import argparse from ast import parse import datetime import json import os import time import hashlib import re import gradio as gr import requests import random from filelock import FileLock from io import BytesIO from PIL import Image, ImageDraw, ImageFont from constants import LOGDIR from utils import ( build_logger, server_error_msg, violates_moderation, moderation_msg, load_image_from_base64, get_log_filename, ) from conversation import Conversation logger = build_logger("gradio_web_server", "gradio_web_server.log") headers = {"User-Agent": "InternVL-Chat Client"} no_change_btn = gr.Button() enable_btn = gr.Button(interactive=True) disable_btn = gr.Button(interactive=False) def write2file(path, content): lock = FileLock(f"{path}.lock") with lock: with open(path, "a") as fout: fout.write(content) def sort_models(models): def custom_sort_key(model_name): # InternVL-Chat-V1-5 should be the first item if model_name == "InternVL-Chat-V1-5": return (1, model_name) # 1 indicates highest precedence elif model_name.startswith("InternVL-Chat-V1-5-"): return (1, model_name) # 1 indicates highest precedence else: return (0, model_name) # 0 indicates normal order models.sort(key=custom_sort_key, reverse=True) try: # We have five InternVL-Chat-V1-5 models, randomly choose one to be the first first_three = models[:4] random.shuffle(first_three) models[:4] = first_three except: pass return models def get_model_list(): ret = requests.post(args.controller_url + "/refresh_all_workers") assert ret.status_code == 200 ret = requests.post(args.controller_url + "/list_models") models = ret.json()["models"] models = sort_models(models) logger.info(f"Models: {models}") return models get_window_url_params = """ function() { const params = new URLSearchParams(window.location.search); url_params = Object.fromEntries(params); console.log(url_params); return url_params; } """ def init_state(state=None): if state is not None: del state return Conversation() def find_bounding_boxes(state, response): pattern = re.compile(r"\s*(.*?)\s*\s*\s*(\[\[.*?\]\])\s*") matches = pattern.findall(response) results = [] for match in matches: results.append((match[0], eval(match[1]))) returned_image = None latest_image = state.get_images(source=state.USER)[-1] returned_image = latest_image.copy() width, height = returned_image.size draw = ImageDraw.Draw(returned_image) for result in results: line_width = max(1, int(min(width, height) / 200)) random_color = ( random.randint(0, 128), random.randint(0, 128), random.randint(0, 128), ) category_name, coordinates = result coordinates = [ ( float(x[0]) / 1000, float(x[1]) / 1000, float(x[2]) / 1000, float(x[3]) / 1000, ) for x in coordinates ] coordinates = [ ( int(x[0] * width), int(x[1] * height), int(x[2] * width), int(x[3] * height), ) for x in coordinates ] for box in coordinates: draw.rectangle(box, outline=random_color, width=line_width) font = ImageFont.truetype("assets/SimHei.ttf", int(20 * line_width / 2)) text_size = font.getbbox(category_name) text_width, text_height = ( text_size[2] - text_size[0], text_size[3] - text_size[1], ) text_position = (box[0], max(0, box[1] - text_height)) draw.rectangle( [ text_position, (text_position[0] + text_width, text_position[1] + text_height), ], fill=random_color, ) draw.text(text_position, category_name, fill="white", font=font) return returned_image if len(matches) > 0 else None def query_image_generation(response, sd_worker_url, timeout=15): if not sd_worker_url: return None sd_worker_url = f"{sd_worker_url}/generate_image/" pattern = r"```drawing-instruction\n(.*?)\n```" match = re.search(pattern, response, re.DOTALL) if match: payload = {"caption": match.group(1)} print("drawing-instruction:", payload) response = requests.post(sd_worker_url, json=payload, timeout=timeout) response.raise_for_status() # 检查HTTP请求是否成功 image = Image.open(BytesIO(response.content)) return image else: return None def load_demo(url_params, request: gr.Request): logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}") dropdown_update = gr.Dropdown(visible=True) if "model" in url_params: model = url_params["model"] if model in models: dropdown_update = gr.Dropdown(value=model, visible=True) state = init_state() return state, dropdown_update def load_demo_refresh_model_list(request: gr.Request): logger.info(f"load_demo. ip: {request.client.host}") models = get_model_list() state = init_state() dropdown_update = gr.Dropdown( choices=models, value=models[0] if len(models) > 0 else "" ) return state, dropdown_update def vote_last_response(state, liked, model_selector, request: gr.Request): conv_data = { "tstamp": round(time.time(), 4), "like": liked, "model": model_selector, "state": state.dict(), "ip": request.client.host, } write2file(get_log_filename(), json.dumps(conv_data) + "\n") def upvote_last_response(state, model_selector, request: gr.Request): logger.info(f"upvote. ip: {request.client.host}") vote_last_response(state, True, model_selector, request) textbox = gr.MultimodalTextbox(value=None, interactive=True) return (textbox,) + (disable_btn,) * 3 def downvote_last_response(state, model_selector, request: gr.Request): logger.info(f"downvote. ip: {request.client.host}") vote_last_response(state, False, model_selector, request) textbox = gr.MultimodalTextbox(value=None, interactive=True) return (textbox,) + (disable_btn,) * 3 def vote_selected_response( state, model_selector, request: gr.Request, data: gr.LikeData ): logger.info( f"Vote: {data.liked}, index: {data.index}, value: {data.value} , ip: {request.client.host}" ) conv_data = { "tstamp": round(time.time(), 4), "like": data.liked, "index": data.index, "model": model_selector, "state": state.dict(), "ip": request.client.host, } write2file(get_log_filename(), json.dumps(conv_data) + "\n") return def flag_last_response(state, model_selector, request: gr.Request): logger.info(f"flag. ip: {request.client.host}") vote_last_response(state, "flag", model_selector, request) textbox = gr.MultimodalTextbox(value=None, interactive=True) return (textbox,) + (disable_btn,) * 3 def regenerate(state, image_process_mode, request: gr.Request): logger.info(f"regenerate. ip: {request.client.host}") # state.messages[-1][-1] = None state.update_message(Conversation.ASSISTANT, None, -1) prev_human_msg = state.messages[-2] if type(prev_human_msg[1]) in (tuple, list): prev_human_msg[1] = (*prev_human_msg[1][:2], image_process_mode) state.skip_next = False textbox = gr.MultimodalTextbox(value=None, interactive=True) return (state, state.to_gradio_chatbot(), textbox) + (disable_btn,) * 5 def clear_history(request: gr.Request): logger.info(f"clear_history. ip: {request.client.host}") state = init_state() textbox = gr.MultimodalTextbox(value=None, interactive=True) return (state, state.to_gradio_chatbot(), textbox) + (disable_btn,) * 5 def change_system_prompt(state, system_prompt, request: gr.Request): logger.info(f"Change system prompt. ip: {request.client.host}") state.set_system_message(system_prompt) return state def add_text(state, message, system_prompt, request: gr.Request): images = message.get("files", []) text = message.get("text", "").strip() logger.info(f"add_text. ip: {request.client.host}. len: {len(text)}") # import pdb; pdb.set_trace() textbox = gr.MultimodalTextbox(value=None, interactive=False) if len(text) <= 0 and len(images) == 0: state.skip_next = True return (state, state.to_gradio_chatbot(), textbox) + (no_change_btn,) * 5 if args.moderate: flagged = violates_moderation(text) if flagged: state.skip_next = True textbox = gr.MultimodalTextbox( value={"text": moderation_msg}, interactive=True ) return (state, state.to_gradio_chatbot(), textbox) + (no_change_btn,) * 5 images = [Image.open(path).convert("RGB") for path in images] if len(images) > 0 and len(state.get_images(source=state.USER)) > 0: state = init_state(state) state.set_system_message(system_prompt) state.append_message(Conversation.USER, text, images) state.skip_next = False return (state, state.to_gradio_chatbot(), textbox) + (disable_btn,) * 5 def http_bot( state, model_selector, temperature, top_p, repetition_penalty, max_new_tokens, max_input_tiles, # bbox_threshold, # mask_threshold, request: gr.Request, ): logger.info(f"http_bot. ip: {request.client.host}") start_tstamp = time.time() model_name = model_selector if hasattr(state, "skip_next") and state.skip_next: # This generate call is skipped due to invalid inputs yield ( state, state.to_gradio_chatbot(), gr.MultimodalTextbox(interactive=False), ) + (no_change_btn,) * 5 return # Query worker address controller_url = args.controller_url ret = requests.post( controller_url + "/get_worker_address", json={"model": model_name} ) worker_addr = ret.json()["address"] logger.info(f"model_name: {model_name}, worker_addr: {worker_addr}") # No available worker if worker_addr == "": # state.messages[-1][-1] = server_error_msg state.update_message(Conversation.ASSISTANT, server_error_msg) yield ( state, state.to_gradio_chatbot(), gr.MultimodalTextbox(interactive=False), disable_btn, disable_btn, disable_btn, enable_btn, enable_btn, ) return all_images = state.get_images(source=state.USER) all_image_paths = [state.save_image(image) for image in all_images] # Make requests pload = { "model": model_name, "prompt": state.get_prompt(), "temperature": float(temperature), "top_p": float(top_p), "max_new_tokens": max_new_tokens, "max_input_tiles": max_input_tiles, # "bbox_threshold": bbox_threshold, # "mask_threshold": mask_threshold, "repetition_penalty": repetition_penalty, "images": f"List of {len(all_images)} images: {all_image_paths}", } logger.info(f"==== request ====\n{pload}") pload.pop("images") pload["prompt"] = state.get_prompt(inlude_image=True) state.append_message(Conversation.ASSISTANT, state.streaming_placeholder) yield ( state, state.to_gradio_chatbot(), gr.MultimodalTextbox(interactive=False), ) + (disable_btn,) * 5 try: # Stream output response = requests.post( worker_addr + "/worker_generate_stream", headers=headers, json=pload, stream=True, timeout=20, ) for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"): if chunk: data = json.loads(chunk.decode()) if data["error_code"] == 0: if "text" in data: output = data["text"].strip() output += state.streaming_placeholder image = None if "image" in data: image = load_image_from_base64(data["image"]) _ = state.save_image(image) state.update_message(Conversation.ASSISTANT, output, image) yield ( state, state.to_gradio_chatbot(), gr.MultimodalTextbox(interactive=False), ) + (disable_btn,) * 5 else: output = ( f"**{data['text']}**" + f" (error_code: {data['error_code']})" ) state.update_message(Conversation.ASSISTANT, output, None) yield ( state, state.to_gradio_chatbot(), gr.MultimodalTextbox(interactive=True), ) + ( disable_btn, disable_btn, disable_btn, enable_btn, enable_btn, ) return except requests.exceptions.RequestException as e: state.update_message(Conversation.ASSISTANT, server_error_msg, None) yield ( state, state.to_gradio_chatbot(), gr.MultimodalTextbox(interactive=True), ) + ( disable_btn, disable_btn, disable_btn, enable_btn, enable_btn, ) return ai_response = state.return_last_message() if "" in ai_response: returned_image = find_bounding_boxes(state, ai_response) returned_image = [returned_image] if returned_image else [] state.update_message(Conversation.ASSISTANT, ai_response, returned_image) if "```drawing-instruction" in ai_response: returned_image = query_image_generation( ai_response, sd_worker_url=sd_worker_url ) returned_image = [returned_image] if returned_image else [] state.update_message(Conversation.ASSISTANT, ai_response, returned_image) state.end_of_current_turn() yield ( state, state.to_gradio_chatbot(), gr.MultimodalTextbox(interactive=True), ) + (enable_btn,) * 5 finish_tstamp = time.time() logger.info(f"{output}") data = { "tstamp": round(finish_tstamp, 4), "like": None, "model": model_name, "start": round(start_tstamp, 4), "finish": round(start_tstamp, 4), "state": state.dict(), "images": all_image_paths, "ip": request.client.host, } write2file(get_log_filename(), json.dumps(data) + "\n") title_html = """

InternVL2: Better than the Best—Expanding Performance Boundaries of Open-Source Multimodal Models with the Progressive Scaling Strategy

[📜 InternVL2 Blog] [🤗 HF Demo] [🚀 Quick Start] [🌐 API] """ tos_markdown = """ ### 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 = """ ### 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. ### Acknowledgement This demo is modified from LLaVA's demo. Thanks for their awesome work! """ # .gradio-container {margin: 5px 10px 0 10px !important}; block_css = """ .gradio-container {margin: 0.1% 1% 0 1% !important; max-width: 98% !important;}; #buttons button { min-width: min(120px,100%); } .gradient-text { font-size: 28px; width: auto; font-weight: bold; background: linear-gradient(45deg, red, orange, yellow, green, blue, indigo, violet); background-clip: text; -webkit-background-clip: text; color: transparent; } .plain-text { font-size: 22px; width: auto; font-weight: bold; } """ js = """ function createWaveAnimation() { const text = document.getElementById('text'); var i = 0; setInterval(function() { const colors = [ 'red, orange, yellow, green, blue, indigo, violet, purple', 'orange, yellow, green, blue, indigo, violet, purple, red', 'yellow, green, blue, indigo, violet, purple, red, orange', 'green, blue, indigo, violet, purple, red, orange, yellow', 'blue, indigo, violet, purple, red, orange, yellow, green', 'indigo, violet, purple, red, orange, yellow, green, blue', 'violet, purple, red, orange, yellow, green, blue, indigo', 'purple, red, orange, yellow, green, blue, indigo, violet', ]; const angle = 45; const colorIndex = i % colors.length; text.style.background = `linear-gradient(${angle}deg, ${colors[colorIndex]})`; text.style.webkitBackgroundClip = 'text'; text.style.backgroundClip = 'text'; text.style.color = 'transparent'; text.style.fontSize = '28px'; text.style.width = 'auto'; text.textContent = 'InternVL2'; text.style.fontWeight = 'bold'; i += 1; }, 200); const params = new URLSearchParams(window.location.search); url_params = Object.fromEntries(params); console.log(url_params); return url_params; } """ def build_demo(embed_mode): textbox = gr.MultimodalTextbox( interactive=True, file_types=["image", "video"], placeholder="Enter message or upload file...", show_label=False, ) with gr.Blocks( title="InternVL-Chat", theme=gr.themes.Default(), css=block_css, ) as demo: state = gr.State() if not embed_mode: # gr.Markdown(title_markdown) gr.HTML(title_html) with gr.Row(): with gr.Column(scale=2): with gr.Row(elem_id="model_selector_row"): model_selector = gr.Dropdown( choices=models, value=models[0] if len(models) > 0 else "", # value="InternVL-Chat-V1-5", interactive=True, show_label=False, container=False, ) with gr.Accordion("System Prompt", open=False) as system_prompt_row: system_prompt = gr.Textbox( value="请尽可能详细地回答用户的问题。", label="System Prompt", interactive=True, ) with gr.Accordion("Parameters", open=False) as parameter_row: temperature = gr.Slider( minimum=0.0, maximum=1.0, value=0.2, step=0.1, interactive=True, label="Temperature", ) top_p = gr.Slider( minimum=0.0, maximum=1.0, value=0.7, step=0.1, interactive=True, label="Top P", ) repetition_penalty = gr.Slider( minimum=1.0, maximum=1.5, value=1.1, step=0.02, interactive=True, label="Repetition penalty", ) max_output_tokens = gr.Slider( minimum=0, maximum=4096, value=1024, step=64, interactive=True, label="Max output tokens", ) max_input_tiles = gr.Slider( minimum=1, maximum=32, value=12, step=1, interactive=True, label="Max input tiles (control the image size)", ) examples = gr.Examples( examples=[ [ { "files": [ "gallery/prod_9.jpg", ], "text": "What's at the far end of the image?", } ], [ { "files": [ "gallery/astro_on_unicorn.png", ], "text": "What does this image mean?", } ], [ { "files": [ "gallery/prod_12.png", ], "text": "What are the consequences of the easy decisions shown in this image?", } ], [ { "files": [ "gallery/child_1.jpg", "gallery/child_2.jpg", f"gallery/child_3.jpg", ], "text": "这三帧图片讲述了一件什么事情？", } ], ], inputs=[textbox], ) with gr.Column(scale=8): chatbot = gr.Chatbot( elem_id="chatbot", label="InternVL2", height=580, show_copy_button=True, show_share_button=True, avatar_images=[ "assets/human.png", "assets/assistant.png", ], bubble_full_width=False, ) with gr.Row(): with gr.Column(scale=8): textbox.render() with gr.Column(scale=1, min_width=50): submit_btn = gr.Button(value="Send", variant="primary") with gr.Row(elem_id="buttons") as button_row: upvote_btn = gr.Button(value="👍 Upvote", interactive=False) downvote_btn = gr.Button(value="👎 Downvote", interactive=False) flag_btn = gr.Button(value="⚠️ Flag", interactive=False) # stop_btn = gr.Button(value="⏹️ Stop Generation", interactive=False) regenerate_btn = gr.Button( value="🔄 Regenerate", interactive=False ) clear_btn = gr.Button(value="🗑️ Clear", interactive=False) if not embed_mode: gr.Markdown(tos_markdown) gr.Markdown(learn_more_markdown) url_params = gr.JSON(visible=False) # Register listeners btn_list = [upvote_btn, downvote_btn, flag_btn, regenerate_btn, clear_btn] upvote_btn.click( upvote_last_response, [state, model_selector], [textbox, upvote_btn, downvote_btn, flag_btn], ) downvote_btn.click( downvote_last_response, [state, model_selector], [textbox, upvote_btn, downvote_btn, flag_btn], ) chatbot.like( vote_selected_response, [state, model_selector], [], ) flag_btn.click( flag_last_response, [state, model_selector], [textbox, upvote_btn, downvote_btn, flag_btn], ) regenerate_btn.click( regenerate, [state, system_prompt], [state, chatbot, textbox] + btn_list, ).then( http_bot, [ state, model_selector, temperature, top_p, repetition_penalty, max_output_tokens, max_input_tiles, # bbox_threshold, # mask_threshold, ], [state, chatbot, textbox] + btn_list, ) clear_btn.click(clear_history, None, [state, chatbot, textbox] + btn_list) textbox.submit( add_text, [state, textbox, system_prompt], [state, chatbot, textbox] + btn_list, ).then( http_bot, [ state, model_selector, temperature, top_p, repetition_penalty, max_output_tokens, max_input_tiles, # bbox_threshold, # mask_threshold, ], [state, chatbot, textbox] + btn_list, ) submit_btn.click( add_text, [state, textbox, system_prompt], [state, chatbot, textbox] + btn_list, ).then( http_bot, [ state, model_selector, temperature, top_p, repetition_penalty, max_output_tokens, max_input_tiles, # bbox_threshold, # mask_threshold, ], [state, chatbot, textbox] + btn_list, ) if args.model_list_mode == "once": demo.load( load_demo, [url_params], [state, model_selector], js=js, ) elif args.model_list_mode == "reload": demo.load( load_demo_refresh_model_list, None, [state, model_selector], js=js, ) else: raise ValueError(f"Unknown model list mode: {args.model_list_mode}") return demo if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--host", type=str, default="0.0.0.0") parser.add_argument("--port", type=int, default=11000) parser.add_argument("--controller-url", type=str, default="http://localhost:21001") parser.add_argument("--concurrency-count", type=int, default=10) parser.add_argument( "--model-list-mode", type=str, default="once", choices=["once", "reload"] ) parser.add_argument("--sd-worker-url", type=str, default=None) parser.add_argument("--share", action="store_true") parser.add_argument("--moderate", action="store_true") parser.add_argument("--embed", action="store_true") args = parser.parse_args() logger.info(f"args: {args}") models = get_model_list() sd_worker_url = args.sd_worker_url logger.info(args) demo = build_demo(args.embed) demo.queue(api_open=False).launch( server_name=args.host, server_port=args.port, share=args.share, max_threads=args.concurrency_count, )