mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
548 lines
26 KiB
Python
548 lines
26 KiB
Python
import re
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import gc
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import time
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import glob
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import torch
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import argparse
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import json
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from sys import exit
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from pathlib import Path
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import gradio as gr
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import warnings
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from tqdm import tqdm
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import transformers
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from modules.html_generator import *
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from modules.ui import *
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transformers.logging.set_verbosity_error()
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', type=str, help='Name of the model to load by default.')
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parser.add_argument('--notebook', action='store_true', help='Launch the web UI in notebook mode, where the output is written to the same text box as the input.')
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parser.add_argument('--chat', action='store_true', help='Launch the web UI in chat mode.')
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parser.add_argument('--cai-chat', action='store_true', help='Launch the web UI in chat mode with a style similar to Character.AI\'s. If the file profile.png or profile.jpg exists in the same folder as server.py, this image will be used as the bot\'s profile picture.')
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parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.')
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parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.')
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parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
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parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.')
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parser.add_argument('--disk-cache-dir', type=str, help='Directory to save the disk cache to. Defaults to "cache/".')
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parser.add_argument('--gpu-memory', type=int, help='Maximum GPU memory in GiB to allocate. This is useful if you get out of memory errors while trying to generate text. Must be an integer number.')
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parser.add_argument('--cpu-memory', type=int, help='Maximum CPU memory in GiB to allocate for offloaded weights. Must be an integer number. Defaults to 99.')
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parser.add_argument('--no-stream', action='store_true', help='Don\'t stream the text output in real time. This slightly improves the text generation performance.')
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parser.add_argument('--settings', type=str, help='Load the default interface settings from this json file. See settings-template.json for an example.')
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parser.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.')
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parser.add_argument('--share', action='store_true', help='Create a public URL. This is useful for running the web UI on Google Colab or similar.')
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args = parser.parse_args()
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settings = {
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'max_new_tokens': 200,
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'max_new_tokens_min': 1,
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'max_new_tokens_max': 2000,
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'preset': 'NovelAI-Sphinx Moth',
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'name1': 'Person 1',
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'name2': 'Person 2',
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'context': 'This is a conversation between two people.',
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'prompt': 'Common sense questions and answers\n\nQuestion: \nFactual answer:',
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'prompt_gpt4chan': '-----\n--- 865467536\nInput text\n--- 865467537\n',
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'stop_at_newline': True,
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'history_size': 8,
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'history_size_min': 0,
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'history_size_max': 64,
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'preset_pygmalion': 'Pygmalion',
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'name1_pygmalion': 'You',
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'name2_pygmalion': 'Kawaii',
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'context_pygmalion': "Kawaii's persona: Kawaii is a cheerful person who loves to make others smile. She is an optimist who loves to spread happiness and positivity wherever she goes.\n<START>",
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'stop_at_newline_pygmalion': False,
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}
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if args.settings is not None and Path(args.settings).exists():
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with open(Path(args.settings), 'r') as f:
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new_settings = json.load(f)
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for item in new_settings:
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if item in settings:
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settings[item] = new_settings[item]
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def load_model(model_name):
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print(f"Loading {model_name}...")
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t0 = time.time()
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# Default settings
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if not (args.cpu or args.load_in_8bit or args.auto_devices or args.disk or args.gpu_memory is not None):
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if Path(f"torch-dumps/{model_name}.pt").exists():
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print("Loading in .pt format...")
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model = torch.load(Path(f"torch-dumps/{model_name}.pt"))
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elif model_name.lower().startswith(('gpt-neo', 'opt-', 'galactica')) and any(size in model_name.lower() for size in ('13b', '20b', '30b')):
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model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), device_map='auto', load_in_8bit=True)
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else:
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model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
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# Custom
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else:
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settings = ["low_cpu_mem_usage=True"]
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command = "AutoModelForCausalLM.from_pretrained"
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if args.cpu:
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settings.append("torch_dtype=torch.float32")
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else:
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settings.append("device_map='auto'")
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if args.gpu_memory is not None:
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if args.cpu_memory is not None:
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settings.append(f"max_memory={{0: '{args.gpu_memory}GiB', 'cpu': '{args.cpu_memory}GiB'}}")
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else:
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settings.append(f"max_memory={{0: '{args.gpu_memory}GiB', 'cpu': '99GiB'}}")
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if args.disk:
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if args.disk_cache_dir is not None:
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settings.append(f"offload_folder='{args.disk_cache_dir}'")
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else:
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settings.append("offload_folder='cache'")
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if args.load_in_8bit:
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settings.append("load_in_8bit=True")
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else:
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settings.append("torch_dtype=torch.float16")
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settings = ', '.join(set(settings))
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command = f"{command}(Path(f'models/{model_name}'), {settings})"
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model = eval(command)
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# Loading the tokenizer
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if model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')) and Path(f"models/gpt-j-6B/").exists():
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tokenizer = AutoTokenizer.from_pretrained(Path("models/gpt-j-6B/"))
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else:
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tokenizer = AutoTokenizer.from_pretrained(Path(f"models/{model_name}/"))
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tokenizer.truncation_side = 'left'
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print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
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return model, tokenizer
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# Removes empty replies from gpt4chan outputs
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def fix_gpt4chan(s):
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for i in range(10):
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s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s)
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s = re.sub("--- [0-9]*\n *\n---", "---", s)
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s = re.sub("--- [0-9]*\n\n\n---", "---", s)
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return s
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# Fix the LaTeX equations in galactica
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def fix_galactica(s):
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s = s.replace(r'\[', r'$')
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s = s.replace(r'\]', r'$')
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s = s.replace(r'\(', r'$')
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s = s.replace(r'\)', r'$')
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s = s.replace(r'$$', r'$')
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return s
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def encode(prompt, tokens):
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if not args.cpu:
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torch.cuda.empty_cache()
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input_ids = tokenizer.encode(str(prompt), return_tensors='pt', truncation=True, max_length=2048-tokens).cuda()
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else:
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input_ids = tokenizer.encode(str(prompt), return_tensors='pt', truncation=True, max_length=2048-tokens)
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return input_ids
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def decode(output_ids):
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reply = tokenizer.decode(output_ids, skip_special_tokens=True)
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reply = reply.replace(r'<|endoftext|>', '')
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return reply
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def formatted_outputs(reply, model_name):
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if not (args.chat or args.cai_chat):
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if model_name.lower().startswith('galactica'):
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reply = fix_galactica(reply)
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return reply, reply, generate_basic_html(reply)
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elif model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')):
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reply = fix_gpt4chan(reply)
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return reply, 'Only applicable for GALACTICA models.', generate_4chan_html(reply)
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else:
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return reply, 'Only applicable for GALACTICA models.', generate_basic_html(reply)
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else:
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return reply
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def generate_reply(question, tokens, inference_settings, selected_model, eos_token=None):
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global model, tokenizer, model_name, loaded_preset, preset
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if selected_model != model_name:
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model_name = selected_model
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model = tokenizer = None
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if not args.cpu:
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gc.collect()
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torch.cuda.empty_cache()
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model, tokenizer = load_model(model_name)
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if inference_settings != loaded_preset:
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with open(Path(f'presets/{inference_settings}.txt'), 'r') as infile:
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preset = infile.read()
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loaded_preset = inference_settings
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cuda = "" if args.cpu else ".cuda()"
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n = None if eos_token is None else tokenizer.encode(eos_token, return_tensors='pt')[0][-1]
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input_ids = encode(question, tokens)
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# Generate the entire reply at once
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if args.no_stream:
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output = eval(f"model.generate(input_ids, eos_token_id={n}, {preset}){cuda}")
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reply = decode(output[0])
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yield formatted_outputs(reply, model_name)
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# Generate the reply 1 token at a time
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else:
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yield formatted_outputs(question, model_name)
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preset = preset.replace('max_new_tokens=tokens', 'max_new_tokens=1')
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for i in tqdm(range(tokens)):
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output = eval(f"model.generate(input_ids, {preset}){cuda}")
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reply = decode(output[0])
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if eos_token is not None and reply[-1] == eos_token:
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break
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yield formatted_outputs(reply, model_name)
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input_ids = output
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def get_available_models():
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return sorted(set([item.replace('.pt', '') for item in map(lambda x : str(x.name), list(Path('models/').glob('*'))+list(Path('torch-dumps/').glob('*'))) if not item.endswith('.txt')]), key=str.lower)
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def get_available_presets():
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return sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('presets').glob('*.txt'))), key=str.lower)
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def get_available_characters():
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return ["None"] + sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('characters').glob('*.json'))), key=str.lower)
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available_models = get_available_models()
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available_presets = get_available_presets()
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available_characters = get_available_characters()
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# Choosing the default model
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if args.model is not None:
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model_name = args.model
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else:
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if len(available_models) == 0:
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print("No models are available! Please download at least one.")
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exit(0)
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elif len(available_models) == 1:
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i = 0
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else:
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print("The following models are available:\n")
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for i,model in enumerate(available_models):
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print(f"{i+1}. {model}")
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print(f"\nWhich one do you want to load? 1-{len(available_models)}\n")
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i = int(input())-1
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print()
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model_name = available_models[i]
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model, tokenizer = load_model(model_name)
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loaded_preset = None
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# UI settings
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default_text = settings['prompt_gpt4chan'] if model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')) else settings['prompt']
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description = f"\n\n# Text generation lab\nGenerate text using Large Language Models.\n"
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css = ".my-4 {margin-top: 0} .py-6 {padding-top: 2.5rem} #refresh-button {flex: none; margin: 0; padding: 0; min-width: 50px; border: none; box-shadow: none; border-radius: 0} #download-label, #upload-label {min-height: 0}"
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if args.chat or args.cai_chat:
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history = []
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character = None
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# This gets the new line characters right.
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def clean_chat_message(text):
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text = text.replace('\n', '\n\n')
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text = re.sub(r"\n{3,}", "\n\n", text)
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text = text.strip()
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return text
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def generate_chat_prompt(text, tokens, name1, name2, context, history_size):
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text = clean_chat_message(text)
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rows = [f"{context.strip()}\n"]
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i = len(history)-1
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count = 0
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while i >= 0 and len(encode(''.join(rows), tokens)[0]) < 2048-tokens:
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rows.insert(1, f"{name2}: {history[i][1].strip()}\n")
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count += 1
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if not (history[i][0] == '<|BEGIN-VISIBLE-CHAT|>'):
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rows.insert(1, f"{name1}: {history[i][0].strip()}\n")
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count += 1
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i -= 1
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if history_size != 0 and count >= history_size:
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break
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rows.append(f"{name1}: {text}\n")
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rows.append(f"{name2}:")
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while len(rows) > 3 and len(encode(''.join(rows), tokens)[0]) >= 2048-tokens:
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rows.pop(1)
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rows.pop(1)
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question = ''.join(rows)
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return question
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def remove_example_dialogue_from_history(history):
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_history = copy.deepcopy(history)
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for i in range(len(_history)):
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if '<|BEGIN-VISIBLE-CHAT|>' in _history[i][0]:
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_history[i][0] = _history[i][0].replace('<|BEGIN-VISIBLE-CHAT|>', '')
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_history = _history[i:]
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break
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return _history
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def chatbot_wrapper(text, tokens, inference_settings, selected_model, name1, name2, context, check, history_size):
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question = generate_chat_prompt(text, tokens, name1, name2, context, history_size)
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history.append(['', ''])
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eos_token = '\n' if check else None
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for reply in generate_reply(question, tokens, inference_settings, selected_model, eos_token=eos_token):
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next_character_found = False
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previous_idx = [m.start() for m in re.finditer(f"(^|\n){name2}:", question)]
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idx = [m.start() for m in re.finditer(f"(^|\n){name2}:", reply)]
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idx = idx[len(previous_idx)-1]
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reply = reply[idx + len(f"\n{name2}:"):]
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if check:
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reply = reply.split('\n')[0].strip()
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else:
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idx = reply.find(f"\n{name1}:")
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if idx != -1:
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reply = reply[:idx]
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next_character_found = True
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reply = clean_chat_message(reply)
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history[-1] = [text, reply]
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if next_character_found:
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break
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# Prevent the chat log from flashing if something like "\nYo" is generated just
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# before "\nYou:" is completed
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tmp = f"\n{name1}:"
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next_character_substring_found = False
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for j in range(1, len(tmp)):
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if reply[-j:] == tmp[:j]:
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next_character_substring_found = True
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if not next_character_substring_found:
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yield remove_example_dialogue_from_history(history)
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yield remove_example_dialogue_from_history(history)
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def cai_chatbot_wrapper(text, tokens, inference_settings, selected_model, name1, name2, context, check, history_size):
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for history in chatbot_wrapper(text, tokens, inference_settings, selected_model, name1, name2, context, check, history_size):
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yield generate_chat_html(history, name1, name2, character)
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def regenerate_wrapper(text, tokens, inference_settings, selected_model, name1, name2, context, check, history_size):
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last = history.pop()
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text = last[0]
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if args.cai_chat:
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for i in cai_chatbot_wrapper(text, tokens, inference_settings, selected_model, name1, name2, context, check, history_size):
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yield i
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else:
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for i in chatbot_wrapper(text, tokens, inference_settings, selected_model, name1, name2, context, check, history_size):
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yield i
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def remove_last_message(name1, name2):
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history.pop()
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if args.cai_chat:
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return generate_chat_html(history, name1, name2, character)
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else:
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return history
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def clear():
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global history
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history = []
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def clear_html():
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return generate_chat_html([], "", "", character)
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def redraw_html(name1, name2):
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global history
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_history = remove_example_dialogue_from_history(history)
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return generate_chat_html(_history, name1, name2, character)
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def save_history():
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if not Path('logs').exists():
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Path('logs').mkdir()
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with open(Path('logs/conversation.json'), 'w') as f:
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f.write(json.dumps({'data': history}, indent=2))
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return Path('logs/conversation.json')
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def load_history(file):
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global history
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history = json.loads(file.decode('utf-8'))['data']
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def tokenize_example_dialogue(dialogue, name1, name2):
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dialogue = re.sub('<START>', '', dialogue)
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dialogue = re.sub('(\n|^)[Aa]non:', '\\1You:', dialogue)
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idx = [m.start() for m in re.finditer(f"(^|\n)({name1}|{name2}):", dialogue)]
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messages = []
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for i in range(len(idx)-1):
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messages.append(dialogue[idx[i]:idx[i+1]].strip())
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history = []
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entry = ['', '']
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for i in messages:
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if i.startswith(f'{name1}:'):
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entry[0] = i[len(f'{name1}:'):].strip()
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elif i.startswith(f'{name2}:'):
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entry[1] = i[len(f'{name2}:'):].strip()
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if not (len(entry[0]) == 0 and len(entry[1]) == 0):
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history.append(entry)
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entry = ['', '']
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return history
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def load_character(_character, name1, name2):
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global history, character
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context = ""
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history = []
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if _character != 'None':
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character = _character
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with open(Path(f'characters/{_character}.json'), 'r') as f:
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data = json.loads(f.read())
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name2 = data['char_name']
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if 'char_persona' in data and data['char_persona'] != '':
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context += f"{data['char_name']}'s Persona: {data['char_persona']}\n"
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if 'world_scenario' in data and data['world_scenario'] != '':
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context += f"Scenario: {data['world_scenario']}\n"
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context = f"{context.strip()}\n<START>\n"
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if 'example_dialogue' in data and data['example_dialogue'] != '':
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history = tokenize_example_dialogue(data['example_dialogue'], name1, name2)
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if 'char_greeting' in data and len(data['char_greeting'].strip()) > 0:
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history += [['<|BEGIN-VISIBLE-CHAT|>', data['char_greeting']]]
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else:
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history += [['<|BEGIN-VISIBLE-CHAT|>', "Hello there!"]]
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else:
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character = None
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context = settings['context_pygmalion']
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name2 = settings['name2_pygmalion']
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_history = remove_example_dialogue_from_history(history)
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if args.cai_chat:
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return name2, context, generate_chat_html(_history, name1, name2, character)
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|
else:
|
|
return name2, context, _history
|
|
|
|
suffix = '_pygmalion' if 'pygmalion' in model_name.lower() else ''
|
|
with gr.Blocks(css=css+".h-\[40vh\] {height: 66.67vh} .gradio-container {max-width: 800px; margin-left: auto; margin-right: auto}", analytics_enabled=False) as interface:
|
|
if args.cai_chat:
|
|
display1 = gr.HTML(value=generate_chat_html([], "", "", character))
|
|
else:
|
|
display1 = gr.Chatbot()
|
|
textbox = gr.Textbox(lines=2, label='Input')
|
|
btn = gr.Button("Generate")
|
|
with gr.Row():
|
|
stop = gr.Button("Stop")
|
|
btn_regenerate = gr.Button("Regenerate")
|
|
btn_remove_last = gr.Button("Remove last")
|
|
btn_clear = gr.Button("Clear history")
|
|
|
|
with gr.Row():
|
|
with gr.Column():
|
|
length_slider = gr.Slider(minimum=settings['max_new_tokens_min'], maximum=settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=settings['max_new_tokens'])
|
|
with gr.Row():
|
|
model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model')
|
|
create_refresh_button(model_menu, lambda : None, lambda : {"choices": get_available_models()}, "refresh-button")
|
|
with gr.Column():
|
|
history_size_slider = gr.Slider(minimum=settings['history_size_min'], maximum=settings['history_size_max'], step=1, label='Chat history size in prompt (0 for no limit)', value=settings['history_size'])
|
|
with gr.Row():
|
|
preset_menu = gr.Dropdown(choices=available_presets, value=settings[f'preset{suffix}'], label='Settings preset')
|
|
create_refresh_button(preset_menu, lambda : None, lambda : {"choices": get_available_presets()}, "refresh-button")
|
|
|
|
name1 = gr.Textbox(value=settings[f'name1{suffix}'], lines=1, label='Your name')
|
|
name2 = gr.Textbox(value=settings[f'name2{suffix}'], lines=1, label='Bot\'s name')
|
|
context = gr.Textbox(value=settings[f'context{suffix}'], lines=2, label='Context')
|
|
with gr.Row():
|
|
character_menu = gr.Dropdown(choices=available_characters, value="None", label='Character')
|
|
create_refresh_button(character_menu, lambda : None, lambda : {"choices": get_available_characters()}, "refresh-button")
|
|
|
|
with gr.Row():
|
|
check = gr.Checkbox(value=settings[f'stop_at_newline{suffix}'], label='Stop generating at new line character?')
|
|
with gr.Row():
|
|
with gr.Tab('Download chat history'):
|
|
download = gr.File()
|
|
save_btn = gr.Button(value="Click me")
|
|
with gr.Tab('Upload chat history'):
|
|
upload = gr.File(type='binary')
|
|
|
|
input_params = [textbox, length_slider, preset_menu, model_menu, name1, name2, context, check, history_size_slider]
|
|
if args.cai_chat:
|
|
gen_event = btn.click(cai_chatbot_wrapper, input_params, display1, show_progress=args.no_stream, api_name="textgen")
|
|
gen_event2 = textbox.submit(cai_chatbot_wrapper, input_params, display1, show_progress=args.no_stream)
|
|
btn_clear.click(clear_html, [], display1, show_progress=False)
|
|
else:
|
|
gen_event = btn.click(chatbot_wrapper, input_params, display1, show_progress=args.no_stream, api_name="textgen")
|
|
gen_event2 = textbox.submit(chatbot_wrapper, input_params, display1, show_progress=args.no_stream)
|
|
btn_clear.click(lambda x: "", display1, display1, show_progress=False)
|
|
gen_event3 = btn_regenerate.click(regenerate_wrapper, input_params, display1, show_progress=args.no_stream)
|
|
|
|
btn_clear.click(clear)
|
|
btn_remove_last.click(remove_last_message, [name1, name2], display1, show_progress=False)
|
|
btn.click(lambda x: "", textbox, textbox, show_progress=False)
|
|
btn_regenerate.click(lambda x: "", textbox, textbox, show_progress=False)
|
|
textbox.submit(lambda x: "", textbox, textbox, show_progress=False)
|
|
stop.click(None, None, None, cancels=[gen_event, gen_event2, gen_event3])
|
|
save_btn.click(save_history, inputs=[], outputs=[download])
|
|
upload.upload(load_history, [upload], [])
|
|
character_menu.change(load_character, [character_menu, name1, name2], [name2, context, display1])
|
|
|
|
if args.cai_chat:
|
|
upload.upload(redraw_html, [name1, name2], [display1])
|
|
else:
|
|
upload.upload(lambda : remove_example_dialogue_from_history(history), [], [display1])
|
|
|
|
elif args.notebook:
|
|
with gr.Blocks(css=css, analytics_enabled=False) as interface:
|
|
gr.Markdown(description)
|
|
with gr.Tab('Raw'):
|
|
textbox = gr.Textbox(value=default_text, lines=23)
|
|
with gr.Tab('Markdown'):
|
|
markdown = gr.Markdown()
|
|
with gr.Tab('HTML'):
|
|
html = gr.HTML()
|
|
btn = gr.Button("Generate")
|
|
stop = gr.Button("Stop")
|
|
|
|
length_slider = gr.Slider(minimum=settings['max_new_tokens_min'], maximum=settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=settings['max_new_tokens'])
|
|
with gr.Row():
|
|
with gr.Column():
|
|
with gr.Row():
|
|
model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model')
|
|
create_refresh_button(model_menu, lambda : None, lambda : {"choices": get_available_models()}, "refresh-button")
|
|
with gr.Column():
|
|
with gr.Row():
|
|
preset_menu = gr.Dropdown(choices=available_presets, value=settings['preset'], label='Settings preset')
|
|
create_refresh_button(preset_menu, lambda : None, lambda : {"choices": get_available_presets()}, "refresh-button")
|
|
|
|
gen_event = btn.click(generate_reply, [textbox, length_slider, preset_menu, model_menu], [textbox, markdown, html], show_progress=args.no_stream, api_name="textgen")
|
|
gen_event2 = textbox.submit(generate_reply, [textbox, length_slider, preset_menu, model_menu], [textbox, markdown, html], show_progress=args.no_stream)
|
|
stop.click(None, None, None, cancels=[gen_event, gen_event2])
|
|
|
|
else:
|
|
with gr.Blocks(css=css, analytics_enabled=False) as interface:
|
|
gr.Markdown(description)
|
|
with gr.Row():
|
|
with gr.Column():
|
|
textbox = gr.Textbox(value=default_text, lines=15, label='Input')
|
|
length_slider = gr.Slider(minimum=settings['max_new_tokens_min'], maximum=settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=settings['max_new_tokens'])
|
|
with gr.Row():
|
|
preset_menu = gr.Dropdown(choices=available_presets, value=settings['preset'], label='Settings preset')
|
|
create_refresh_button(preset_menu, lambda : None, lambda : {"choices": get_available_presets()}, "refresh-button")
|
|
with gr.Row():
|
|
model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model')
|
|
create_refresh_button(model_menu, lambda : None, lambda : {"choices": get_available_models()}, "refresh-button")
|
|
btn = gr.Button("Generate")
|
|
with gr.Row():
|
|
with gr.Column():
|
|
cont = gr.Button("Continue")
|
|
with gr.Column():
|
|
stop = gr.Button("Stop")
|
|
with gr.Column():
|
|
with gr.Tab('Raw'):
|
|
output_textbox = gr.Textbox(lines=15, label='Output')
|
|
with gr.Tab('Markdown'):
|
|
markdown = gr.Markdown()
|
|
with gr.Tab('HTML'):
|
|
html = gr.HTML()
|
|
|
|
gen_event = btn.click(generate_reply, [textbox, length_slider, preset_menu, model_menu], [output_textbox, markdown, html], show_progress=args.no_stream, api_name="textgen")
|
|
gen_event2 = textbox.submit(generate_reply, [textbox, length_slider, preset_menu, model_menu], [output_textbox, markdown, html], show_progress=args.no_stream)
|
|
cont_event = cont.click(generate_reply, [output_textbox, length_slider, preset_menu, model_menu], [output_textbox, markdown, html], show_progress=args.no_stream)
|
|
stop.click(None, None, None, cancels=[gen_event, gen_event2, cont_event])
|
|
|
|
interface.queue()
|
|
if args.listen:
|
|
interface.launch(share=args.share, server_name="0.0.0.0")
|
|
else:
|
|
interface.launch(share=args.share)
|