import subprocess import sys import os # from .demo_modelpart import InferenceDemo import gradio as gr import os from threading import Thread # import time import cv2 import datetime # 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, process_images, get_model_name_from_path, KeywordsStoppingCriteria, ) from serve_constants import html_header, bibtext, learn_more_markdown, tos_markdown import requests from PIL import Image from io import BytesIO from transformers import TextStreamer, TextIteratorStreamer import hashlib import PIL import base64 import json import datetime import gradio as gr import gradio_client from huggingface_hub import HfApi from huggingface_hub import login from huggingface_hub import revision_exists login(token=os.environ["HF_TOKEN"], write_permission=True) api = HfApi() repo_name = os.environ["LOG_REPO"] external_log_dir = "./logs" LOGDIR = external_log_dir VOTEDIR = "./votes" def install_gradio_4_35_0(): current_version = gr.__version__ if current_version != "4.35.0": print(f"Current Gradio version: {current_version}") print("Installing Gradio 4.35.0...") subprocess.check_call([sys.executable, "-m", "pip", "install", "gradio==4.35.0", "--force-reinstall"]) print("Gradio 4.35.0 installed successfully.") else: print("Gradio 4.35.0 is already installed.") # Call the function to install Gradio 4.35.0 if needed install_gradio_4_35_0() import gradio as gr import gradio_client print(f"Gradio version: {gr.__version__}") print(f"Gradio-client version: {gradio_client.__version__}") def get_conv_log_filename(): t = datetime.datetime.now() name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-user_conv.json") return name def get_conv_vote_filename(): t = datetime.datetime.now() name = os.path.join(VOTEDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-user_vote.json") if not os.path.isfile(name): os.makedirs(os.path.dirname(name), exist_ok=True) return name def vote_last_response(state, vote_type, model_selector): with open(get_conv_vote_filename(), "a") as fout: data = { "type": vote_type, "model": model_selector, "state": state, } fout.write(json.dumps(data) + "\n") api.upload_file( path_or_fileobj=get_conv_vote_filename(), path_in_repo=get_conv_vote_filename().replace("./votes/", ""), repo_id=repo_name, repo_type="dataset") def upvote_last_response(state): vote_last_response(state, "upvote", "PULSE-7B") gr.Info("Thank you for your voting!") return state def downvote_last_response(state): vote_last_response(state, "downvote", "PULSE-7B") gr.Info("Thank you for your voting!") return state 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() or "pulse" 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 class ChatSessionManager: def __init__(self): self.chatbot_instance = None def initialize_chatbot(self, args, model_path, tokenizer, model, image_processor, context_len): self.chatbot_instance = InferenceDemo(args, model_path, tokenizer, model, image_processor, context_len) print(f"Initialized Chatbot instance with ID: {id(self.chatbot_instance)}") def reset_chatbot(self): self.chatbot_instance = None def get_chatbot(self, args, model_path, tokenizer, model, image_processor, context_len): if self.chatbot_instance is None: self.initialize_chatbot(args, model_path, tokenizer, model, image_processor, context_len) return self.chatbot_instance 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 is_valid_image_filename(name): image_extensions = ["jpg", "jpeg", "png", "bmp", "gif", "tiff", "webp", "heic", "heif", "jfif", "svg", "eps", "raw"] ext = name.split(".")[-1].lower() if ext in image_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_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 chat_manager = ChatSessionManager() def clear_history(history): chatbot_instance = chat_manager.get_chatbot(args, model_path, tokenizer, model, image_processor, context_len) chatbot_instance.conversation = conv_templates[chatbot_instance.conv_mode].copy() return None def add_message(history, message): # history=[] # global our_chatbot global chat_image_num if not history: history = [] our_chatbot = chat_manager.get_chatbot(args, model_path, tokenizer, model, image_processor, context_len) # our_chatbot = InferenceDemo( # args, model_path, tokenizer, model, image_processor, context_len # ) chat_image_num = 0 print("# Add message message",message) if len(message["files"]) <= 1: for x in message["files"]: history.append(((x,), None)) chat_image_num += 1 if chat_image_num > 1: history = [] chat_manager.reset_chatbot() our_chatbot = chat_manager.get_chatbot(args, model_path, tokenizer, model, image_processor, context_len) # our_chatbot = InferenceDemo( # args, model_path, tokenizer, model, image_processor, context_len # ) chat_image_num = 0 for x in message["files"]: history.append(((x,), None)) chat_image_num += 1 if message["text"] is not None: history.append((message["text"], None)) print("### Not bigger than one history", history) print("### Not bigger than one conv", our_chatbot.conversation) print(f"### Chatbot instance ID: {id(our_chatbot)}") return history, gr.MultimodalTextbox(value=None, interactive=False)#, our_chatbot else: for x in message["files"]: history.append(((x,), None)) if message["text"] is not None: history.append((message["text"], None)) print("### Bigger than one history", history) print("### Bigger than one conv", our_chatbot.conversation) return history, gr.MultimodalTextbox(value=None, interactive=False)#, our_chatbot @spaces.GPU def bot(history, temperature, top_p, max_output_tokens): # global our_chatbot # if not history: # gr.Warning("Only one image can be uploaded in a conversation. Please reduce the number of images and start a new conversation.") # return history print("### turn start history",history) our_chatbot = chat_manager.get_chatbot(args, model_path, tokenizer, model, image_processor, context_len) print("### turn start conv",our_chatbot.conversation) print(f"### Chatbot instance ID: {id(our_chatbot)}") text = history[-1][0] images_this_term = [] text_this_term = "" # import pdb;pdb.set_trace() num_new_images = 0 previous_image = False for i, message in enumerate(history[:-1]): if type(message[0]) is tuple: if previous_image: gr.Warning("Only one image can be uploaded in a conversation. Please reduce the number of images and start a new conversation.") our_chatbot.conversation = conv_templates[our_chatbot.conv_mode].copy() return None # print("### message[0]",message[0]) # if len(message[0])>1: # gr.Warning("Only one image can be uploaded in a conversation. Please reduce the number of images and start a new conversation.") # return history # else: images_this_term.append(message[0][0]) if is_valid_video_filename(message[0][0]): raise ValueError("Video is not supported") num_new_images += our_chatbot.num_frames elif is_valid_image_filename(message[0][0]): print("#### Load image from local file",message[0][0]) num_new_images += 1 else: raise ValueError("Invalid image file") previous_image = True else: num_new_images = 0 previous_image = False # 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] all_image_hash = [] all_image_path = [] for image_path in images_this_term: with open(image_path, "rb") as image_file: image_data = image_file.read() image_hash = hashlib.md5(image_data).hexdigest() all_image_hash.append(image_hash) image = PIL.Image.open(image_path).convert("RGB") t = datetime.datetime.now() filename = os.path.join( LOGDIR, "serve_images", f"{t.year}-{t.month:02d}-{t.day:02d}", f"{image_hash}.jpg", ) all_image_path.append(filename) if not os.path.isfile(filename): os.makedirs(os.path.dirname(filename), exist_ok=True) print("image save to",filename) image.save(filename) image_list = [] for f in images_this_term: if is_valid_video_filename(f): image_list += sample_frames(f, our_chatbot.num_frames) elif is_valid_image_filename(f): image_list.append(load_image(f)) else: raise ValueError("Invalid image file") image_tensor = [ process_images([f], our_chatbot.image_processor, our_chatbot.model.config)[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() if len(images_this_term) == 0: gr.Warning("You should upload an image. Please upload the image and try again.") return history # if len(images_this_term) > 1: # gr.Warning("Only one image can be uploaded in a conversation. Please reduce the number of images and start a new conversation.") # return history 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 = TextIteratorStreamer( our_chatbot.tokenizer, skip_prompt=True, skip_special_tokens=True ) print(our_chatbot.model.device) print(input_ids.device) print(image_tensor.device) # our_chatbot.conversation.messages[-1][-1] = outputs # history[-1] = [text, outputs] # return history generate_kwargs = dict( inputs=input_ids, streamer=streamer, images=image_tensor, do_sample=True, temperature=temperature, top_p=top_p, max_new_tokens=max_output_tokens, use_cache=False, stopping_criteria=[stopping_criteria], ) t = Thread(target=our_chatbot.model.generate, kwargs=generate_kwargs) t.start() outputs = [] for stream_token in streamer: outputs.append(stream_token) # print("### stream_token",stream_token) # our_chatbot.conversation.messages[-1][-1] = "".join(outputs) history[-1] = [text, "".join(outputs)] yield history our_chatbot.conversation.messages[-1][-1] = "".join(outputs) print("### turn end history", history) print("### turn end conv",our_chatbot.conversation) with open(get_conv_log_filename(), "a") as fout: data = { "type": "chat", "model": "PULSE-7b", "state": history, "images": all_image_hash, "images_path": all_image_path } print("#### conv log",data) fout.write(json.dumps(data) + "\n") for upload_img in all_image_path: api.upload_file( path_or_fileobj=upload_img, path_in_repo=upload_img.replace("./logs/", ""), repo_id=repo_name, repo_type="dataset", # revision=revision, # ignore_patterns=["data*"] ) # upload json api.upload_file( path_or_fileobj=get_conv_log_filename(), path_in_repo=get_conv_log_filename().replace("./logs/", ""), repo_id=repo_name, repo_type="dataset") txt = gr.Textbox( scale=4, show_label=False, placeholder="Enter text and press enter.", container=False, ) with gr.Blocks( css=".message-wrap.svelte-1lcyrx4>div.svelte-1lcyrx4 img {min-width: 40px}", ) as demo: cur_dir = os.path.dirname(os.path.abspath(__file__)) # gr.Markdown(title_markdown) gr.HTML(html_header) gr.Markdown("\nNote: \n1. To achieve the best results, we highly recommend that you provide standardized ECG images similar to the examples below, since interpreting ECGs is challenging for visual-language models, and excessive noise can exacerbate this issue.\n2. We recommend that you avoid uploading non-ECG-related content, as the model has been specifically optimized for ECG tasks. \n3. 🚨**The content generated by the model is for reference only and should not be used in real diagnostic scenarios.**") with gr.Column(): with gr.Accordion("Parameters", open=False) as parameter_row: temperature = gr.Slider( minimum=0.05, maximum=1.0, value=0.05, step=0.1, interactive=True, label="Temperature", ) top_p = gr.Slider( minimum=0.0, maximum=1.0, value=1, step=0.1, interactive=True, label="Top P", ) max_output_tokens = gr.Slider( minimum=0, maximum=8192, value=4096, step=256, interactive=True, label="Max output tokens", ) with gr.Row(): chatbot = gr.Chatbot([], elem_id="PULSE", bubble_full_width=False, height=750) # our_chatbot = None # our_chatbot = gr.Variable(None) # our_chatbot = gr.State(None) 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_input = gr.MultimodalTextbox( interactive=True, file_types=["image"], placeholder="Enter message or upload file...", show_label=False, submit_btn="🚀" ) print(cur_dir) gr.Examples( examples_per_page=5, examples=[ [ { "files": [ f"{cur_dir}/examples/ecg_example2.png", ], "text": "What are the main features in this ECG image?", }, ], [ { "files": [ f"{cur_dir}/examples/ecg_example1.jpg", ], "text": "What can be inferred from the pattern of the qR complexes and rS complexes in the leads of this ECG image?", }, ] ], inputs=[chat_input], label="Image", ) gr.Markdown(tos_markdown) gr.Markdown(learn_more_markdown) gr.Markdown(bibtext) # chat_msg = # chat_input.submit( # add_message, [chatbot, chat_input], [chatbot, chat_input] # ).then(bot, [chatbot,temperature, top_p, max_output_tokens], chatbot, api_name="bot_response").then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input]) chat_input.submit( add_message, [chatbot, chat_input], [chatbot, chat_input] ).then(bot, [chatbot, temperature, top_p, max_output_tokens], chatbot, api_name="bot_response").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" ) upvote_btn.click( fn=upvote_last_response, inputs=chatbot, outputs=chatbot, api_name="upvote_last_response" ) downvote_btn.click( fn=downvote_last_response, inputs=chatbot, outputs=chatbot, api_name="downvote_last_response" ) 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="PULSE-ECG/PULSE-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=0.05) argparser.add_argument("--max-new-tokens", type=int, default=1024) 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) print("### image_processor",image_processor) # print("### model",model) chat_image_num = 0 print("### tokenzier",tokenizer) model=model.to(torch.device('cuda')) # our_chatbot = None demo.launch()