Spaces:
Runtime error
Runtime error
File size: 13,199 Bytes
a65550c 10068f3 a65550c 5ccd5ad a65550c 33cb067 a65550c a184f90 |
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 |
# from .demo_modelpart import InferenceDemo
import gradio as gr
import os
# import time
import cv2
# import copy
import torch
import spaces
import numpy as np
from llava import conversation as conversation_lib
from llava.constants import DEFAULT_IMAGE_TOKEN
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
from PIL import Image
import requests
from PIL import Image
from io import BytesIO
from transformers import TextStreamer
class InferenceDemo(object):
def __init__(self,args,model_path,tokenizer, model, image_processor, context_len) -> None:
disable_torch_init()
self.tokenizer, self.model, self.image_processor, self.context_len = tokenizer, model, image_processor, context_len
if "llama-2" in model_name.lower():
conv_mode = "llava_llama_2"
elif "v1" in model_name.lower():
conv_mode = "llava_v1"
elif "mpt" in model_name.lower():
conv_mode = "mpt"
elif 'qwen' in model_name.lower():
conv_mode = "qwen_1_5"
else:
conv_mode = "llava_v0"
if args.conv_mode is not None and conv_mode != args.conv_mode:
print("[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}".format(conv_mode, args.conv_mode, args.conv_mode))
else:
args.conv_mode = conv_mode
self.conv_mode=conv_mode
self.conversation = conv_templates[args.conv_mode].copy()
self.num_frames = args.num_frames
def is_valid_video_filename(name):
video_extensions = ['avi', 'mp4', 'mov', 'mkv', 'flv', 'wmv', 'mjpeg']
ext = name.split('.')[-1].lower()
if ext in video_extensions:
return True
else:
return False
def sample_frames(video_file, num_frames) :
video = cv2.VideoCapture(video_file)
total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
interval = total_frames // num_frames
frames = []
for i in range(total_frames):
ret, frame = video.read()
pil_img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
if not ret:
continue
if i % interval == 0:
frames.append(pil_img)
video.release()
return frames
def load_image(image_file):
if image_file.startswith("http") or image_file.startswith("https"):
response = requests.get(image_file)
if response.status_code == 200:
image = Image.open(BytesIO(response.content)).convert("RGB")
else:
print('failed to load the image')
else:
print('Load image from local file')
print(image_file)
image = Image.open(image_file).convert("RGB")
return image
def clear_history(history):
our_chatbot.conversation = conv_templates[our_chatbot.conv_mode].copy()
return None
def clear_response(history):
for index_conv in range(1, len(history)):
# loop until get a text response from our model.
conv = history[-index_conv]
if not (conv[0] is None):
break
question = history[-index_conv][0]
history = history[:-index_conv]
return history, question
def print_like_dislike(x: gr.LikeData):
print(x.index, x.value, x.liked)
def add_message(history, message):
# history=[]
global our_chatbot
if len(history)==0:
our_chatbot = InferenceDemo(args,model_path,tokenizer, model, image_processor, context_len)
for x in message["files"]:
history.append(((x,), None))
if message["text"] is not None:
history.append((message["text"], None))
return history, gr.MultimodalTextbox(value=None, interactive=False)
@spaces.GPU
def bot(history):
text=history[-1][0]
images_this_term=[]
text_this_term=''
# import pdb;pdb.set_trace()
num_new_images = 0
for i,message in enumerate(history[:-1]):
if type(message[0]) is tuple:
images_this_term.append(message[0][0])
if is_valid_video_filename(message[0][0]):
num_new_images+=our_chatbot.num_frames
else:
num_new_images+=1
else:
num_new_images=0
# for message in history[-i-1:]:
# images_this_term.append(message[0][0])
assert len(images_this_term)>0, "must have an image"
# image_files = (args.image_file).split(',')
# image = [load_image(f) for f in images_this_term if f]
image_list=[]
for f in images_this_term:
if is_valid_video_filename(f):
image_list+=sample_frames(f, our_chatbot.num_frames)
else:
image_list.append(load_image(f))
image_tensor = [our_chatbot.image_processor.preprocess(f, return_tensors="pt")["pixel_values"][0].half().to(our_chatbot.model.device) for f in image_list]
image_tensor = torch.stack(image_tensor)
image_token = DEFAULT_IMAGE_TOKEN*num_new_images
# if our_chatbot.model.config.mm_use_im_start_end:
# inp = DEFAULT_IM_START_TOKEN + image_token + DEFAULT_IM_END_TOKEN + "\n" + inp
# else:
inp=text
inp = image_token+ "\n" + inp
our_chatbot.conversation.append_message(our_chatbot.conversation.roles[0], inp)
# image = None
our_chatbot.conversation.append_message(our_chatbot.conversation.roles[1], None)
prompt = our_chatbot.conversation.get_prompt()
input_ids = tokenizer_image_token(prompt, our_chatbot.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(our_chatbot.model.device)
stop_str = our_chatbot.conversation.sep if our_chatbot.conversation.sep_style != SeparatorStyle.TWO else our_chatbot.conversation.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, our_chatbot.tokenizer, input_ids)
streamer = TextStreamer(our_chatbot.tokenizer, skip_prompt=True, skip_special_tokens=True)
# import pdb;pdb.set_trace()
with torch.inference_mode():
output_ids = our_chatbot.model.generate(input_ids, images=image_tensor, do_sample=True, temperature=0.2, max_new_tokens=1024, streamer=streamer, use_cache=False, stopping_criteria=[stopping_criteria])
outputs = our_chatbot.tokenizer.decode(output_ids[0]).strip()
if outputs.endswith(stop_str):
outputs = outputs[:-len(stop_str)]
our_chatbot.conversation.messages[-1][-1] = outputs
history[-1]=[text,outputs]
return history
txt = gr.Textbox(
scale=4,
show_label=False,
placeholder="Enter text and press enter.",
container=False,
)
with gr.Blocks() as demo:
# Informations
title_markdown = ("""
# LLaVA-NeXT Interleave
[[Blog]](https://llava-vl.github.io/blog/2024-06-16-llava-next-interleave/) [[Code]](https://github.com/LLaVA-VL/LLaVA-NeXT) [[Model]](https://huggingface.co/lmms-lab/llava-next-interleave-7b)
""")
tos_markdown = ("""
### TODO!. Terms of use
By using this service, users are required to agree to the following terms:
The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research.
Please click the "Flag" button if you get any inappropriate answer! We will collect those to keep improving our moderator.
For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality.
""")
learn_more_markdown = ("""
### TODO!. License
The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
""")
models = [
"LLaVA-Interleave-7B",
]
cur_dir = os.path.dirname(os.path.abspath(__file__))
gr.Markdown(title_markdown)
chatbot = gr.Chatbot(
[],
elem_id="chatbot",
bubble_full_width=False
)
chat_input = gr.MultimodalTextbox(interactive=True, file_types=["image","video"], placeholder="Enter message or upload file...", show_label=False)
with gr.Row():
upvote_btn = gr.Button(value="👍 Upvote", interactive=True)
downvote_btn = gr.Button(value="👎 Downvote", interactive=True)
flag_btn = gr.Button(value="⚠️ Flag", interactive=True)
#stop_btn = gr.Button(value="⏹️ Stop Generation", interactive=True)
regenerate_btn = gr.Button(value="🔄 Regenerate", interactive=True)
clear_btn = gr.Button(value="🗑️ Clear history", interactive=True)
chat_msg = chat_input.submit(add_message, [chatbot, chat_input], [chatbot, chat_input])
bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name="bot_response")
bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])
chatbot.like(print_like_dislike, None, None)
clear_btn.click(fn=clear_history, inputs=[chatbot], outputs=[chatbot], api_name="clear_all")
with gr.Column():
gr.Examples(examples=[
[{"files": [f"{cur_dir}/examples/code1.jpeg",f"{cur_dir}/examples/code2.jpeg"], "text": "Please pay attention to the movement of the object from the first image to the second image, then write a HTML code to show this movement."}],
[{"files": [f"{cur_dir}/examples/shub.jpg",f"{cur_dir}/examples/shuc.jpg",f"{cur_dir}/examples/shud.jpg"], "text": "what is fun about the images?"}],
[{"files": [f"{cur_dir}/examples/iphone-15-price-1024x576.jpg",f"{cur_dir}/examples/dynamic-island-1024x576.jpg",f"{cur_dir}/examples/iphone-15-colors-1024x576.jpg",f"{cur_dir}/examples/Iphone-15-Usb-c-charger-1024x576.jpg",f"{cur_dir}/examples/A-17-processors-1024x576.jpg"], "text": "The images are the PPT of iPhone 15 review. can you summarize the main information?"}],
[{"files": [f"{cur_dir}/examples/fangao3.jpeg",f"{cur_dir}/examples/fangao2.jpeg",f"{cur_dir}/examples/fangao1.jpeg"], "text": "Do you kown who draw these paintings?"}],
[{"files": [f"{cur_dir}/examples/oprah-winfrey-resume.png",f"{cur_dir}/examples/steve-jobs-resume.jpg"], "text": "Hi, there are two candidates, can you provide a brief description for each of them for me?"}],
[{"files": [f"{cur_dir}/examples/original_bench.jpeg",f"{cur_dir}/examples/changed_bench.jpeg"], "text": "How to edit image1 to make it look like image2?"}],
[{"files": [f"{cur_dir}/examples/twitter2.jpeg",f"{cur_dir}/examples/twitter3.jpeg",f"{cur_dir}/examples/twitter4.jpeg"], "text": "Please write a twitter blog post with the images."}],
# [{"files": [f"{cur_dir}/examples/twitter3.jpeg",f"{cur_dir}/examples/twitter4.jpeg"], "text": "Please write a twitter blog post with the images."}],
# [{"files": [f"playground/demo/examples/lion1_.mp4",f"playground/demo/examples/lion2_.mp4"], "text": "The input contains two videos, the first half is the first video and the second half is the second video. What is the difference between the two videos?"}],
], inputs=[chat_input], label="Compare images: ")
demo.queue()
if __name__ == "__main__":
import argparse
argparser = argparse.ArgumentParser()
argparser.add_argument("--server_name", default="0.0.0.0", type=str)
argparser.add_argument("--port", default="6123", type=str)
argparser.add_argument("--model_path", default="lmms-lab/llava-next-interleave-qwen-7b", type=str)
# argparser.add_argument("--model-path", type=str, default="facebook/opt-350m")
argparser.add_argument("--model-base", type=str, default=None)
argparser.add_argument("--num-gpus", type=int, default=1)
argparser.add_argument("--conv-mode", type=str, default=None)
argparser.add_argument("--temperature", type=float, default=0.2)
argparser.add_argument("--max-new-tokens", type=int, default=512)
argparser.add_argument("--num_frames", type=int, default=16)
argparser.add_argument("--load-8bit", action="store_true")
argparser.add_argument("--load-4bit", action="store_true")
argparser.add_argument("--debug", action="store_true")
args = argparser.parse_args()
model_path = args.model_path
filt_invalid="cut"
model_name = get_model_name_from_path(args.model_path)
tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit)
our_chatbot = None
# import pdb;pdb.set_trace()
# try:
demo.launch()
|