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import spaces
import os
import re
import time
import gradio as gr
import torch
from transformers import AutoModelForCausalLM
from transformers import TextIteratorStreamer
from threading import Thread
model_name = 'AIDC-AI/Ovis1.6-Gemma2-9B'
# load model
model = AutoModelForCausalLM.from_pretrained(model_name,
torch_dtype=torch.bfloat16,
multimodal_max_length=8192,
trust_remote_code=True).to(device='cpu')
text_tokenizer = model.get_text_tokenizer()
visual_tokenizer = model.get_visual_tokenizer()
streamer = TextIteratorStreamer(text_tokenizer, skip_prompt=True, skip_special_tokens=True)
image_placeholder = '<image>'
cur_dir = os.path.dirname(os.path.abspath(__file__))
def submit_chat(chatbot, text_input):
response = ''
chatbot.append((text_input, response))
return chatbot ,''
# @spaces.GPU <-- Remove this line
def ovis_chat(chatbot, image_input):
# preprocess inputs
conversations = []
response = ""
text_input = chatbot[-1][0]
for query, response in chatbot[:-1]:
conversations.append({
"from": "human",
"value": query
})
conversations.append({
"from": "gpt",
"value": response
})
text_input = text_input.replace(image_placeholder, '')
conversations.append({
"from": "human",
"value": text_input
})
if image_input is not None:
conversations[0]["value"] = image_placeholder + '\n' + conversations[0]["value"]
prompt, input_ids, pixel_values = model.preprocess_inputs(conversations, [image_input])
attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id)
input_ids = input_ids.unsqueeze(0).to(device='cpu')
attention_mask = attention_mask.unsqueeze(0).to(device='cpu')
if image_input is None:
pixel_values = [None]
else:
pixel_values = [pixel_values.to(dtype=visual_tokenizer.dtype, device='cpu')]
with torch.inference_mode():
gen_kwargs = dict(
max_new_tokens=512,
do_sample=False,
top_p=None,
top_k=None,
temperature=None,
repetition_penalty=None,
eos_token_id=model.generation_config.eos_token_id,
pad_token_id=text_tokenizer.pad_token_id,
use_cache=True
)
response = ""
thread = Thread(target=model.generate,
kwargs={"inputs": input_ids,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"streamer": streamer,
**gen_kwargs})
thread.start()
for new_text in streamer:
response += new_text
chatbot[-1][1] = response
yield chatbot
thread.join()
# debug
print('*'*60)
print('*'*60)
print('OVIS_CONV_START')
for i, (request, answer) in enumerate(chatbot[:-1], 1):
print(f'Q{i}:\n {request}')
print(f'A{i}:\n {answer}')
print('New_Q:\n', text_input)
print('New_A:\n', response)
print('OVIS_CONV_END')
def clear_chat():
return [], None, ""
with open(f"{cur_dir}/resource/logo.svg", "r", encoding="utf-8") as svg_file:
svg_content = svg_file.read()
font_size = "2.5em"
svg_content = re.sub(r'(<svg[^>]*)(>)', rf'\1 height="{font_size}" style="vertical-align: middle; display: inline-block;"\2', svg_content)
html = f"""
<p align="center" style="font-size: {font_size}; line-height: 1;">
<span style="display: inline-block; vertical-align: middle;">{svg_content}</span>
<span style="display: inline-block; vertical-align: middle;">{model_name.split('/')[-1]}</span>
</p>
<center><font size=3><b>Ovis</b> has been open-sourced on <a href='https://huggingface.co./{model_name}'>😊 Huggingface</a> and <a href='https://github.com/AIDC-AI/Ovis'>🌟 GitHub</a>. If you find Ovis useful, a like❤️ or a star🌟 would be appreciated.</font></center>
"""
latex_delimiters_set = [{
"left": "\\(",
"right": "\\)",
"display": False
}, {
"left": "\\begin{equation}",
"right": "\\end{equation}",
"display": True
}, {
"left": "\\begin{align}",
"right": "\\end{align}",
"display": True
}, {
"left": "\\begin{alignat}",
"right": "\\end{alignat}",
"display": True
}, {
"left": "\\begin{gather}",
"right": "\\end{gather}",
"display": True
}, {
"left": "\\begin{CD}",
"right": "\\end{CD}",
"display": True
}, {
"left": "\\[",
"right": "\\]",
"display": True
}]
text_input = gr.Textbox(label="prompt", placeholder="Enter your text here...", lines=1, container=False)
with gr.Blocks(title=model_name.split('/')[-1]) as demo:
gr.HTML(html)
with gr.Row():
with gr.Column(scale=3):
image_input = gr.Image(label="image", height=350, type="pil")
gr.Examples(
examples=[
[f"{cur_dir}/examples/case0.png", "Find the area of the shaded region."],
[f"{cur_dir}/examples/case1.png", "explain this model to me."],
[f"{cur_dir}/examples/case2.png", "What is net profit margin as a percentage of total revenue?"],
],
inputs=[image_input, text_input]
)
with gr.Column(scale=7):
chatbot = gr.Chatbot(label="Ovis", layout="panel", height=600, show_copy_button=True, latex_delimiters=latex_delimiters_set)
text_input.render()
with gr.Row():
send_btn = gr.Button("Send", variant="primary")
clear_btn = gr.Button("Clear", variant="secondary")
send_click_event = send_btn.click(submit_chat, [chatbot, text_input], [chatbot, text_input]).then(ovis_chat,[chatbot, image_input],chatbot)
submit_event = text_input.submit(submit_chat, [chatbot, text_input], [chatbot, text_input]).then(ovis_chat,[chatbot, image_input],chatbot)
clear_btn.click(clear_chat, outputs=[chatbot, image_input, text_input])
demo.launch() |