Spaces:
Runtime error
Runtime error
File size: 12,629 Bytes
09773e9 955a80d 09773e9 175e27a 09773e9 3a7c22b 09773e9 3a7c22b 09773e9 3a7c22b 09773e9 712e55f 09773e9 712e55f 09773e9 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 |
import gradio as gr
import spaces
import os
import time
from PIL import Image
import functools
import torch
import matplotlib.pyplot as plt
import re
import ast
from model import GeckoForConditionalGeneration, GeckoConfig, GeckoProcessor, chat_gecko, chat_gecko_stream
from model.conversation import conv_templates
from typing import List
from io import StringIO
import sys
class Capturing(list):
def __enter__(self):
self._stdout = sys.stdout
sys.stdout = self._stringio = StringIO()
return self
def __exit__(self, *args):
self.extend(self._stringio.getvalue().splitlines())
del self._stringio # free up some memory
sys.stdout = self._stdout
# initialization
topk = 1
keyword_criteria = 'word'
positional_information = 'explicit'
vision_feature_select_strategy = 'cls'
patch_picking_strategy = 'last_layer'
cropping_method = 'naive'
crop_size = 384
visualize_topk_patches = False
print_keyword=True
print_topk_patches = True
torch_dtype = torch.float16
attn_implementation = 'sdpa'
device_map = 'cuda'
conv_template = conv_templates['llama_3']
model = 'TIGER-Lab/Mantis-8B-siglip-llama3'
config = GeckoConfig.from_pretrained(model,
topk=topk,
visualize_topk_patches=visualize_topk_patches,
keyword_criteria=keyword_criteria,
positional_information=positional_information,
vision_feature_select_strategy=vision_feature_select_strategy,
patch_picking_strategy=patch_picking_strategy,
print_keyword=print_keyword)
processor = GeckoProcessor.from_pretrained(model, config=config, use_keyword=True, cropping_method=cropping_method, crop_size=crop_size)
model = GeckoForConditionalGeneration.from_pretrained(
model, config=config)
model.load_text_encoder(processor)
@spaces.GPU
def generate_stream(text:str, images:List[Image.Image], history: List[dict], **kwargs):
global processor, model
model = model.to("cuda")
if not images:
images = None
# print(history)
print(f'length of images: {len(images)}')
generator, print_kw, inputs = chat_gecko_stream(text, images, model, processor, history=history, **kwargs)
texts = []
# for text, history in chat_gecko_stream(text, images, model, processor, history=history, **kwargs):
# yield text
for text, history in generator:
texts.append(text)
# return text
return texts, print_kw, inputs
@spaces.GPU
def generate(text:str, images:List[Image.Image], history: List[dict], **kwargs):
global processor, model
model = model.to("cuda")
if not images:
images = None
generated_text, history = chat_gecko(text, images, model, processor, history=history, **kwargs)
return generated_text
def enable_next_image(uploaded_images, image):
uploaded_images.append(image)
return uploaded_images, gr.MultimodalTextbox(value=None, interactive=False)
def add_message(history, message):
if message["files"]:
for file in message["files"]:
history.append([(file,), None])
if message["text"]:
history.append([message["text"], None])
return history, gr.MultimodalTextbox(value=None)
def print_like_dislike(x: gr.LikeData):
print(x.index, x.value, x.liked)
def get_chat_history(history):
chat_history = []
user_role = conv_template.roles[0]
assistant_role = conv_template.roles[1]
for i, message in enumerate(history):
if isinstance(message[0], str):
chat_history.append({"role": user_role, "text": message[0]})
if i != len(history) - 1:
assert message[1], "The bot message is not provided, internal error"
chat_history.append({"role": assistant_role, "text": message[1]})
else:
assert not message[1], "the bot message internal error, get: {}".format(message[1])
chat_history.append({"role": assistant_role, "text": ""})
return chat_history
def get_chat_images(history):
images = []
for message in history:
if isinstance(message[0], tuple):
images.extend(message[0])
return images
def bot(history, topk=None):
print(history)
cur_messages = {"text": "", "images": []}
for message in history[::-1]:
if message[1]:
break
if isinstance(message[0], str):
cur_messages["text"] = message[0] + " " + cur_messages["text"]
elif isinstance(message[0], tuple):
cur_messages["images"].extend(message[0])
cur_messages["text"] = cur_messages["text"].strip()
cur_messages["images"] = cur_messages["images"][::-1]
if not cur_messages["text"]:
raise gr.Error("Please enter a message")
if cur_messages['text'].count("<image>") < len(cur_messages['images']):
gr.Warning("The number of images uploaded is more than the number of <image> placeholders in the text. Will automatically prepend <image> to the text.")
cur_messages['text'] = "<image> "* (len(cur_messages['images']) - cur_messages['text'].count("<image>")) + cur_messages['text']
history[-1][0] = cur_messages["text"]
if cur_messages['text'].count("<image>") > len(cur_messages['images']):
gr.Warning("The number of images uploaded is less than the number of <image> placeholders in the text. Will automatically remove extra <image> placeholders from the text.")
cur_messages['text'] = cur_messages['text'][::-1].replace("<image>"[::-1], "", cur_messages['text'].count("<image>") - len(cur_messages['images']))[::-1]
history[-1][0] = cur_messages["text"]
chat_history = get_chat_history(history)
chat_images = get_chat_images(history)
generation_kwargs = {
"max_new_tokens": 4096,
"num_beams": 1,
"do_sample": False,
"topk": topk,
}
response = generate_stream(None, chat_images, chat_history, **generation_kwargs)
num_images = len(response[2].pixel_values)
coords = response[1][-num_images:]
print_kw = '\n'.join(response[1][:-num_images-1])
patches_fig = plot_patches(response[2])
topk_patches_fig = plot_topk_patches(response[2], coords)
for _output in response[0]:
history[-1][1] = _output
time.sleep(0.05)
# yield history, print_kw, patches_fig, topk_patches_fig
history
def plot_patches(inputs):
pixel_value = inputs.pixel_values[0].cpu().numpy()
x, y = inputs.coords[0][-1][0] + 1, inputs.coords[0][-1][1] + 1
fig, axes = plt.subplots(y, x, figsize=(x * 4, y * 4))
for i in range(y):
for j in range(x):
axes[i, j].imshow(pixel_value[1 + i * x + j].transpose(1, 2, 0))
axes[i, j].axis('off')
return fig
def plot_topk_patches(inputs, selected_coords):
selected_coords_list = []
for selected_coord in selected_coords:
match = re.search(r"\[\[.*\]\]", selected_coord)
if match:
coordinates_str = match.group(0)
# Convert the string representation of the list to an actual list
coordinates = ast.literal_eval(coordinates_str)
selected_coords_list.append(coordinates)
num_images = len(selected_coords_list)
fig, axes = plt.subplots(num_images, len(selected_coords_list[0])+1, figsize=((len(selected_coords_list[0])+1) * 10, num_images * 10))
if num_images == 1:
xmax = inputs.coords[0][-1][0] + 1
for j in range(len(selected_coords_list[0])+1):
if j == 0:
axes[j].imshow(inputs.pixel_values[0][0].cpu().numpy().transpose(1, 2, 0))
axes[j].axis('off')
continue
x, y = selected_coords_list[0][j-1][0], selected_coords_list[0][j-1][1]
axes[j].imshow(inputs.pixel_values[0][1 + y * xmax + x].cpu().numpy().transpose(1, 2, 0))
axes[j].axis('off')
else:
for i in range(num_images):
xmax = inputs.coords[i][-1][0] + 1
for j in range(len(selected_coords_list[0])+1):
if j == 0:
axes[i, j].imshow(inputs.pixel_values[i][0].cpu().numpy().transpose(1, 2, 0))
continue
x, y = selected_coords_list[i][j-1][0], selected_coords_list[i][j-1][1]
axes[i, j].imshow(inputs.pixel_values[i][1 + y * xmax + x].cpu().numpy().transpose(1, 2, 0))
axes[i, j].axis('off')
return fig
def build_demo():
with gr.Blocks() as demo:
# gr.Markdown(""" # Mantis
# Mantis is a multimodal conversational AI model that can chat with users about images and text. It's optimized for multi-image reasoning, where inverleaved text and images can be used to generate responses.
# ### [Paper](https://arxiv.org/abs/2405.01483) | [Github](https://github.com/TIGER-AI-Lab/Mantis) | [Models](https://huggingface.co/collections/TIGER-Lab/mantis-6619b0834594c878cdb1d6e4) | [Dataset](https://huggingface.co/datasets/TIGER-Lab/Mantis-Instruct) | [Website](https://tiger-ai-lab.github.io/Mantis/)
# """)
# gr.Markdown("""## Chat with Mantis
# Mantis supports interleaved text-image input format, where you can simply use the placeholder `<image>` to indicate the position of uploaded images.
# The model is optimized for multi-image reasoning, while preserving the ability to chat about text and images in a single conversation.
# (The model currently serving is [π€ TIGER-Lab/Mantis-8B-siglip-llama3](https://huggingface.co/TIGER-Lab/Mantis-8B-siglip-llama3))
# """)
chatbot = gr.Chatbot(line_breaks=True)
chat_input = gr.MultimodalTextbox(interactive=True, file_types=["image"], placeholder="Enter message or upload images. Please use <image> to indicate the position of uploaded images", show_label=True)
chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input])
# print_kw = gr.Textbox(label="keywords")
# depict_patches = gr.Plot(label="image patches", format="png")
# depict_topk_patches = gr.Plot(label="top-k image patches", format="png")
# with gr.Accordion(label='Advanced options', open=False):
# temperature = gr.Slider(
# label='Temperature',
# minimum=0.1,
# maximum=2.0,
# step=0.1,
# value=0.2,
# interactive=True
# )
# top_p = gr.Slider(
# label='Top-p',
# minimum=0.05,
# maximum=1.0,
# step=0.05,
# value=1.0,
# interactive=True
# )
topk = gr.Slider(
label='Top-k',
minimum=1,
maximum=10,
step=1,
value=1,
interactive=True)
bot_msg = chat_msg.success(bot, chatbot,
chatbot, api_name="bot_response")
chatbot.like(print_like_dislike, None, None)
with gr.Row():
send_button = gr.Button("Send")
clear_button = gr.ClearButton([chatbot, chat_input])
send_button.click(
add_message, [chatbot, chat_input], [chatbot, chat_input]
).then(
bot,
[chatbot, topk],
# [chatbot, print_kw, depict_patches, depict_topk_patches], api_name="bot_response"
chatbot
)
gr.Examples(
examples=[
{
"text": open("./examples/little_girl.txt").read(),
"files": ["./examples/little_girl.jpg"]
},
{
"text": open("./examples/bus_luggage.txt").read(),
"files": ["./examples/bus_luggage.jpg"]
},
],
inputs=[chat_input],
)
# gr.Markdown("""
# ## Citation
# ```
# @article{jiang2024mantis,
# title={MANTIS: Interleaved Multi-Image Instruction Tuning},
# author={Jiang, Dongfu and He, Xuan and Zeng, Huaye and Wei, Con and Ku, Max and Liu, Qian and Chen, Wenhu},
# journal={arXiv preprint arXiv:2405.01483},
# year={2024}
# }
# ```""")
return demo
if __name__ == "__main__":
demo = build_demo()
demo.launch(share=False)
|