# 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, 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 import subprocess import sys 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") if not os.path.isfile(name): os.makedirs(os.path.dirname(name), exist_ok=True) 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", "Pangea-7b") gr.Info("Thank you for your voting!") return state def downvote_last_response(state): vote_last_response(state, "downvote", "Pangea-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(): 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 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): global chat_image_num print("#### len(history)",len(history)) if not history: history = [] print("### Initialize chatbot") our_chatbot = chat_manager.get_chatbot(args, model_path, tokenizer, model, image_processor, context_len) chat_image_num = 0 print("chat_image_num", chat_image_num) 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) 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("chat_image_num", chat_image_num) print(f"### Chatbot instance ID: {id(our_chatbot)}") return history, gr.MultimodalTextbox(value=None, interactive=False) else: 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): our_chatbot = chat_manager.get_chatbot(args, model_path, tokenizer, model, image_processor, context_len) print(f"### Chatbot instance ID: {id(our_chatbot)}") text = history[-1][0] images_this_term = [] text_this_term = "" 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 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 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 = [] if num_new_images > 0: 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 inp = text inp = image_token + "\n" + inp else: inp = text 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) # 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 = TextIteratorStreamer( our_chatbot.tokenizer, skip_prompt=True, skip_special_tokens=True ) print(our_chatbot.model.device) print(input_ids.device) # print(image_tensor.device) generate_kwargs = dict( inputs=input_ids, streamer=streamer, images=image_tensor if num_new_images > 0 else None, 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) 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", ) gr.Markdown(tos_markdown) gr.Markdown(learn_more_markdown) gr.Markdown(bibtext) 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="upvote_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="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')) chat_image_num = 0 demo.launch()