import argparse import datetime import json import os import time import gradio as gr import requests from mplug_docowl.conversation import (default_conversation, conv_templates, SeparatorStyle) from mplug_docowl.constants import LOGDIR from mplug_docowl.utils import (build_logger, server_error_msg, violates_moderation, moderation_msg) from model_worker import ModelWorker import hashlib from huggingface_hub import snapshot_download model_dir = snapshot_download('mPLUG/DocOwl1.5-Omni', cache_dir='./') print(os.listdir('./')) print(os.system('ls ./mPLUG/DocOwl1.5-Omni')) print(os.system('ls ./model/mPLUG/DocOwl1.5-Omni')) print(os.system('ls ./models--mPLUG--DocOwl1.5-Omni')) logger = build_logger("gradio_web_server_local", "gradio_web_server_local.log") headers = {"User-Agent": "mPLUG-DocOwl1.5 Client"} no_change_btn = gr.Button() enable_btn = gr.Button(interactive=True) disable_btn = gr.Button(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 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}") state = default_conversation.copy() return state def vote_last_response(state, vote_type, request: gr.Request): with open(get_conv_log_filename(), "a") as fout: data = { "tstamp": round(time.time(), 4), "type": vote_type, "state": state.dict(), "ip": request.client.host, } fout.write(json.dumps(data) + "\n") def upvote_last_response(state, request: gr.Request): logger.info(f"upvote. ip: {request.client.host}") vote_last_response(state, "upvote", request) return ("",) + (disable_btn,) * 3 def downvote_last_response(state, request: gr.Request): logger.info(f"downvote. ip: {request.client.host}") vote_last_response(state, "downvote", request) return ("",) + (disable_btn,) * 3 def flag_last_response(state, request: gr.Request): logger.info(f"flag. ip: {request.client.host}") vote_last_response(state, "flag", request) return ("",) + (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 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[:3584] # Hard cut-off if image is not None: text = text[:3500] # Hard cut-off for images if '<|image|>' not in text: text = '<|image|>' + text 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, temperature, top_p, max_new_tokens, request: gr.Request): logger.info(f"http_bot. ip: {request.client.host}") start_tstamp = time.time() 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 template_name = "mplug_owl2" new_state = conv_templates[template_name].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 # 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, 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"{hash}.jpg") if not os.path.isfile(filename): os.makedirs(os.path.dirname(filename), exist_ok=True) image.save(filename) # Make requests pload = { "prompt": prompt, "temperature": float(temperature), "top_p": float(top_p), "max_new_tokens": min(int(max_new_tokens), 2048), "stop": state.sep if state.sep_style in [SeparatorStyle.SINGLE, SeparatorStyle.MPT] else state.sep2, "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"): response = model.generate_stream_gate(pload) for chunk in response: 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 as e: 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", "start": round(start_tstamp, 4), "finish": round(start_tstamp, 4), "state": state.dict(), "images": all_image_hash, "ip": request.client.host, } fout.write(json.dumps(data) + "\n") title_markdown = ("""

mPLUG-DocOwl

mPLUG-DocOwl1.5: Unified Stucture Learning for OCR-free Document Understanding

If you like our project, please give us a star ✨ on Github for latest update.
Note: This demo is temporarily only supported for English Document Understanding. The Chinese-and-English model is under development.
注意: 当前Demo只支持英文文档理解, 中英模型正在全力开发中。
Note: If you want a detailed explanation, please remember to add a prompot "Give a detailed explanation." after the question.
注意: 如果你想要详细的推理解释, 请在问题后面加上“Give a detailed explanation.”。
""") 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. """) block_css = """ #buttons button { min-width: min(120px,100%); } """ placeholder = """ **mPLUG-DocOwl1.5: Unified Stucture Learning for OCR-free Document Understanding** """ def build_demo(embed_mode): textbox = gr.Textbox(show_label=False, placeholder="Enter text and press ENTER", container=False) with gr.Blocks(title="mPLUG-Owl2", theme=gr.themes.Default(), css=block_css) as demo: state = gr.State() if not embed_mode: gr.Markdown(title_markdown) with gr.Row(): with gr.Column(scale=3): 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/cvpr.png", "what is this schedule for? Give detailed explanation."], [f"{cur_dir}/examples/fflw0023_1.png", "Parse texts in the image."], [f"{cur_dir}/examples/col_type_46452.jpg", "Convert the table into Markdown format."], [f"{cur_dir}/examples/col_type_177029.jpg", "What is unusual about this image? Provide detailed explanation."], [f"{cur_dir}/examples/multi_col_60204.png", "Convert the illustration into Markdown language."], [f"{cur_dir}/examples/Rebecca_(1939_poster)_Small.jpeg", "What is the name of the movie in the poster? Provide detailed explanation."], [f"{cur_dir}/examples/extreme_ironing.jpg", "What is unusual about this image? Provide detailed explanation."], ], inputs=[imagebox, textbox]) with gr.Accordion("Parameters", open=True) as parameter_row: temperature = gr.Slider(minimum=0.0, maximum=1.0, value=1.0, 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",) max_output_tokens = gr.Slider(minimum=0, maximum=1024, value=512, step=64, interactive=True, label="Max output tokens",) with gr.Column(scale=8): chatbot = gr.Chatbot(elem_id="Chatbot", label="mPLUG-DocOwl1.5 Chatbot", height=600, placeholder=placeholder) 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, [textbox, upvote_btn, downvote_btn, flag_btn], queue=False, concurrency_limit=10, ) downvote_btn.click( downvote_last_response, state, [textbox, upvote_btn, downvote_btn, flag_btn], queue=False, concurrency_limit=10, ) flag_btn.click( flag_last_response, state, [textbox, upvote_btn, downvote_btn, flag_btn], queue=False, concurrency_limit=10, ) regenerate_btn.click( regenerate, [state, image_process_mode], [state, chatbot, textbox, imagebox] + btn_list, queue=False, concurrency_limit=10, ).then( http_bot, [state, temperature, top_p, max_output_tokens], [state, chatbot] + btn_list ) clear_btn.click( clear_history, None, [state, chatbot, textbox, imagebox] + btn_list, queue=False, concurrency_limit=10, ) textbox.submit( add_text, [state, textbox, imagebox, image_process_mode], [state, chatbot, textbox, imagebox] + btn_list, queue=False ).then( http_bot, [state, temperature, top_p, 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, concurrency_limit=10, ).then( http_bot, [state, temperature, top_p, max_output_tokens], [state, chatbot] + btn_list ) demo.load( load_demo, [url_params], state, js=get_window_url_params, queue=False ) 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("--concurrency-count", type=int, default=10) parser.add_argument("--model-list-mode", type=str, default="once", choices=["once", "reload"]) parser.add_argument("--model-path", type=str, default="mPLUG/DocOwl1.5-Omni") parser.add_argument("--device", type=str, default="cuda:0") parser.add_argument("--load-8bit", action="store_true") parser.add_argument("--load-4bit", 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}") model = ModelWorker(args.model_path, None, None, resolution=448, anchors='grid_9', add_global_img=True, load_8bit=args.load_8bit, load_4bit=args.load_4bit, device=args.device) logger.info(args) demo = build_demo(args.embed) demo.queue( api_open=False ).launch( server_name=args.host, server_port=args.port, share=False )