# 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, get_model_name_from_path, KeywordsStoppingCriteria, ) from serve_constants import html_header 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 import subprocess import sys from huggingface_hub import HfApi from huggingface_hub import login from huggingface_hub import revision_exists api = HfApi() repo_name = "paralym/pangea_logs" external_log_dir = "./logs" LOGDIR = external_log_dir 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 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" elif "pangea" 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 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_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, temperature, top_p, max_output_tokens): print("### turn start history",history) print("### turn start conv",our_chatbot.conversation) 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]): # 不接受视频 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") 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] 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 = [ 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) # ) input_ids = tokenizer_image_token( prompt, our_chatbot.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt" ).unsqueeze(0).to(our_chatbot.model.device) # print("### input_id",input_ids) 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 # ) 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) # with torch.inference_mode(): # output_ids = our_chatbot.model.generate( # input_ids, # images=image_tensor, # do_sample=True, # temperature=0.7, # top_p=1.0, # max_new_tokens=4096, # 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 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": "Pangea-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) with gr.Column(): with gr.Accordion("Parameters", open=False) as parameter_row: temperature = gr.Slider( minimum=0.0, maximum=1.0, value=0.7, 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="Pangea", bubble_full_width=False, height=750) 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_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=20, examples=[ [ { "files": [ f"{cur_dir}/examples/user_example_07.jpg", ], "text": "那要我问问你，你这个是什么🐱？", }, ], [ { "files": [ f"{cur_dir}/examples/user_example_05.jpg", ], "text": "この猫の目の大きさは、どのような理由で他の猫と比べて特に大きく見えますか？", }, ], [ { "files": [ f"{cur_dir}/examples/172197131626056_P7966202.png", ], "text": "Why this image funny?", }, ], [ { "files": [ f"{cur_dir}/examples/norway.jpg", ], "text": "Analysieren, in welchem Land diese Szene höchstwahrscheinlich gedreht wurde.", }, ], [ { "files": [ f"{cur_dir}/examples/totoro.jpg", ], "text": "¿En qué anime aparece esta escena? ¿Puedes presentarlo?", }, ], [ { "files": [ f"{cur_dir}/examples/africa.jpg", ], "text": "इस तस्वीर में हर एक दृश्य तत्व का क्या प्रतिनिधित्व करता है?", }, ], [ { "files": [ f"{cur_dir}/examples/hot_ballon.jpg", ], "text": "ฉากบอลลูนลมร้อนในภาพนี้อาจอยู่ที่ไหน? สถานที่นี้มีความพิเศษอย่างไร?", }, ], [ { "files": [ f"{cur_dir}/examples/bar.jpg", ], "text": "Você pode me dar ideias de design baseadas no tema de coquetéis deste letreiro?", }, ], [ { "files": [ f"{cur_dir}/examples/pink_lake.jpg", ], "text": "Обясни защо езерото на този остров е в този цвят.", }, ], [ { "files": [ f"{cur_dir}/examples/hanzi.jpg", ], "text": "Can you describe in Hebrew the evolution process of these four Chinese characters from pictographs to modern characters?", }, ], [ { "files": [ f"{cur_dir}/examples/ballon.jpg", ], "text": "இந்த காட்சியை விவரிக்கவும், மேலும் இந்த படத்தின் அடிப்படையில் துருக்கியில் இந்த காட்சியுடன் தொடர்பான சில பிரபலமான நிகழ்வுகள் என்ன?", }, ], [ { "files": [ f"{cur_dir}/examples/pie.jpg", ], "text": "Décrivez ce graphique. Quelles informations pouvons-nous en tirer?", }, ], [ { "files": [ f"{cur_dir}/examples/camera.jpg", ], "text": "Apa arti dari dua angka di sebelah kiri yang ditampilkan di layar kamera?", }, ], [ { "files": [ f"{cur_dir}/examples/dog.jpg", ], "text": "이 강아지의 표정을 보고 어떤 기분이나 감정을 느끼고 있는지 설명해 주시겠어요?", }, ], [ { "files": [ f"{cur_dir}/examples/book.jpg", ], "text": "What language is the text in, and what does the title mean in English?", }, ], [ { "files": [ f"{cur_dir}/examples/food.jpg", ], "text": "Unaweza kunipa kichocheo cha kutengeneza hii pancake?", }, ], [ { "files": [ f"{cur_dir}/examples/line chart.jpg", ], "text": "Hãy trình bày những xu hướng mà bạn quan sát được từ biểu đồ và hiện tượng xã hội tiềm ẩn từ đó.", }, ], [ { "files": [ f"{cur_dir}/examples/south africa.jpg", ], "text": "Waar is hierdie plek? Help my om ’n reisroete vir hierdie land te beplan.", }, ], [ { "files": [ f"{cur_dir}/examples/girl.jpg", ], "text": "لماذا هذه الصورة مضحكة؟", }, ], [ { "files": [ f"{cur_dir}/examples/eagles.jpg", ], "text": "Какой креатив должен быть в этом логотипе?", }, ], ], inputs=[chat_input], label="Image", ) chat_msg = chat_input.submit( add_message, [chatbot, chat_input], [chatbot, chat_input] ) bot_msg = chat_msg.then(bot, [chatbot,temperature, top_p, max_output_tokens], 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" ) 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="neulab/Pangea-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=0.7) argparser.add_argument("--max-new-tokens", type=int, default=4096) 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 demo.launch()