from transformers import AutoModel, AutoTokenizer
import gradio as gr
import mdtex2html
import torch
"""Override Chatbot.postprocess"""
def postprocess(self, y):
if y is None:
return []
for i, (message, response) in enumerate(y):
y[i] = (
None if message is None else mdtex2html.convert((message)),
None if response is None else mdtex2html.convert(response),
)
return y
gr.Chatbot.postprocess = postprocess
def parse_text(text):
"""copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
lines = text.split("\n")
lines = [line for line in lines if line != ""]
count = 0
for i, line in enumerate(lines):
if "```" in line:
count += 1
items = line.split('`')
if count % 2 == 1:
lines[i] = f'<pre><code class="language-{items[-1]}">'
else:
lines[i] = f'<br></code></pre>'
else:
if i > 0:
if count % 2 == 1:
line = line.replace("`", "\`")
line = line.replace("<", "<")
line = line.replace(">", ">")
line = line.replace(" ", " ")
line = line.replace("*", "*")
line = line.replace("_", "_")
line = line.replace("-", "-")
line = line.replace(".", ".")
line = line.replace("!", "!")
line = line.replace("(", "(")
line = line.replace(")", ")")
line = line.replace("$", "$")
lines[i] = "<br>"+line
text = "".join(lines)
return text
def predict(input, image_path, chatbot, max_length, top_p, temperature, history):
if image_path is None:
return [(input, "图片不能为空。请重新上传图片并重试。")], []
chatbot.append((parse_text(input), ""))
with torch.no_grad():
for response, history in model.stream_chat(tokenizer, image_path, input, history, max_length=max_length, top_p=top_p,
temperature=temperature):
chatbot[-1] = (parse_text(input), parse_text(response))
yield chatbot, history
def predict_new_image(image_path, chatbot, max_length, top_p, temperature):
input, history = "描述这张图片。", []
chatbot.append((parse_text(input), ""))
with torch.no_grad():
for response, history in model.stream_chat(tokenizer, image_path, input, history, max_length=max_length,
top_p=top_p,
temperature=temperature):
chatbot[-1] = (parse_text(input), parse_text(response))
yield chatbot, history
def reset_user_input():
return gr.update(value='')
def reset_state():
return None, [], []
DESCRIPTION = '''<h1 align="center"><a href="https://github.com/THUDM/VisualGLM-6B">VisualGLM</a></h1>'''
MAINTENANCE_NOTICE = '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.'
NOTES = 'This app is adapted from <a href="https://github.com/THUDM/VisualGLM-6B">https://github.com/THUDM/VisualGLM-6B</a>. It would be recommended to check out the repo if you want to see the detail of our model and training process.'
def main(args):
global model, tokenizer
tokenizer = AutoTokenizer.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True)
if args.quant in [4, 8]:
model = AutoModel.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True).quantize(args.quant).half().cuda()
else:
model = AutoModel.from_pretrained("THUDM/visualglm-6b", trust_remote_code=True).half().cuda()
model = model.eval()
with gr.Blocks(css='style.css') as demo:
gr.HTML(DESCRIPTION)
with gr.Row():
with gr.Column(scale=2):
image_path = gr.Image(type="filepath", label="Image Prompt", value=None).style(height=504)
with gr.Column(scale=4):
chatbot = gr.Chatbot().style(height=480)
with gr.Row():
with gr.Column(scale=2, min_width=100):
max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
top_p = gr.Slider(0, 1, value=0.4, step=0.01, label="Top P", interactive=True)
temperature = gr.Slider(0, 1, value=0.8, step=0.01, label="Temperature", interactive=True)
with gr.Column(scale=4):
with gr.Box():
with gr.Row():
with gr.Column(scale=2):
user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=4).style(
container=False)
with gr.Column(scale=1, min_width=64):
submitBtn = gr.Button("Submit", variant="primary")
emptyBtn = gr.Button("Clear History")
gr.Markdown(MAINTENANCE_NOTICE + '\n' + NOTES)
history = gr.State([])
submitBtn.click(predict, [user_input, image_path, chatbot, max_length, top_p, temperature, history], [chatbot, history],
show_progress=True)
image_path.upload(predict_new_image, [image_path, chatbot, max_length, top_p, temperature], [chatbot, history],
show_progress=True)
image_path.clear(reset_state, outputs=[image_path, chatbot, history], show_progress=True)
submitBtn.click(reset_user_input, [], [user_input])
emptyBtn.click(reset_state, outputs=[image_path, chatbot, history], show_progress=True)
print(gr.__version__)
demo.queue().launch(share=args.share, inbrowser=True, server_name='0.0.0.0', server_port=8080)
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")
args = parser.parse_args()
main(args)