result_text)
return "", result_text
DESCRIPTION = '''# <a href="https://github.com/WangRongsheng/XrayGLM">XRAY-GLM</a>'''
MAINTENANCE_NOTICE1 = 'Hint 1: If the app report "Something went wrong, connection error out", please turn off your proxy and retry.\nHint 2: If you upload a large size of image like 10MB, it may take some time to upload and process. Please be patient and wait.'
MAINTENANCE_NOTICE2 = '提示1: 如果应用报了“Something went wrong, connection error out”的错误，请关闭代理并重试。\n提示2: 如果你上传了很大的图片，比如10MB大小，那将需要一些时间来上传和处理，请耐心等待。'
NOTES = 'This app is adapted from <a href="https://github.com/WangRongsheng/XrayGLM">https://github.com/WangRongsheng/XrayGLM</a>. It would be recommended to check out the repo if you want to see the detail of our model and training process.'
def clear_fn(value):
return "", [("", "Hi, What do you want to know about this image?")], None
def clear_fn2(value):
return [("", "Hi, What do you want to know about this image?")]
def main(args):
global model, tokenizer
# load model
model, model_args = AutoModel.from_pretrained(
args.from_pretrained,
args=argparse.Namespace(
fp16=True,
skip_init=True,
use_gpu_initialization=True if (torch.cuda.is_available() and args.quant is None) else False,
device='cuda' if (torch.cuda.is_available() and args.quant is None) else 'cpu',
))
model = model.eval()
if args.quant:
quantize(model.transformer, args.quant)
if torch.cuda.is_available():
model = model.cuda()
model.add_mixin('auto-regressive', CachedAutoregressiveMixin())
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
with gr.Blocks(css='style.css') as demo:
gr.Markdown(DESCRIPTION)
with gr.Row():
with gr.Column(scale=4.5):
with gr.Group():
input_text = gr.Textbox(label='Input Text', placeholder='Please enter text prompt below and press ENTER.')
with gr.Row():
run_button = gr.Button('Generate')
clear_button = gr.Button('Clear')
image_prompt = gr.Image(type="filepath", label="Image Prompt", value=None)
with gr.Row():
temperature = gr.Slider(maximum=1, value=0.8, minimum=0, label='Temperature')
top_p = gr.Slider(maximum=1, value=0.4, minimum=0, label='Top P')
with gr.Group():
with gr.Row():
maintenance_notice = gr.Markdown(MAINTENANCE_NOTICE1)
with gr.Column(scale=5.5):
result_text = gr.components.Chatbot(label='Multi-round conversation History', value=[("", "Hi, What do you want to know about this image?")]).style(height=550)
gr.Markdown(NOTES)
print(gr.__version__)
run_button.click(fn=request_model,inputs=[input_text, temperature, top_p, image_prompt, result_text],
outputs=[input_text, result_text])
input_text.submit(fn=request_model,inputs=[input_text, temperature, top_p, image_prompt, result_text],
outputs=[input_text, result_text])
clear_button.click(fn=clear_fn, inputs=clear_button, outputs=[input_text, result_text, image_prompt])
image_prompt.upload(fn=clear_fn2, inputs=clear_button, outputs=[result_text])
image_prompt.clear(fn=clear_fn2, inputs=clear_button, outputs=[result_text])
print(gr.__version__)
demo.queue(concurrency_count=10)
demo.launch(share=args.share)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--quant", choices=[8, 4], type=int, default=None)
parser.add_argument("--share", action="store_true")
parser.add_argument("--from_pretrained", type=str, default="checkpoints", help='pretrained ckpt')
args = parser.parse_args()
main(args)