# 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) print(our_chatbot.model.device) print(input_ids.device) print(image_tensor.device) # 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="⚠ī¸ Flag", interactive=True) #stop_btn = gr.Button(value="⏚ī¸ Stop Generation", interactive=True) regenerate_btn = gr.Button(value="🔄 Regenerate", interactive=True) clear_btn = gr.Button(value="🗑ī¸ 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(): with gr.Row(): 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: ",examples_per_page=3) 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) model=model.to(torch.device('cuda')) our_chatbot = None # import pdb;pdb.set_trace() # try: demo.launch()