import argparse import datetime import json import os import time from PIL import Image import gradio as gr import requests from llava.conversation import (default_conversation, conv_templates, SeparatorStyle) from llava.constants import LOGDIR from llava.utils import (build_logger, server_error_msg, violates_moderation, moderation_msg) import hashlib logger = build_logger("gradio_web_server", "gradio_web_server.log") headers = {"User-Agent": "LLaVA Client"} no_change_btn = gr.Button.update() enable_btn = gr.Button.update(interactive=True) disable_btn = gr.Button.update(interactive=False) priority = { "vicuna-13b": "aaaaaaa", "koala-13b": "aaaaaab", } 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.sort(key=lambda x: priority.get(x, x)) 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 upvote_last_response(state, model_selector, request: gr.Request): logger.info(f"upvote. ip: {request.client.host}") vote_last_response(state, "upvote", model_selector, request) return ("",) + (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, "downvote", model_selector, request) return ("",) + (disable_btn,) * 3 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) 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[: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 batch_process_images(folder_path, textbox, model_selector, temperature, top_p, max_output_tokens, request: gr.Request): print("calling batch_process_images") for filename in os.listdir(folder_path): if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif')): image_path = os.path.join(folder_path, filename) with Image.open(image_path) as image: state = default_conversation.copy() state, _, _, _, _, _, _, _, _ = add_text(state, textbox, image, "Default", request) # Call http_bot and iterate over the generator response_text = "" for state_update in http_bot(state, model_selector, temperature, top_p, max_output_tokens, request): # Update state and extract response text state, chatbot_output, *_ = state_update response_text = chatbot_output # Save the final response to a file try: with open(os.path.splitext(image_path)[0] + '.txt', 'w') as f: f.write(response_text[0][1]) except Exception as e: print(f"An error occurred: {e}") return "Batch processing completed." def http_bot(state, model_selector, temperature, top_p, max_new_tokens, request: gr.Request): logger.info(f"http_bot. ip: {request.client.host}") print(f"model_selector {model_selector}") start_tstamp = time.time() model_name = model_selector if state.skip_next: # This generate call is skipped due to invalid inputs print("invalid input state.skip_next") yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5 return if len(state.messages) == state.offset + 2: # First round of conversation if "llava" in model_name.lower(): if 'llama-2' in model_name.lower(): template_name = "llava_llama_2" elif "v1" in model_name.lower(): if 'mmtag' in model_name.lower(): template_name = "v1_mmtag" elif 'plain' in model_name.lower() and 'finetune' not in model_name.lower(): template_name = "v1_mmtag" else: template_name = "llava_v1" elif "mpt" in model_name.lower(): template_name = "mpt" else: if 'mmtag' in model_name.lower(): template_name = "v0_mmtag" elif 'plain' in model_name.lower() and 'finetune' not in model_name.lower(): template_name = "v0_mmtag" else: template_name = "llava_v0" elif "mpt" in model_name: template_name = "mpt_text" elif "llama-2" in model_name: template_name = "llama_2" else: template_name = "vicuna_v1" print(f"template_name {template_name}") 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 # 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 print(f"error No available worker") 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, 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 = { "model": model_name, "prompt": prompt, "temperature": float(temperature), "top_p": float(top_p), "max_new_tokens": min(int(max_new_tokens), 1536), "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 print(f"entering Stream output") 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 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", "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 = (""" Most Up To Date Scripts On : https://www.patreon.com/posts/sota-very-best-90744385 \n Original Project : https://llava-vl.github.io """) 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%); } """ def build_demo(embed_mode): textbox = gr.Textbox(show_label=False, placeholder="Enter text and press ENTER", container=False) # New components for batch processing folder_input = gr.Textbox(label="Enter Folder Path for Batch Processing") batch_btn = gr.Button("Batch Process") with gr.Blocks(title="LLaVA", 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): 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/extreme_ironing.jpg", "just caption the image with details, colors, items, objects, emotions, art style, drawing style and objects but do not add any description or comment. do not miss any item in the given image"], ], inputs=[imagebox, textbox]) 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",) 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="LLaVA 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="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) regenerate_btn = gr.Button(value="🔄 Regenerate", interactive=False) clear_btn = gr.Button(value="🗑️ Clear", interactive=False) url_params = gr.JSON(visible=False) # Add new components for batch processing with gr.Row(): folder_input.render() batch_btn.render() # Batch processing button event batch_btn.click( batch_process_images, # This function needs to be defined to handle batch processing inputs=[folder_input, textbox, model_selector , temperature, top_p, max_output_tokens], outputs=[] ) # 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], queue=False ) downvote_btn.click( downvote_last_response, [state, model_selector], [textbox, upvote_btn, downvote_btn, flag_btn], queue=False ) flag_btn.click( flag_last_response, [state, model_selector], [textbox, upvote_btn, downvote_btn, flag_btn], queue=False ) regenerate_btn.click( regenerate, [state, image_process_mode], [state, chatbot, textbox, imagebox] + btn_list, queue=False ).then( http_bot, [state, model_selector, temperature, top_p, 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, 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 ).then( http_bot, [state, model_selector, temperature, top_p, 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:10000") parser.add_argument("--concurrency-count", type=int, default=10) parser.add_argument("--model-list-mode", type=str, default="reload", 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 )