Spaces:
Runtime error
Runtime error
File size: 6,397 Bytes
493e200 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 |
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() |