""" gradio_web_server.py Entry point for all VLM-Bench interactive demos; specify model and get a gradio UI where you can chat with it! This file is copied from the script used to define the gradio web server in the LLaVa codebase: https://github.com/haotian-liu/LLaVA/blob/main/llava/serve/gradio_web_server.py with only very minor modifications. """ import argparse import datetime import hashlib import json import os import time import gradio as gr import requests from llava.constants import LOGDIR from llava.conversation import conv_templates, default_conversation from llava.utils import build_logger, moderation_msg, server_error_msg, violates_moderation from serve import INTERACTION_MODES_MAP, MODEL_ID_TO_NAME logger = build_logger("gradio_web_server", "gradio_web_server.log") headers = {"User-Agent": "PrismaticVLMs Client"} no_change_btn = gr.Button.update() enable_btn = gr.Button.update(interactive=True) disable_btn = gr.Button.update(interactive=False) def get_conv_log_filename(): t = datetime.datetime.now() name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json") return name 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 = sorted( models, key=lambda x: list(MODEL_ID_TO_NAME.values()).index(x) if x in MODEL_ID_TO_NAME.values() else len(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 load_demo(url_params, request: gr.Request): logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}") dropdown_update = gr.Dropdown.update(visible=True) if "model" in url_params: model = url_params["model"] if model in models: dropdown_update = gr.Dropdown.update(value=model, visible=True) state = default_conversation.copy() 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 = default_conversation.copy() dropdown_update = gr.Dropdown.update(choices=models, value=models[0] if len(models) > 0 else "") return state, dropdown_update def vote_last_response(state, vote_type, model_selector, request: gr.Request): with open(get_conv_log_filename(), "a") as fout: data = { "tstamp": round(time.time(), 4), "type": vote_type, "model": model_selector, "state": state.dict(), "ip": request.client.host, } fout.write(json.dumps(data) + "\n") def regenerate(state, image_process_mode, request: gr.Request): logger.info(f"regenerate. ip: {request.client.host}") state.messages[-1][-1] = None 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 return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5 def clear_history(request: gr.Request): logger.info(f"clear_history. ip: {request.client.host}") state = default_conversation.copy() return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5 def add_text(state, text, image, image_process_mode, request: gr.Request): logger.info(f"add_text. ip: {request.client.host}. len: {len(text)}") if len(text) <= 0 and image is None: state.skip_next = True return (state, state.to_gradio_chatbot(), "", None) + (no_change_btn,) * 5 if args.moderate: flagged = violates_moderation(text) if flagged: state.skip_next = True return (state, state.to_gradio_chatbot(), moderation_msg, None) + (no_change_btn,) * 5 text = text[:1536] # Hard cut-off if image is not None: text = text[:1200] # Hard cut-off for images if "" not in text: # text = '' + text text = text + "\n" text = (text, image, image_process_mode) if len(state.get_images(return_pil=True)) > 0: state = default_conversation.copy() state.append_message(state.roles[0], text) state.append_message(state.roles[1], None) state.skip_next = False return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5 def http_bot(state, model_selector, interaction_mode, temperature, max_new_tokens, request: gr.Request): logger.info(f"http_bot. ip: {request.client.host}") start_tstamp = time.time() model_name = model_selector if state.skip_next: # This generate call is skipped due to invalid inputs yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5 return if len(state.messages) == state.offset + 2: # First round of conversation # (Note): Hardcoding llava_v1 conv template for now new_state = conv_templates["llava_v1"].copy() new_state.append_message(new_state.roles[0], state.messages[-2][1]) new_state.append_message(new_state.roles[1], None) state = new_state # 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 yield (state, state.to_gradio_chatbot(), disable_btn, disable_btn, disable_btn, enable_btn, enable_btn) return # Construct prompt prompt = state.get_prompt() all_images = state.get_images(return_pil=True) all_image_hash = [hashlib.md5(image.tobytes()).hexdigest() for image in all_images] for image, im_hash in zip(all_images, all_image_hash): t = datetime.datetime.now() filename = os.path.join(LOGDIR, "serve_images", f"{t.year}-{t.month:02d}-{t.day:02d}", f"{im_hash}.jpg") if not os.path.isfile(filename): os.makedirs(os.path.dirname(filename), exist_ok=True) image.save(filename) # Make requests pload = { "model": model_name, "prompt": prompt, "interaction_mode": interaction_mode, "temperature": float(temperature), "max_new_tokens": int(max_new_tokens), "images": f"List of {len(state.get_images())} images: {all_image_hash}", } logger.info(f"==== request ====\n{pload}") pload["images"] = state.get_images() state.messages[-1][-1] = "▌" yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5 try: # Stream output response = requests.post( worker_addr + "/worker_generate_stream", headers=headers, json=pload, stream=True, timeout=10 ) for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"): if chunk: data = json.loads(chunk.decode()) if data["error_code"] == 0: output = data["text"][len(prompt) :].strip() state.messages[-1][-1] = output + "▌" yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5 else: output = data["text"] + f" (error_code: {data['error_code']})" state.messages[-1][-1] = output yield (state, state.to_gradio_chatbot()) + ( disable_btn, disable_btn, disable_btn, enable_btn, enable_btn, ) return time.sleep(0.03) except requests.exceptions.RequestException: state.messages[-1][-1] = server_error_msg yield (state, state.to_gradio_chatbot()) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn) return state.messages[-1][-1] = state.messages[-1][-1][:-1] yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5 finish_tstamp = time.time() logger.info(f"{output}") with open(get_conv_log_filename(), "a") as fout: data = { "tstamp": round(finish_tstamp, 4), "type": "chat", "model": model_name, "start": round(start_tstamp, 4), "finish": round(finish_tstamp, 4), "state": state.dict(), "images": all_image_hash, "ip": request.client.host, } fout.write(json.dumps(data) + "\n") title_markdown = """ # Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models [[Project Page](TODO)] [[Code](TODO)] [[Models](TODO)] | 📚 [[Paper](TODO)] """ 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. This website is heavily inspired by the website released by [LLaVA](https://github.com/haotian-liu/LLaVA). """ 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. """ block_css = """ #buttons button { min-width: min(120px,100%); } """ def build_demo(embed_mode): textbox = gr.Textbox(show_label=False, placeholder="Enter text and press ENTER", container=False) with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="stone")) as demo: state = gr.State() if not embed_mode: gr.Markdown(title_markdown) with gr.Row(): with gr.Column(scale=3): with gr.Row(elem_id="model_selector_row"): model_selector = gr.Dropdown( choices=models, value=models[0] if len(models) > 0 else "", interactive=True, show_label=False, container=False, ) imagebox = gr.Image(type="pil") image_process_mode = gr.Radio( ["Crop", "Resize", "Pad", "Default"], value="Default", label="Preprocess for non-square image", visible=False, ) cur_dir = os.path.dirname(os.path.abspath(__file__)) gr.Examples( examples=[ [f"{cur_dir}/examples/cows_in_pasture.png", "How many cows are in this image?"], [ f"{cur_dir}/examples/monkey_knives.png", "What is happening in this image?", ], ], inputs=[imagebox, textbox], ) with gr.Accordion("Parameters", open=False): temperature = gr.Slider( minimum=0.0, maximum=1.0, value=0.2, step=0.1, interactive=True, label="Temperature", ) max_output_tokens = gr.Slider( minimum=0, maximum=4096, value=2048, step=64, interactive=True, label="Max output tokens", ) with gr.Accordion("Interaction Mode", open=False): interaction_modes = list(INTERACTION_MODES_MAP.keys()) interaction_mode = gr.Dropdown( choices=interaction_modes, value=interaction_modes[0] if len(interaction_modes) > 0 else "Chat", interactive=True, show_label=False, container=False, ) with gr.Column(scale=8): chatbot = gr.Chatbot(elem_id="chatbot", label="PrismaticVLMs Chatbot", height=550) with gr.Row(): with gr.Column(scale=8): textbox.render() with gr.Column(scale=1, min_width=50): submit_btn = gr.Button(value="Generate", variant="primary") with gr.Row(elem_id="buttons"): # upvote_btn = gr.Button(value="👍 Upvote", interactive=False) # downvote_btn = gr.Button(value="👎 Downvote", interactive=False) # flag_btn = gr.Button(value="⚠ī¸ Flag", interactive=False) # stop_btn = gr.Button(value="⏚ī¸ Stop Generation", interactive=False) regenerate_btn = gr.Button(value="🔄 Regenerate", interactive=False) clear_btn = gr.Button(value="🗑ī¸ 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 = [regenerate_btn, clear_btn] regenerate_btn.click( regenerate, [state, image_process_mode], [state, chatbot, textbox, imagebox, *btn_list], queue=False ).then( http_bot, [state, model_selector, interaction_mode, temperature, max_output_tokens], [state, chatbot, *btn_list], ) clear_btn.click(clear_history, None, [state, chatbot, textbox, imagebox, *btn_list], queue=False) textbox.submit( add_text, [state, textbox, imagebox, image_process_mode], [state, chatbot, textbox, imagebox, *btn_list], queue=False, ).then( http_bot, [state, model_selector, interaction_mode, temperature, max_output_tokens], [state, chatbot, *btn_list], ) submit_btn.click( add_text, [state, textbox, imagebox, image_process_mode], [state, chatbot, textbox, imagebox, *btn_list], queue=False, ).then( http_bot, [state, model_selector, interaction_mode, temperature, max_output_tokens], [state, chatbot, *btn_list], ) if args.model_list_mode == "once": demo.load(load_demo, [url_params], [state, model_selector], _js=get_window_url_params, queue=False) elif args.model_list_mode == "reload": demo.load(load_demo_refresh_model_list, None, [state, model_selector], queue=False) 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) 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("--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() logger.info(args) demo = build_demo(args.embed) demo.queue(concurrency_count=args.concurrency_count, api_open=False).launch( server_name=args.host, server_port=args.port, share=args.share )