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Delete gradio_web_server.py
Browse files- gradio_web_server.py +0 -508
gradio_web_server.py
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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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from taxonomy import wrap_taxonomy, default_taxonomy
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def clear_conv(conv):
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conv.messages = []
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return conv
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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()
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enable_btn = gr.Button(interactive=True)
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disable_btn = gr.Button(interactive=False)
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priority = {
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"LlavaGuard-7B": "aaaaaaa",
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"LlavaGuard-13B": "aaaaaab",
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"LlavaGuard-34B": "aaaaaac",
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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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logger.info(f"get_model_list: {ret.json()}")
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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(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(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(
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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 or 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 = wrap_taxonomy(text)
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if image is not None:
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text = text # 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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state = default_conversation.copy()
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state = clear_conv(state)
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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(), default_taxonomy, 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 "mistral" in model_name.lower() or "mixtral" in model_name.lower():
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if 'orca' in model_name.lower():
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template_name = "mistral_orca"
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elif 'hermes' in model_name.lower():
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template_name = "chatml_direct"
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else:
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template_name = "mistral_instruct"
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elif 'llava-v1.6-34b' in model_name.lower():
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template_name = "chatml_direct"
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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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# "num_beams": 2,
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# "top_k": 50,
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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()) + (
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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(finish_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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# LLAVAGUARD: VLM-based Safeguard for Vision Dataset Curation and Safety Assessment
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[[Project Page](https://ml-research.github.io/human-centered-genai/projects/llavaguard/index.html)]
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[[Code](https://github.com/ml-research/LlavaGuard)]
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[[Model](https://huggingface.co/collections/AIML-TUDA/llavaguard-665b42e89803408ee8ec1086)]
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[[Dataset](https://huggingface.co/datasets/aiml-tuda/llavaguard)]
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[[LavaGuard](https://arxiv.org/abs/2406.05113)]
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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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taxonomies = ["Default", "Modified w/ O1 non-violating", "Default message 3"]
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def build_demo(embed_mode, cur_dir=None, concurrency_count=10):
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with gr.Accordion("Safety Risk Taxonomy", open=False) as accordion:
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textbox = gr.Textbox(
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label="Safety Risk Taxonomy",
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show_label=True,
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placeholder="Enter your safety policy here",
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container=True,
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value=default_taxonomy,
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lines=50)
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with gr.Blocks(title="LlavaGuard", 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", label="Image", container=False)
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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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if cur_dir is None:
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cur_dir = os.path.dirname(os.path.abspath(__file__))
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gr.Examples(examples=[
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370 |
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[f"{cur_dir}/examples/image{i}.png"] for i in range(1,6)
|
371 |
-
], inputs=imagebox)
|
372 |
-
|
373 |
-
with gr.Accordion("Parameters", open=False) as parameter_row:
|
374 |
-
temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.2, step=0.1, interactive=True,
|
375 |
-
label="Temperature", )
|
376 |
-
top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.95, step=0.1, interactive=True, label="Top P", )
|
377 |
-
max_output_tokens = gr.Slider(minimum=0, maximum=1024, value=512, step=64, interactive=True,
|
378 |
-
label="Max output tokens", )
|
379 |
-
|
380 |
-
with gr.Column(scale=8):
|
381 |
-
chatbot = gr.Chatbot(
|
382 |
-
elem_id="chatbot",
|
383 |
-
label="LLavaGuard Safety Assessment",
|
384 |
-
height=650,
|
385 |
-
layout="panel",
|
386 |
-
)
|
387 |
-
with gr.Row():
|
388 |
-
with gr.Column(scale=8):
|
389 |
-
textbox.render()
|
390 |
-
with gr.Column(scale=1, min_width=50):
|
391 |
-
submit_btn = gr.Button(value="Send", variant="primary")
|
392 |
-
with gr.Row(elem_id="buttons") as button_row:
|
393 |
-
upvote_btn = gr.Button(value="👍 Upvote", interactive=False)
|
394 |
-
downvote_btn = gr.Button(value="👎 Downvote", interactive=False)
|
395 |
-
flag_btn = gr.Button(value="⚠️ Flag", interactive=False)
|
396 |
-
# stop_btn = gr.Button(value="⏹️ Stop Generation", interactive=False)
|
397 |
-
regenerate_btn = gr.Button(value="🔄 Regenerate", interactive=False)
|
398 |
-
clear_btn = gr.Button(value="🗑️ Clear", interactive=False)
|
399 |
-
|
400 |
-
if not embed_mode:
|
401 |
-
gr.Markdown(tos_markdown)
|
402 |
-
gr.Markdown(learn_more_markdown)
|
403 |
-
url_params = gr.JSON(visible=False)
|
404 |
-
|
405 |
-
# Register listeners
|
406 |
-
btn_list = [upvote_btn, downvote_btn, flag_btn, regenerate_btn, clear_btn]
|
407 |
-
upvote_btn.click(
|
408 |
-
upvote_last_response,
|
409 |
-
[state, model_selector],
|
410 |
-
[textbox, upvote_btn, downvote_btn, flag_btn]
|
411 |
-
)
|
412 |
-
downvote_btn.click(
|
413 |
-
downvote_last_response,
|
414 |
-
[state, model_selector],
|
415 |
-
[textbox, upvote_btn, downvote_btn, flag_btn]
|
416 |
-
)
|
417 |
-
flag_btn.click(
|
418 |
-
flag_last_response,
|
419 |
-
[state, model_selector],
|
420 |
-
[textbox, upvote_btn, downvote_btn, flag_btn]
|
421 |
-
)
|
422 |
-
|
423 |
-
regenerate_btn.click(
|
424 |
-
regenerate,
|
425 |
-
[state, image_process_mode],
|
426 |
-
[state, chatbot, textbox, imagebox] + btn_list
|
427 |
-
).then(
|
428 |
-
http_bot,
|
429 |
-
[state, model_selector, temperature, top_p, max_output_tokens],
|
430 |
-
[state, chatbot] + btn_list,
|
431 |
-
concurrency_limit=concurrency_count
|
432 |
-
)
|
433 |
-
|
434 |
-
clear_btn.click(
|
435 |
-
clear_history,
|
436 |
-
None,
|
437 |
-
[state, chatbot, textbox, imagebox] + btn_list,
|
438 |
-
queue=False
|
439 |
-
)
|
440 |
-
|
441 |
-
textbox.submit(
|
442 |
-
add_text,
|
443 |
-
[state, textbox, imagebox, image_process_mode],
|
444 |
-
[state, chatbot, textbox, imagebox] + btn_list,
|
445 |
-
queue=False
|
446 |
-
).then(
|
447 |
-
http_bot,
|
448 |
-
[state, model_selector, temperature, top_p, max_output_tokens],
|
449 |
-
[state, chatbot] + btn_list,
|
450 |
-
concurrency_limit=concurrency_count
|
451 |
-
)
|
452 |
-
|
453 |
-
submit_btn.click(
|
454 |
-
add_text,
|
455 |
-
[state, textbox, imagebox, image_process_mode],
|
456 |
-
[state, chatbot, textbox, imagebox] + btn_list
|
457 |
-
).then(
|
458 |
-
http_bot,
|
459 |
-
[state, model_selector, temperature, top_p, max_output_tokens],
|
460 |
-
[state, chatbot] + btn_list,
|
461 |
-
concurrency_limit=concurrency_count
|
462 |
-
)
|
463 |
-
|
464 |
-
if args.model_list_mode == "once":
|
465 |
-
demo.load(
|
466 |
-
load_demo,
|
467 |
-
[url_params],
|
468 |
-
[state, model_selector],
|
469 |
-
js=get_window_url_params
|
470 |
-
)
|
471 |
-
elif args.model_list_mode == "reload":
|
472 |
-
demo.load(
|
473 |
-
load_demo_refresh_model_list,
|
474 |
-
None,
|
475 |
-
[state, model_selector],
|
476 |
-
queue=False
|
477 |
-
)
|
478 |
-
else:
|
479 |
-
raise ValueError(f"Unknown model list mode: {args.model_list_mode}")
|
480 |
-
|
481 |
-
return demo
|
482 |
-
|
483 |
-
|
484 |
-
if __name__ == "__main__":
|
485 |
-
parser = argparse.ArgumentParser()
|
486 |
-
parser.add_argument("--host", type=str, default="0.0.0.0")
|
487 |
-
parser.add_argument("--port", type=int)
|
488 |
-
parser.add_argument("--controller-url", type=str, default="http://localhost:21001")
|
489 |
-
parser.add_argument("--concurrency-count", type=int, default=16)
|
490 |
-
parser.add_argument("--model-list-mode", type=str, default="once",
|
491 |
-
choices=["once", "reload"])
|
492 |
-
parser.add_argument("--share", action="store_true")
|
493 |
-
parser.add_argument("--moderate", action="store_true")
|
494 |
-
parser.add_argument("--embed", action="store_true")
|
495 |
-
args = parser.parse_args()
|
496 |
-
logger.info(f"args: {args}")
|
497 |
-
|
498 |
-
models = get_model_list()
|
499 |
-
|
500 |
-
logger.info(args)
|
501 |
-
demo = build_demo(args.embed, concurrency_count=args.concurrency_count)
|
502 |
-
demo.queue(
|
503 |
-
api_open=False
|
504 |
-
).launch(
|
505 |
-
server_name=args.host,
|
506 |
-
server_port=args.port,
|
507 |
-
share=args.share
|
508 |
-
)
|
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