gecko / app.py
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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 = 'flash_attention_2'
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
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 [πŸ€— 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"
)
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)