420 lines
17 KiB
Python
420 lines
17 KiB
Python
import argparse
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import datetime
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import json
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import os
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import time
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import gradio as gr
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import requests
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from llava.conversation import (default_conversation, conv_templates,
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SeparatorStyle)
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from llava.constants import LOGDIR
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from llava.utils import (build_logger, server_error_msg,
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violates_moderation, moderation_msg)
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import hashlib
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logger = build_logger("gradio_web_server", "gradio_web_server.log")
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headers = {"User-Agent": "LLaVA Client"}
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no_change_btn = gr.Button.update()
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enable_btn = gr.Button.update(interactive=True)
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disable_btn = gr.Button.update(interactive=False)
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priority = {
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"vicuna-13b": "aaaaaaa",
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"koala-13b": "aaaaaab",
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}
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def get_conv_log_filename():
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t = datetime.datetime.now()
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name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json")
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return name
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def get_model_list():
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ret = requests.post(args.controller_url + "/refresh_all_workers")
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assert ret.status_code == 200
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ret = requests.post(args.controller_url + "/list_models")
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models = ret.json()["models"]
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models.sort(key=lambda x: priority.get(x, x))
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logger.info(f"Models: {models}")
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return models
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get_window_url_params = """
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function() {
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const params = new URLSearchParams(window.location.search);
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url_params = Object.fromEntries(params);
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console.log(url_params);
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return url_params;
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}
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"""
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def load_demo(url_params, request: gr.Request):
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logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}")
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dropdown_update = gr.Dropdown.update(visible=True)
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if "model" in url_params:
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model = url_params["model"]
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if model in models:
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dropdown_update = gr.Dropdown.update(
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value=model, visible=True)
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state = default_conversation.copy()
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return state, dropdown_update
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def load_demo_refresh_model_list(request: gr.Request):
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logger.info(f"load_demo. ip: {request.client.host}")
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models = get_model_list()
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state = default_conversation.copy()
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dropdown_update = gr.Dropdown.update(
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choices=models,
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value=models[0] if len(models) > 0 else ""
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)
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return state, dropdown_update
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def vote_last_response(state, vote_type, model_selector, request: gr.Request):
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with open(get_conv_log_filename(), "a") as fout:
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data = {
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"tstamp": round(time.time(), 4),
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"type": vote_type,
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"model": model_selector,
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"state": state.dict(),
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"ip": request.client.host,
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}
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fout.write(json.dumps(data) + "\n")
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def upvote_last_response(state, model_selector, request: gr.Request):
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logger.info(f"upvote. ip: {request.client.host}")
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vote_last_response(state, "upvote", model_selector, request)
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return ("",) + (disable_btn,) * 3
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def downvote_last_response(state, model_selector, request: gr.Request):
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logger.info(f"downvote. ip: {request.client.host}")
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vote_last_response(state, "downvote", model_selector, request)
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return ("",) + (disable_btn,) * 3
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def flag_last_response(state, model_selector, request: gr.Request):
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logger.info(f"flag. ip: {request.client.host}")
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vote_last_response(state, "flag", model_selector, request)
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return ("",) + (disable_btn,) * 3
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def regenerate(state, image_process_mode, request: gr.Request):
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logger.info(f"regenerate. ip: {request.client.host}")
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state.messages[-1][-1] = None
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prev_human_msg = state.messages[-2]
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if type(prev_human_msg[1]) in (tuple, list):
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prev_human_msg[1] = (*prev_human_msg[1][:2], image_process_mode)
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state.skip_next = False
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return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5
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def clear_history(request: gr.Request):
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logger.info(f"clear_history. ip: {request.client.host}")
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state = default_conversation.copy()
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return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5
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def add_text(state, text, image, image_process_mode, request: gr.Request):
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logger.info(f"add_text. ip: {request.client.host}. len: {len(text)}")
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if len(text) <= 0 and image is None:
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state.skip_next = True
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return (state, state.to_gradio_chatbot(), "", None) + (no_change_btn,) * 5
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if args.moderate:
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flagged = violates_moderation(text)
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if flagged:
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state.skip_next = True
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return (state, state.to_gradio_chatbot(), moderation_msg, None) + (
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no_change_btn,) * 5
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text = text[:1536] # Hard cut-off
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if image is not None:
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text = text[:1200] # Hard cut-off for images
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if '<image>' not in text:
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# text = '<Image><image></Image>' + text
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text = text + '\n<image>'
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text = (text, image, image_process_mode)
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if len(state.get_images(return_pil=True)) > 0:
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state = default_conversation.copy()
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state.append_message(state.roles[0], text)
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state.append_message(state.roles[1], None)
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state.skip_next = False
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return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5
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def http_bot(state, model_selector, temperature, top_p, max_new_tokens, request: gr.Request):
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logger.info(f"http_bot. ip: {request.client.host}")
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start_tstamp = time.time()
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model_name = model_selector
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if state.skip_next:
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# This generate call is skipped due to invalid inputs
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yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
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return
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if len(state.messages) == state.offset + 2:
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# First round of conversation
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if "llava" in model_name.lower():
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if 'llama-2' in model_name.lower():
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template_name = "llava_llama_2"
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elif "v1" in model_name.lower():
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if 'mmtag' in model_name.lower():
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template_name = "v1_mmtag"
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elif 'plain' in model_name.lower() and 'finetune' not in model_name.lower():
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template_name = "v1_mmtag"
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else:
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template_name = "llava_v1"
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elif "mpt" in model_name.lower():
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template_name = "mpt"
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else:
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if 'mmtag' in model_name.lower():
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template_name = "v0_mmtag"
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elif 'plain' in model_name.lower() and 'finetune' not in model_name.lower():
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template_name = "v0_mmtag"
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else:
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template_name = "llava_v0"
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elif "mpt" in model_name:
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template_name = "mpt_text"
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elif "llama-2" in model_name:
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template_name = "llama_2"
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else:
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template_name = "vicuna_v1"
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new_state = conv_templates[template_name].copy()
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new_state.append_message(new_state.roles[0], state.messages[-2][1])
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new_state.append_message(new_state.roles[1], None)
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state = new_state
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# Query worker address
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controller_url = args.controller_url
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ret = requests.post(controller_url + "/get_worker_address",
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json={"model": model_name})
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worker_addr = ret.json()["address"]
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logger.info(f"model_name: {model_name}, worker_addr: {worker_addr}")
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# No available worker
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if worker_addr == "":
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state.messages[-1][-1] = server_error_msg
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yield (state, state.to_gradio_chatbot(), disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
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return
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# Construct prompt
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prompt = state.get_prompt()
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all_images = state.get_images(return_pil=True)
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all_image_hash = [hashlib.md5(image.tobytes()).hexdigest() for image in all_images]
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for image, hash in zip(all_images, all_image_hash):
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t = datetime.datetime.now()
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filename = os.path.join(LOGDIR, "serve_images", f"{t.year}-{t.month:02d}-{t.day:02d}", f"{hash}.jpg")
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if not os.path.isfile(filename):
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os.makedirs(os.path.dirname(filename), exist_ok=True)
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image.save(filename)
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# Make requests
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pload = {
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"model": model_name,
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"prompt": prompt,
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"temperature": float(temperature),
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"top_p": float(top_p),
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"max_new_tokens": min(int(max_new_tokens), 1536),
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"stop": state.sep if state.sep_style in [SeparatorStyle.SINGLE, SeparatorStyle.MPT] else state.sep2,
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"images": f'List of {len(state.get_images())} images: {all_image_hash}',
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}
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logger.info(f"==== request ====\n{pload}")
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pload['images'] = state.get_images()
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state.messages[-1][-1] = "▌"
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yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
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try:
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# Stream output
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response = requests.post(worker_addr + "/worker_generate_stream",
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headers=headers, json=pload, stream=True, timeout=10)
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for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
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if chunk:
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data = json.loads(chunk.decode())
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if data["error_code"] == 0:
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output = data["text"][len(prompt):].strip()
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state.messages[-1][-1] = output + "▌"
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yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
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else:
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output = data["text"] + f" (error_code: {data['error_code']})"
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state.messages[-1][-1] = output
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yield (state, state.to_gradio_chatbot()) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
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return
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time.sleep(0.03)
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except requests.exceptions.RequestException as e:
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state.messages[-1][-1] = server_error_msg
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yield (state, state.to_gradio_chatbot()) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
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return
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state.messages[-1][-1] = state.messages[-1][-1][:-1]
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yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
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finish_tstamp = time.time()
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logger.info(f"{output}")
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with open(get_conv_log_filename(), "a") as fout:
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data = {
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"tstamp": round(finish_tstamp, 4),
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"type": "chat",
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"model": model_name,
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"start": round(start_tstamp, 4),
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"finish": round(start_tstamp, 4),
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"state": state.dict(),
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"images": all_image_hash,
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"ip": request.client.host,
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}
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fout.write(json.dumps(data) + "\n")
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title_markdown = ("""
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# 🌋 LLaVA: Large Language and Vision Assistant
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[[Project Page](https://llava-vl.github.io)] [[Code](https://github.com/haotian-liu/LLaVA)] [[Model](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)] | 📚 [[LLaVA](https://arxiv.org/abs/2304.08485)] [[LLaVA-v1.5](https://arxiv.org/abs/2310.03744)]
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""")
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tos_markdown = ("""
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### Terms of use
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By using this service, users are required to agree to the following terms:
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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.
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Please click the "Flag" button if you get any inappropriate answer! We will collect those to keep improving our moderator.
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For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality.
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""")
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learn_more_markdown = ("""
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### License
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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.
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""")
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block_css = """
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#buttons button {
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min-width: min(120px,100%);
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}
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"""
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def build_demo(embed_mode):
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textbox = gr.Textbox(show_label=False, placeholder="Enter text and press ENTER", container=False)
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with gr.Blocks(title="LLaVA", theme=gr.themes.Default(), css=block_css) as demo:
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state = gr.State()
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if not embed_mode:
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gr.Markdown(title_markdown)
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with gr.Row():
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with gr.Column(scale=3):
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with gr.Row(elem_id="model_selector_row"):
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model_selector = gr.Dropdown(
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choices=models,
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value=models[0] if len(models) > 0 else "",
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interactive=True,
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show_label=False,
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container=False)
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imagebox = gr.Image(type="pil")
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image_process_mode = gr.Radio(
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["Crop", "Resize", "Pad", "Default"],
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value="Default",
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label="Preprocess for non-square image", visible=False)
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cur_dir = os.path.dirname(os.path.abspath(__file__))
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gr.Examples(examples=[
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[f"{cur_dir}/examples/extreme_ironing.jpg", "What is unusual about this image?"],
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[f"{cur_dir}/examples/waterview.jpg", "What are the things I should be cautious about when I visit here?"],
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], inputs=[imagebox, textbox])
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with gr.Accordion("Parameters", open=False) as parameter_row:
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temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.2, step=0.1, interactive=True, label="Temperature",)
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top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.7, step=0.1, interactive=True, label="Top P",)
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max_output_tokens = gr.Slider(minimum=0, maximum=1024, value=512, step=64, interactive=True, label="Max output tokens",)
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with gr.Column(scale=8):
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chatbot = gr.Chatbot(elem_id="chatbot", label="LLaVA Chatbot", height=550)
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with gr.Row():
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with gr.Column(scale=8):
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textbox.render()
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with gr.Column(scale=1, min_width=50):
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submit_btn = gr.Button(value="Send", variant="primary")
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with gr.Row(elem_id="buttons") as button_row:
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upvote_btn = gr.Button(value="👍 Upvote", interactive=False)
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downvote_btn = gr.Button(value="👎 Downvote", interactive=False)
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flag_btn = gr.Button(value="⚠️ Flag", interactive=False)
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#stop_btn = gr.Button(value="⏹️ Stop Generation", interactive=False)
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regenerate_btn = gr.Button(value="🔄 Regenerate", interactive=False)
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clear_btn = gr.Button(value="🗑️ Clear", interactive=False)
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if not embed_mode:
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gr.Markdown(tos_markdown)
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gr.Markdown(learn_more_markdown)
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url_params = gr.JSON(visible=False)
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# Register listeners
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btn_list = [upvote_btn, downvote_btn, flag_btn, regenerate_btn, clear_btn]
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upvote_btn.click(upvote_last_response,
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[state, model_selector], [textbox, upvote_btn, downvote_btn, flag_btn])
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downvote_btn.click(downvote_last_response,
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[state, model_selector], [textbox, upvote_btn, downvote_btn, flag_btn])
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flag_btn.click(flag_last_response,
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[state, model_selector], [textbox, upvote_btn, downvote_btn, flag_btn])
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regenerate_btn.click(regenerate, [state, image_process_mode],
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[state, chatbot, textbox, imagebox] + btn_list).then(
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http_bot, [state, model_selector, temperature, top_p, max_output_tokens],
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[state, chatbot] + btn_list)
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clear_btn.click(clear_history, None, [state, chatbot, textbox, imagebox] + btn_list)
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textbox.submit(add_text, [state, textbox, imagebox, image_process_mode], [state, chatbot, textbox, imagebox] + btn_list
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).then(http_bot, [state, model_selector, temperature, top_p, max_output_tokens],
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[state, chatbot] + btn_list)
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submit_btn.click(add_text, [state, textbox, imagebox, image_process_mode], [state, chatbot, textbox, imagebox] + btn_list
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).then(http_bot, [state, model_selector, temperature, top_p, max_output_tokens],
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[state, chatbot] + btn_list)
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if args.model_list_mode == "once":
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demo.load(load_demo, [url_params], [state, model_selector],
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_js=get_window_url_params)
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elif args.model_list_mode == "reload":
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demo.load(load_demo_refresh_model_list, None, [state, model_selector])
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else:
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raise ValueError(f"Unknown model list mode: {args.model_list_mode}")
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return demo
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--host", type=str, default="0.0.0.0")
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parser.add_argument("--port", type=int)
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parser.add_argument("--controller-url", type=str, default="http://localhost:21001")
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parser.add_argument("--concurrency-count", type=int, default=10)
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parser.add_argument("--model-list-mode", type=str, default="once",
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choices=["once", "reload"])
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parser.add_argument("--share", action="store_true")
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parser.add_argument("--moderate", action="store_true")
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parser.add_argument("--embed", action="store_true")
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args = parser.parse_args()
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logger.info(f"args: {args}")
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models = get_model_list()
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logger.info(args)
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demo = build_demo(args.embed)
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demo.queue(
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concurrency_count=args.concurrency_count,
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api_open=False
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).launch(
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server_name=args.host,
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server_port=args.port,
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share=args.share
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)
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