#!/usr/bin/python3
#==================================================================#
# KoboldAI
# Version: 1.16.4
# By: KoboldAIDev and the KoboldAI Community
#==================================================================#
# External packages
import os
from os import path, getcwd
import re
import tkinter as tk
from tkinter import messagebox
import json
import collections
import zipfile
import packaging
from typing import Any, Union, Dict, Set, List
import requests
import html
import argparse
import sys
import gc
# KoboldAI
import fileops
import gensettings
from utils import debounce
import utils
import structures
#==================================================================#
# Variables & Storage
#==================================================================#
# Terminal tags for colored text
class colors:
PURPLE = '\033[95m'
BLUE = '\033[94m'
CYAN = '\033[96m'
GREEN = '\033[92m'
YELLOW = '\033[93m'
RED = '\033[91m'
END = '\033[0m'
UNDERLINE = '\033[4m'
# AI models
modellist = [
["Load a model from its directory", "NeoCustom", ""],
["Load an old GPT-2 model (eg CloverEdition)", "GPT2Custom", ""],
["GPT-Neo 1.3B", "EleutherAI/gpt-neo-1.3B", "8GB"],
["GPT-Neo 2.7B", "EleutherAI/gpt-neo-2.7B", "16GB"],
["GPT-J 6B (HF GIT Required)", "EleutherAI/gpt-j-6B", "24GB"],
["GPT-2", "gpt2", "1GB"],
["GPT-2 Med", "gpt2-medium", "2GB"],
["GPT-2 Large", "gpt2-large", "4GB"],
["GPT-2 XL", "gpt2-xl", "8GB"],
["InferKit API (requires API key)", "InferKit", ""],
["Google Colab", "Colab", ""],
["OpenAI API (requires API key)", "OAI", ""],
["Read Only (No AI)", "ReadOnly", ""]
]
# Variables
class vars:
lastact = "" # The last action received from the user
lastctx = "" # The last context submitted to the generator
model = "" # Model ID string chosen at startup
noai = False # Runs the script without starting up the transformers pipeline
aibusy = False # Stops submissions while the AI is working
max_length = 1024 # Maximum number of tokens to submit per action
ikmax = 3000 # Maximum number of characters to submit to InferKit
genamt = 80 # Amount of text for each action to generate
ikgen = 200 # Number of characters for InferKit to generate
rep_pen = 1.1 # Default generator repetition_penalty
temp = 0.5 # Default generator temperature
top_p = 0.9 # Default generator top_p
top_k = 0 # Default generator top_k
tfs = 1.0 # Default generator tfs (tail-free sampling)
numseqs = 1 # Number of sequences to ask the generator to create
gamestarted = False # Whether the game has started (disables UI elements)
prompt = "" # Prompt
memory = "" # Text submitted to memory field
authornote = "" # Text submitted to Author's Note field
andepth = 3 # How far back in history to append author's note
actions = structures.KoboldStoryRegister() # Actions submitted by user and AI
worldinfo = [] # Array of World Info key/value objects
# badwords = [] # Array of str/chr values that should be removed from output
badwordsids = [[13460], [6880], [50256], [42496], [4613], [17414], [22039], [16410], [27], [29], [38430], [37922], [15913], [24618], [28725], [58], [47175], [36937], [26700], [12878], [16471], [37981], [5218], [29795], [13412], [45160], [3693], [49778], [4211], [20598], [36475], [33409], [44167], [32406], [29847], [29342], [42669], [685], [25787], [7359], [3784], [5320], [33994], [33490], [34516], [43734], [17635], [24293], [9959], [23785], [21737], [28401], [18161], [26358], [32509], [1279], [38155], [18189], [26894], [6927], [14610], [23834], [11037], [14631], [26933], [46904], [22330], [25915], [47934], [38214], [1875], [14692], [41832], [13163], [25970], [29565], [44926], [19841], [37250], [49029], [9609], [44438], [16791], [17816], [30109], [41888], [47527], [42924], [23984], [49074], [33717], [31161], [49082], [30138], [31175], [12240], [14804], [7131], [26076], [33250], [3556], [38381], [36338], [32756], [46581], [17912], [49146]] # Tokenized array of badwords used to prevent AI artifacting
deletewi = -1 # Temporary storage for index to delete
wirmvwhtsp = False # Whether to remove leading whitespace from WI entries
widepth = 3 # How many historical actions to scan for WI hits
mode = "play" # Whether the interface is in play, memory, or edit mode
editln = 0 # Which line was last selected in Edit Mode
url = "https://api.inferkit.com/v1/models/standard/generate" # InferKit API URL
oaiurl = "" # OpenAI API URL
oaiengines = "https://api.openai.com/v1/engines"
colaburl = "" # Ngrok url for Google Colab mode
apikey = "" # API key to use for InferKit API calls
oaiapikey = "" # API key to use for OpenAI API calls
savedir = getcwd()+"\stories"
hascuda = False # Whether torch has detected CUDA on the system
usegpu = False # Whether to launch pipeline with GPU support
custmodpth = "" # Filesystem location of custom model to run
formatoptns = {'frmttriminc': True, 'frmtrmblln': False, 'frmtrmspch': False, 'frmtadsnsp': False, 'singleline': False} # Container for state of formatting options
importnum = -1 # Selection on import popup list
importjs = {} # Temporary storage for import data
loadselect = "" # Temporary storage for story filename to load
spselect = "" # Temporary storage for soft prompt filename to load
sp = None # Current soft prompt tensor (as a NumPy array)
sp_length = 0 # Length of current soft prompt in tokens, or 0 if not using a soft prompt
svowname = "" # Filename that was flagged for overwrite confirm
saveow = False # Whether or not overwrite confirm has been displayed
genseqs = [] # Temporary storage for generated sequences
recentback = False # Whether Back button was recently used without Submitting or Retrying after
useprompt = False # Whether to send the full prompt with every submit action
breakmodel = False # For GPU users, whether to use both system RAM and VRAM to conserve VRAM while offering speedup compared to CPU-only
bmsupported = False # Whether the breakmodel option is supported (GPT-Neo/GPT-J only, currently)
smandelete = False # Whether stories can be deleted from inside the browser
smanrename = False # Whether stories can be renamed from inside the browser
allowsp = False # Whether we are allowed to use soft prompts (by default enabled if we're using GPT-2, GPT-Neo or GPT-J)
modeldim = -1 # Embedding dimension of your model (e.g. it's 4096 for GPT-J-6B and 2560 for GPT-Neo-2.7B)
laststory = None # Filename (without extension) of most recent story JSON file we loaded
regex_sl = re.compile(r'\n*(?<=.) *\n(.|\n)*') # Pattern for limiting the output to a single line
acregex_ai = re.compile(r'\n* *>(.|\n)*') # Pattern for matching adventure actions from the AI so we can remove them
acregex_ui = re.compile(r'^ *(>.*)$', re.MULTILINE) # Pattern for matching actions in the HTML-escaped story so we can apply colouring, etc (make sure to encase part to format in parentheses)
comregex_ai = re.compile(r'(?:\n<\|(?:.|\n)*?\|>(?=\n|$))|(?:<\|(?:.|\n)*?\|>\n?)') # Pattern for matching comments to remove them before sending them to the AI
comregex_ui = re.compile(r'(<\|(?:.|\n)*?\|>)') # Pattern for matching comments in the editor
actionmode = 1
adventure = False
dynamicscan = False
remote = False
#==================================================================#
# Function to get model selection at startup
#==================================================================#
def getModelSelection():
print(" # Model V/RAM\n =========================================")
i = 1
for m in modellist:
print(" {0} - {1}\t\t{2}".format("{:<2}".format(i), m[0].ljust(15), m[2]))
i += 1
print(" ");
modelsel = 0
vars.model = ''
while(vars.model == ''):
modelsel = input("Model #> ")
if(modelsel.isnumeric() and int(modelsel) > 0 and int(modelsel) <= len(modellist)):
vars.model = modellist[int(modelsel)-1][1]
else:
print("{0}Please enter a valid selection.{1}".format(colors.RED, colors.END))
# If custom model was selected, get the filesystem location and store it
if(vars.model == "NeoCustom" or vars.model == "GPT2Custom"):
print("{0}Please choose the folder where pytorch_model.bin is located:{1}\n".format(colors.CYAN, colors.END))
modpath = fileops.getdirpath(getcwd(), "Select Model Folder")
if(modpath):
# Save directory to vars
vars.custmodpth = modpath
else:
# Print error and retry model selection
print("{0}Model select cancelled!{1}".format(colors.RED, colors.END))
print("{0}Select an AI model to continue:{1}\n".format(colors.CYAN, colors.END))
getModelSelection()
#==================================================================#
# Return all keys in tokenizer dictionary containing char
#==================================================================#
def gettokenids(char):
keys = []
for key in vocab_keys:
if(key.find(char) != -1):
keys.append(key)
return keys
#==================================================================#
# Return Model Name
#==================================================================#
def getmodelname():
if(args.configname):
modelname = args.configname
return modelname
if(vars.model in ("NeoCustom", "GPT2Custom", "TPUMeshTransformerGPTJ")):
modelname = os.path.basename(os.path.normpath(vars.custmodpth))
return modelname
else:
modelname = vars.model
return modelname
#==================================================================#
# Breakmodel configuration functions
#==================================================================#
def device_list(n_layers, primary=None, selected=None):
device_count = torch.cuda.device_count()
if(device_count < 2):
primary = None
gpu_blocks = breakmodel.gpu_blocks + (device_count - len(breakmodel.gpu_blocks))*[0]
print(f"{colors.YELLOW} DEVICE ID | LAYERS | DEVICE NAME{colors.END}")
for i in range(device_count):
name = torch.cuda.get_device_name(i)
if(len(name) > 47):
name = "..." + name[-44:]
row_color = colors.END
sep_color = colors.YELLOW
print(f"{row_color}{colors.YELLOW + '->' + row_color if i == selected else ' '} {'(primary)' if i == primary else ' '*9} {i:3} {sep_color}|{row_color} {gpu_blocks[i]:3} {sep_color}|{row_color} {name}{colors.END}")
row_color = colors.END
sep_color = colors.YELLOW
print(f"{row_color} {' '*9} N/A {sep_color}|{row_color} {n_layers:3} {sep_color}|{row_color} (CPU){colors.END}")
def device_config(model):
global breakmodel, generator
import breakmodel
n_layers = model.config.num_layers if hasattr(model.config, "num_layers") else model.config.n_layer
if(args.breakmodel_gpulayers is not None):
try:
breakmodel.gpu_blocks = list(map(int, args.breakmodel_gpulayers.split(',')))
assert len(breakmodel.gpu_blocks) <= torch.cuda.device_count()
assert sum(breakmodel.gpu_blocks) <= n_layers
n_layers -= sum(breakmodel.gpu_blocks)
except:
print("WARNING: --layers is malformatted. Please use the --help option to see correct usage of --layers. Defaulting to all layers on device 0.", file=sys.stderr)
breakmodel.gpu_blocks = [n_layers]
n_layers = 0
elif(args.breakmodel_layers is not None):
breakmodel.gpu_blocks = [n_layers - max(0, min(n_layers, args.breakmodel_layers))]
n_layers -= sum(breakmodel.gpu_blocks)
elif(args.model is not None):
print("Breakmodel not specified, assuming GPU 0")
breakmodel.gpu_blocks = [n_layers]
n_layers = 0
else:
device_count = torch.cuda.device_count()
if(device_count > 1):
print(colors.CYAN + "\nPlease select one of your GPUs to be your primary GPU.")
print("VRAM usage in your primary GPU will be higher than for your other ones.")
print("It is recommended you make your fastest GPU your primary GPU.")
device_list(n_layers)
while(True):
primaryselect = input("device ID> ")
if(primaryselect.isnumeric() and 0 <= int(primaryselect) < device_count):
breakmodel.primary_device = int(primaryselect)
break
else:
print(f"{colors.RED}Please enter an integer between 0 and {device_count-1}.{colors.END}")
else:
breakmodel.primary_device = 0
print(colors.PURPLE + "\nIf you don't have enough VRAM to run the model on a single GPU")
print("you can split the model between your CPU and your GPU(s), or between")
print("multiple GPUs if you have more than one.")
print("By putting more 'layers' on a GPU or CPU, more computations will be")
print("done on that device and more VRAM or RAM will be required on that device")
print("(roughly proportional to number of layers).")
print("It should be noted that GPUs are orders of magnitude faster than the CPU.")
print(f"This model has{colors.YELLOW} {n_layers} {colors.PURPLE}layers.{colors.END}\n")
for i in range(device_count):
device_list(n_layers, primary=breakmodel.primary_device, selected=i)
print(f"{colors.CYAN}\nHow many of the remaining{colors.YELLOW} {n_layers} {colors.CYAN}layers would you like to put into device {i}?\nYou can also enter -1 to allocate all remaining layers to this device.{colors.END}\n")
while(True):
layerselect = input("# of layers> ")
if((layerselect.isnumeric() or layerselect.strip() == '-1') and -1 <= int(layerselect) <= n_layers):
layerselect = int(layerselect)
layerselect = n_layers if layerselect == -1 else layerselect
breakmodel.gpu_blocks.append(layerselect)
n_layers -= layerselect
break
else:
print(f"{colors.RED}Please enter an integer between -1 and {n_layers}.{colors.END}")
if(n_layers == 0):
break
print(colors.PURPLE + "\nFinal device configuration:")
device_list(n_layers)
# If all layers are on the same device, use the old GPU generation mode
while(len(breakmodel.gpu_blocks) and breakmodel.gpu_blocks[-1] == 0):
breakmodel.gpu_blocks.pop()
if(len(breakmodel.gpu_blocks) and breakmodel.gpu_blocks[-1] in (-1, model.config.num_layers if hasattr(model.config, "num_layers") else model.config.n_layer)):
vars.breakmodel = False
vars.usegpu = True
model = model.half().to(len(breakmodel.gpu_blocks)-1)
generator = model.generate
return
if(not breakmodel.gpu_blocks):
print("Nothing assigned to a GPU, reverting to CPU only mode")
vars.breakmodel = False
vars.usegpu = False
model = model.to('cpu')
generator = model.generate
return
model.half().to('cpu')
gc.collect()
model.transformer.wte.to(breakmodel.primary_device)
model.transformer.ln_f.to(breakmodel.primary_device)
if(hasattr(model, 'lm_head')):
model.lm_head.to(breakmodel.primary_device)
if(hasattr(model.transformer, 'wpe')):
model.transformer.wpe.to(breakmodel.primary_device)
gc.collect()
GPTNeoModel.forward = breakmodel.new_forward
if("GPTJModel" in globals()):
GPTJModel.forward = breakmodel.new_forward
generator = model.generate
breakmodel.move_hidden_layers(model.transformer)
#==================================================================#
# Startup
#==================================================================#
# Parsing Parameters
parser = argparse.ArgumentParser(description="KoboldAI Server")
parser.add_argument("--remote", action='store_true', help="Optimizes KoboldAI for Remote Play")
parser.add_argument("--model", help="Specify the Model Type to skip the Menu")
parser.add_argument("--path", help="Specify the Path for local models (For model NeoCustom or GPT2Custom)")
parser.add_argument("--cpu", action='store_true', help="By default unattended launches are on the GPU use this option to force CPU usage.")
parser.add_argument("--breakmodel", action='store_true', help=argparse.SUPPRESS)
parser.add_argument("--breakmodel_layers", type=int, help=argparse.SUPPRESS)
parser.add_argument("--breakmodel_gpulayers", type=str, help="If using a model that supports hybrid generation, this is a comma-separated list that specifies how many layers to put on each GPU device. For example to put 8 layers on device 0, 9 layers on device 1 and 11 layers on device 2, use --layers 8,9,11")
parser.add_argument("--override_delete", action='store_true', help="Deleting stories from inside the browser is disabled if you are using --remote and enabled otherwise. Using this option will instead allow deleting stories if using --remote and prevent deleting stories otherwise.")
parser.add_argument("--override_rename", action='store_true', help="Renaming stories from inside the browser is disabled if you are using --remote and enabled otherwise. Using this option will instead allow renaming stories if using --remote and prevent renaming stories otherwise.")
parser.add_argument("--configname", help="Force a fixed configuration name to aid with config management.")
args = parser.parse_args()
vars.model = args.model;
if args.remote:
vars.remote = True;
vars.smandelete = vars.remote == args.override_delete
vars.smanrename = vars.remote == args.override_rename
# Select a model to run
if args.model:
print("Welcome to KoboldAI!\nYou have selected the following Model:", vars.model)
if args.path:
print("You have selected the following path for your Model :", args.path)
vars.custmodpth = args.path;
vars.colaburl = args.path + "/request"; # Lets just use the same parameter to keep it simple
else:
print("{0}Welcome to the KoboldAI Server!\nSelect an AI model to continue:{1}\n".format(colors.CYAN, colors.END))
getModelSelection()
# If transformers model was selected & GPU available, ask to use CPU or GPU
if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransformerGPTJ"]):
vars.allowsp = True
# Test for GPU support
import torch
print("{0}Looking for GPU support...{1}".format(colors.PURPLE, colors.END), end="")
vars.hascuda = torch.cuda.is_available()
vars.bmsupported = vars.model in ("EleutherAI/gpt-neo-1.3B", "EleutherAI/gpt-neo-2.7B", "NeoCustom")
if(args.breakmodel is not None and args.breakmodel):
print("WARNING: --breakmodel is no longer supported. Breakmodel mode is now automatically enabled when --layers is used (see --help for details).", file=sys.stderr)
if(args.breakmodel_layers is not None):
print("WARNING: --breakmodel_layers is deprecated. Use --layers instead (see --help for details).", file=sys.stderr)
if(not vars.bmsupported and (args.breakmodel_gpulayers is not None or args.breakmodel_layers is not None)):
print("WARNING: This model does not support hybrid generation. --layers will be ignored.", file=sys.stderr)
if(vars.hascuda):
print("{0}FOUND!{1}".format(colors.GREEN, colors.END))
else:
print("{0}NOT FOUND!{1}".format(colors.YELLOW, colors.END))
if args.model:
if(vars.hascuda):
genselected = True
vars.usegpu = True
vars.breakmodel = False
if(vars.bmsupported):
vars.usegpu = False
vars.breakmodel = True
if(args.cpu):
vars.usegpu = False
vars.breakmodel = False
elif(vars.hascuda):
if(vars.bmsupported):
genselected = True
vars.usegpu = False
vars.breakmodel = True
else:
print(" 1 - GPU\n 2 - CPU\n")
genselected = False
else:
genselected = False
if(vars.hascuda):
while(genselected == False):
genselect = input("Mode> ")
if(genselect == ""):
vars.breakmodel = False
vars.usegpu = True
genselected = True
elif(genselect.isnumeric() and int(genselect) == 1):
if(vars.bmsupported):
vars.breakmodel = True
vars.usegpu = False
genselected = True
else:
vars.breakmodel = False
vars.usegpu = True
genselected = True
elif(genselect.isnumeric() and int(genselect) == 2):
vars.breakmodel = False
vars.usegpu = False
genselected = True
else:
print("{0}Please enter a valid selection.{1}".format(colors.RED, colors.END))
# Ask for API key if InferKit was selected
if(vars.model == "InferKit"):
if(not path.exists("settings/" + getmodelname().replace('/', '_') + ".settings")):
# If the client settings file doesn't exist, create it
print("{0}Please enter your InferKit API key:{1}\n".format(colors.CYAN, colors.END))
vars.apikey = input("Key> ")
# Write API key to file
os.makedirs('settings', exist_ok=True)
file = open("settings/" + getmodelname().replace('/', '_') + ".settings", "w")
try:
js = {"apikey": vars.apikey}
file.write(json.dumps(js, indent=3))
finally:
file.close()
else:
# Otherwise open it up
file = open("settings/" + getmodelname().replace('/', '_') + ".settings", "r")
# Check if API key exists
js = json.load(file)
if("apikey" in js and js["apikey"] != ""):
# API key exists, grab it and close the file
vars.apikey = js["apikey"]
file.close()
else:
# Get API key, add it to settings object, and write it to disk
print("{0}Please enter your InferKit API key:{1}\n".format(colors.CYAN, colors.END))
vars.apikey = input("Key> ")
js["apikey"] = vars.apikey
# Write API key to file
file = open("settings/" + getmodelname().replace('/', '_') + ".settings", "w")
try:
file.write(json.dumps(js, indent=3))
finally:
file.close()
# Ask for API key if OpenAI was selected
if(vars.model == "OAI"):
if(not path.exists("settings/" + getmodelname().replace('/', '_') + ".settings")):
# If the client settings file doesn't exist, create it
print("{0}Please enter your OpenAI API key:{1}\n".format(colors.CYAN, colors.END))
vars.oaiapikey = input("Key> ")
# Write API key to file
os.makedirs('settings', exist_ok=True)
file = open("settings/" + getmodelname().replace('/', '_') + ".settings", "w")
try:
js = {"oaiapikey": vars.oaiapikey}
file.write(json.dumps(js, indent=3))
finally:
file.close()
else:
# Otherwise open it up
file = open("settings/" + getmodelname().replace('/', '_') + ".settings", "r")
# Check if API key exists
js = json.load(file)
if("oaiapikey" in js and js["oaiapikey"] != ""):
# API key exists, grab it and close the file
vars.oaiapikey = js["oaiapikey"]
file.close()
else:
# Get API key, add it to settings object, and write it to disk
print("{0}Please enter your OpenAI API key:{1}\n".format(colors.CYAN, colors.END))
vars.oaiapikey = input("Key> ")
js["oaiapikey"] = vars.oaiapikey
# Write API key to file
file = open("settings/" + getmodelname().replace('/', '_') + ".settings", "w")
try:
file.write(json.dumps(js, indent=3))
finally:
file.close()
# Get list of models from OAI
print("{0}Retrieving engine list...{1}".format(colors.PURPLE, colors.END), end="")
req = requests.get(
vars.oaiengines,
headers = {
'Authorization': 'Bearer '+vars.oaiapikey
}
)
if(req.status_code == 200):
print("{0}OK!{1}".format(colors.GREEN, colors.END))
print("{0}Please select an engine to use:{1}\n".format(colors.CYAN, colors.END))
engines = req.json()["data"]
# Print list of engines
i = 0
for en in engines:
print(" {0} - {1} ({2})".format(i, en["id"], "\033[92mready\033[0m" if en["ready"] == True else "\033[91mnot ready\033[0m"))
i += 1
# Get engine to use
print("")
engselected = False
while(engselected == False):
engine = input("Engine #> ")
if(engine.isnumeric() and int(engine) < len(engines)):
vars.oaiurl = "https://api.openai.com/v1/engines/{0}/completions".format(engines[int(engine)]["id"])
engselected = True
else:
print("{0}Please enter a valid selection.{1}".format(colors.RED, colors.END))
else:
# Something went wrong, print the message and quit since we can't initialize an engine
print("{0}ERROR!{1}".format(colors.RED, colors.END))
print(req.json())
quit()
# Ask for ngrok url if Google Colab was selected
if(vars.model == "Colab"):
if(vars.colaburl == ""):
print("{0}Please enter the ngrok.io or trycloudflare.com URL displayed in Google Colab:{1}\n".format(colors.CYAN, colors.END))
vars.colaburl = input("URL> ") + "/request"
if(vars.model == "ReadOnly"):
vars.noai = True
# Set logging level to reduce chatter from Flask
import logging
log = logging.getLogger('werkzeug')
log.setLevel(logging.ERROR)
# Start flask & SocketIO
print("{0}Initializing Flask... {1}".format(colors.PURPLE, colors.END), end="")
from flask import Flask, render_template, Response, request
from flask_socketio import SocketIO, emit
app = Flask(__name__)
app.config['SECRET KEY'] = 'secret!'
socketio = SocketIO(app)
print("{0}OK!{1}".format(colors.GREEN, colors.END))
# Start transformers and create pipeline
if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransformerGPTJ"]):
if(not vars.noai):
print("{0}Initializing transformers, please wait...{1}".format(colors.PURPLE, colors.END))
from transformers import StoppingCriteria, GPT2Tokenizer, GPT2LMHeadModel, GPTNeoForCausalLM, GPTNeoModel, AutoModelForCausalLM
import transformers.generation_utils
from transformers import __version__ as transformers_version
# Patch transformers to use our soft prompt
def patch_causallm(cls):
old_forward = cls.forward
def new_causallm_forward(self, *args, **kwargs):
input_ids = kwargs.get('input_ids').to(self.device)
assert input_ids is not None
kwargs['input_ids'] = None
if(vars.sp is not None):
shifted_input_ids = input_ids - self.config.vocab_size
input_ids.clamp_(max=self.config.vocab_size-1)
inputs_embeds = self.transformer.wte(input_ids)
if(vars.sp is not None):
vars.sp = vars.sp.to(inputs_embeds.dtype).to(inputs_embeds.device)
inputs_embeds = torch.where(
(shifted_input_ids >= 0)[..., None],
vars.sp[shifted_input_ids.clamp(min=0)],
inputs_embeds,
)
kwargs['inputs_embeds'] = inputs_embeds
return old_forward(self, *args, **kwargs)
cls.forward = new_causallm_forward
for cls in (GPT2LMHeadModel, GPTNeoForCausalLM):
patch_causallm(cls)
try:
from transformers import GPTJForCausalLM
patch_causallm(GPTJForCausalLM)
except:
pass
# Patch transformers to use our custom logit warpers
from transformers import LogitsProcessorList, LogitsWarper, TopKLogitsWarper, TopPLogitsWarper, TemperatureLogitsWarper
class TailFreeLogitsWarper(LogitsWarper):
def __init__(self, tfs: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
tfs = float(tfs)
if tfs < 0 or tfs > 1.0:
raise ValueError(f"`tfs` has to be a float > 0 and < 1, but is {tfs}")
self.tfs = tfs
self.filter_value = filter_value
self.min_tokens_to_keep = min_tokens_to_keep
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
if self.filter_value >= 1.0:
return scores
sorted_logits, sorted_indices = torch.sort(scores, descending=True)
probs = sorted_logits.softmax(dim=-1)
# Compute second derivative normalized CDF
d2 = probs.diff().diff().abs()
normalized_d2 = d2 / d2.sum(dim=-1, keepdim=True)
normalized_d2_cdf = normalized_d2.cumsum(dim=-1)
# Remove tokens with CDF value above the threshold (token with 0 are kept)
sorted_indices_to_remove = normalized_d2_cdf > self.tfs
# Centre the distribution around the cutoff as in the original implementation of the algorithm
sorted_indices_to_remove = torch.cat(
(
torch.zeros(scores.shape[0], 1, dtype=torch.bool, device=scores.device),
sorted_indices_to_remove,
torch.ones(scores.shape[0], 1, dtype=torch.bool, device=scores.device),
),
dim=-1,
)
if self.min_tokens_to_keep > 1:
# Keep at least min_tokens_to_keep
sorted_indices_to_remove[..., : self.min_tokens_to_keep] = 0
indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
scores = scores.masked_fill(indices_to_remove, self.filter_value)
return scores
def new_get_logits_warper(
top_k: int = None,
top_p: float = None,
tfs: float = None,
temp: float = None,
beams: int = 1,
) -> LogitsProcessorList:
warper_list = LogitsProcessorList()
if(top_k is not None and top_k > 0):
warper_list.append(TopKLogitsWarper(top_k=top_k, min_tokens_to_keep=1 + (beams > 1)))
if(top_p is not None and top_p < 1.0):
warper_list.append(TopPLogitsWarper(top_p=top_p, min_tokens_to_keep=1 + (beams > 1)))
if(tfs is not None and tfs < 1.0):
warper_list.append(TailFreeLogitsWarper(tfs=tfs, min_tokens_to_keep=1 + (beams > 1)))
if(temp is not None and temp != 1.0):
warper_list.append(TemperatureLogitsWarper(temperature=temp))
return warper_list
def new_sample(self, *args, **kwargs):
assert kwargs.pop("logits_warper", None) is not None
kwargs["logits_warper"] = new_get_logits_warper(
vars.top_k,
vars.top_p,
vars.tfs,
vars.temp,
1,
)
return new_sample.old_sample(self, *args, **kwargs)
new_sample.old_sample = transformers.generation_utils.GenerationMixin.sample
transformers.generation_utils.GenerationMixin.sample = new_sample
# Sets up dynamic world info scanner
class DynamicWorldInfoScanCriteria(StoppingCriteria):
def __init__(
self,
tokenizer,
excluded_world_info: List[Set],
head_length: int,
):
self.any_new_entries = False
self.tokenizer = tokenizer
self.excluded_world_info = excluded_world_info
self.head_length = head_length
def __call__(
self,
input_ids: torch.LongTensor,
scores: torch.FloatTensor,
**kwargs,
) -> bool:
assert input_ids.ndim == 2
assert len(self.excluded_world_info) == input_ids.shape[0]
self.any_new_entries = False
if(not vars.dynamicscan):
return False
tail = input_ids[..., self.head_length:]
for i, t in enumerate(tail):
decoded = tokenizer.decode(t)
_, found = checkworldinfo(decoded, force_use_txt=True)
found -= self.excluded_world_info[i]
if(len(found) != 0):
self.any_new_entries = True
break
return self.any_new_entries
old_get_stopping_criteria = transformers.generation_utils.GenerationMixin._get_stopping_criteria
def new_get_stopping_criteria(self, *args, **kwargs):
stopping_criteria = old_get_stopping_criteria(self, *args, **kwargs)
global tokenizer
self.kai_scanner = DynamicWorldInfoScanCriteria(
tokenizer=tokenizer,
excluded_world_info=self.kai_scanner_excluded_world_info,
head_length=self.kai_scanner_head_length,
)
stopping_criteria.append(self.kai_scanner)
return stopping_criteria
transformers.generation_utils.GenerationMixin._get_stopping_criteria = new_get_stopping_criteria
def get_hidden_size_from_model(model):
try:
return int(model.transformer.hidden_size)
except:
try:
return int(model.transformer.embed_dim)
except:
return int(model.lm_head.in_features)
def maybe_low_cpu_mem_usage() -> Dict[str, Any]:
if(packaging.version.parse(transformers_version) < packaging.version.parse("4.11.0")):
print(f"\nWARNING: Please upgrade to transformers 4.11.0 for lower RAM usage. You have transformers {transformers_version}.", file=sys.stderr)
return {}
return {"low_cpu_mem_usage": True}
# If custom GPT Neo model was chosen
if(vars.model == "NeoCustom"):
model_config = open(vars.custmodpth + "/config.json", "r")
js = json.load(model_config)
if("model_type" in js):
model = AutoModelForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache/", **maybe_low_cpu_mem_usage())
else:
model = GPTNeoForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model)
tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth, cache_dir="cache/")
# Is CUDA available? If so, use GPU, otherwise fall back to CPU
if(vars.hascuda):
if(vars.usegpu):
model = model.half().to(0)
generator = model.generate
elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel)
device_config(model)
else:
generator = model.generate
else:
generator = model.generate
# If custom GPT2 model was chosen
elif(vars.model == "GPT2Custom"):
model_config = open(vars.custmodpth + "/config.json", "r")
js = json.load(model_config)
model = GPT2LMHeadModel.from_pretrained(vars.custmodpth, cache_dir="cache/", **maybe_low_cpu_mem_usage())
tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model)
# Is CUDA available? If so, use GPU, otherwise fall back to CPU
if(vars.hascuda and vars.usegpu):
model = model.half().to(0)
generator = model.generate
else:
generator = model.generate
# If base HuggingFace model was chosen
else:
# Is CUDA available? If so, use GPU, otherwise fall back to CPU
tokenizer = GPT2Tokenizer.from_pretrained(vars.model, cache_dir="cache/")
if(vars.hascuda):
if(vars.usegpu):
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model)
model = model.half().to(0)
generator = model.generate
elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel)
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model)
device_config(model)
else:
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model)
generator = model.generate
else:
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model)
generator = model.generate
# Suppress Author's Note by flagging square brackets (Old implementation)
#vocab = tokenizer.get_vocab()
#vocab_keys = vocab.keys()
#vars.badwords = gettokenids("[")
#for key in vars.badwords:
# vars.badwordsids.append([vocab[key]])
print("{0}OK! {1} pipeline created!{2}".format(colors.GREEN, vars.model, colors.END))
else:
# If we're running Colab or OAI, we still need a tokenizer.
if(vars.model == "Colab"):
from transformers import GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
elif(vars.model == "OAI"):
from transformers import GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
# Load the TPU backend if requested
elif(vars.model == "TPUMeshTransformerGPTJ"):
print("{0}Initializing Mesh Transformer JAX, please wait...{1}".format(colors.PURPLE, colors.END))
assert vars.model == "TPUMeshTransformerGPTJ" and vars.custmodpth and os.path.isdir(vars.custmodpth)
import tpu_mtj_backend
tpu_mtj_backend.load_model(vars.custmodpth)
vars.allowsp = True
vars.modeldim = int(tpu_mtj_backend.params["d_model"])
tokenizer = tpu_mtj_backend.tokenizer
# Set up Flask routes
@app.route('/')
@app.route('/index')
def index():
return render_template('index.html')
@app.route('/download')
def download():
save_format = request.args.get("format", "json").strip().lower()
if(save_format == "plaintext"):
txt = vars.prompt + "".join(vars.actions.values())
save = Response(txt)
filename = path.basename(vars.savedir)
if filename[-5:] == ".json":
filename = filename[:-5]
save.headers.set('Content-Disposition', 'attachment', filename='%s.txt' % filename)
return(save)
# Build json to write
js = {}
js["gamestarted"] = vars.gamestarted
js["prompt"] = vars.prompt
js["memory"] = vars.memory
js["authorsnote"] = vars.authornote
js["actions"] = tuple(vars.actions.values())
js["worldinfo"] = []
# Extract only the important bits of WI
for wi in vars.worldinfo:
if(wi["constant"] or wi["key"] != ""):
js["worldinfo"].append({
"key": wi["key"],
"keysecondary": wi["keysecondary"],
"content": wi["content"],
"selective": wi["selective"],
"constant": wi["constant"]
})
save = Response(json.dumps(js, indent=3))
filename = path.basename(vars.savedir)
if filename[-5:] == ".json":
filename = filename[:-5]
save.headers.set('Content-Disposition', 'attachment', filename='%s.json' % filename)
return(save)
#============================ METHODS =============================#
#==================================================================#
# Event triggered when browser SocketIO is loaded and connects to server
#==================================================================#
@socketio.on('connect')
def do_connect():
print("{0}Client connected!{1}".format(colors.GREEN, colors.END))
emit('from_server', {'cmd': 'connected', 'smandelete': vars.smandelete, 'smanrename': vars.smanrename})
if(vars.remote):
emit('from_server', {'cmd': 'runs_remotely'})
if(vars.allowsp):
emit('from_server', {'cmd': 'allowsp', 'data': vars.allowsp})
if(not vars.gamestarted):
setStartState()
sendsettings()
refresh_settings()
vars.laststory = None
emit('from_server', {'cmd': 'setstoryname', 'data': vars.laststory})
sendwi()
emit('from_server', {'cmd': 'setmemory', 'data': vars.memory})
emit('from_server', {'cmd': 'setanote', 'data': vars.authornote})
vars.mode = "play"
else:
# Game in session, send current game data and ready state to browser
refresh_story()
sendsettings()
refresh_settings()
emit('from_server', {'cmd': 'setstoryname', 'data': vars.laststory})
sendwi()
emit('from_server', {'cmd': 'setmemory', 'data': vars.memory})
emit('from_server', {'cmd': 'setanote', 'data': vars.authornote})
if(vars.mode == "play"):
if(not vars.aibusy):
emit('from_server', {'cmd': 'setgamestate', 'data': 'ready'})
else:
emit('from_server', {'cmd': 'setgamestate', 'data': 'wait'})
elif(vars.mode == "edit"):
emit('from_server', {'cmd': 'editmode', 'data': 'true'})
elif(vars.mode == "memory"):
emit('from_server', {'cmd': 'memmode', 'data': 'true'})
elif(vars.mode == "wi"):
emit('from_server', {'cmd': 'wimode', 'data': 'true'})
#==================================================================#
# Event triggered when browser SocketIO sends data to the server
#==================================================================#
@socketio.on('message')
def get_message(msg):
print("{0}Data received:{1}{2}".format(colors.GREEN, msg, colors.END))
# Submit action
if(msg['cmd'] == 'submit'):
if(vars.mode == "play"):
actionsubmit(msg['data'], actionmode=msg['actionmode'])
elif(vars.mode == "edit"):
editsubmit(msg['data'])
elif(vars.mode == "memory"):
memsubmit(msg['data'])
# Retry Action
elif(msg['cmd'] == 'retry'):
actionretry(msg['data'])
# Back/Undo Action
elif(msg['cmd'] == 'back'):
actionback()
# EditMode Action (old)
elif(msg['cmd'] == 'edit'):
if(vars.mode == "play"):
vars.mode = "edit"
emit('from_server', {'cmd': 'editmode', 'data': 'true'}, broadcast=True)
elif(vars.mode == "edit"):
vars.mode = "play"
emit('from_server', {'cmd': 'editmode', 'data': 'false'}, broadcast=True)
# EditLine Action (old)
elif(msg['cmd'] == 'editline'):
editrequest(int(msg['data']))
# Inline edit
elif(msg['cmd'] == 'inlineedit'):
inlineedit(msg['chunk'], msg['data'])
elif(msg['cmd'] == 'inlinedelete'):
inlinedelete(msg['data'])
# DeleteLine Action (old)
elif(msg['cmd'] == 'delete'):
deleterequest()
elif(msg['cmd'] == 'memory'):
togglememorymode()
elif(not vars.remote and msg['cmd'] == 'savetofile'):
savetofile()
elif(not vars.remote and msg['cmd'] == 'loadfromfile'):
loadfromfile()
elif(msg['cmd'] == 'loadfromstring'):
loadRequest(json.loads(msg['data']), filename=msg['filename'])
elif(not vars.remote and msg['cmd'] == 'import'):
importRequest()
elif(msg['cmd'] == 'newgame'):
newGameRequest()
elif(msg['cmd'] == 'rndgame'):
randomGameRequest(msg['data'])
elif(msg['cmd'] == 'settemp'):
vars.temp = float(msg['data'])
emit('from_server', {'cmd': 'setlabeltemp', 'data': msg['data']}, broadcast=True)
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'settopp'):
vars.top_p = float(msg['data'])
emit('from_server', {'cmd': 'setlabeltopp', 'data': msg['data']}, broadcast=True)
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'settopk'):
vars.top_k = int(msg['data'])
emit('from_server', {'cmd': 'setlabeltopk', 'data': msg['data']}, broadcast=True)
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'settfs'):
vars.tfs = float(msg['data'])
emit('from_server', {'cmd': 'setlabeltfs', 'data': msg['data']}, broadcast=True)
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setreppen'):
vars.rep_pen = float(msg['data'])
emit('from_server', {'cmd': 'setlabelreppen', 'data': msg['data']}, broadcast=True)
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setoutput'):
vars.genamt = int(msg['data'])
emit('from_server', {'cmd': 'setlabeloutput', 'data': msg['data']}, broadcast=True)
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'settknmax'):
vars.max_length = int(msg['data'])
emit('from_server', {'cmd': 'setlabeltknmax', 'data': msg['data']}, broadcast=True)
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setikgen'):
vars.ikgen = int(msg['data'])
emit('from_server', {'cmd': 'setlabelikgen', 'data': msg['data']}, broadcast=True)
settingschanged()
refresh_settings()
# Author's Note field update
elif(msg['cmd'] == 'anote'):
anotesubmit(msg['data'])
# Author's Note depth update
elif(msg['cmd'] == 'anotedepth'):
vars.andepth = int(msg['data'])
emit('from_server', {'cmd': 'setlabelanotedepth', 'data': msg['data']}, broadcast=True)
settingschanged()
refresh_settings()
# Format - Trim incomplete sentences
elif(msg['cmd'] == 'frmttriminc'):
if('frmttriminc' in vars.formatoptns):
vars.formatoptns["frmttriminc"] = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'frmtrmblln'):
if('frmtrmblln' in vars.formatoptns):
vars.formatoptns["frmtrmblln"] = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'frmtrmspch'):
if('frmtrmspch' in vars.formatoptns):
vars.formatoptns["frmtrmspch"] = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'frmtadsnsp'):
if('frmtadsnsp' in vars.formatoptns):
vars.formatoptns["frmtadsnsp"] = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'singleline'):
if('singleline' in vars.formatoptns):
vars.formatoptns["singleline"] = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'importselect'):
vars.importnum = int(msg["data"].replace("import", ""))
elif(msg['cmd'] == 'importcancel'):
emit('from_server', {'cmd': 'popupshow', 'data': False})
vars.importjs = {}
elif(msg['cmd'] == 'importaccept'):
emit('from_server', {'cmd': 'popupshow', 'data': False})
importgame()
elif(msg['cmd'] == 'wi'):
togglewimode()
elif(msg['cmd'] == 'wiinit'):
if(int(msg['data']) < len(vars.worldinfo)):
vars.worldinfo[msg['data']]["init"] = True
addwiitem()
elif(msg['cmd'] == 'widelete'):
deletewi(msg['data'])
elif(msg['cmd'] == 'wiselon'):
vars.worldinfo[msg['data']]["selective"] = True
elif(msg['cmd'] == 'wiseloff'):
vars.worldinfo[msg['data']]["selective"] = False
elif(msg['cmd'] == 'wiconstanton'):
vars.worldinfo[msg['data']]["constant"] = True
elif(msg['cmd'] == 'wiconstantoff'):
vars.worldinfo[msg['data']]["constant"] = False
elif(msg['cmd'] == 'sendwilist'):
commitwi(msg['data'])
elif(msg['cmd'] == 'aidgimport'):
importAidgRequest(msg['data'])
elif(msg['cmd'] == 'saveasrequest'):
saveas(msg['data'])
elif(msg['cmd'] == 'saverequest'):
save()
elif(msg['cmd'] == 'loadlistrequest'):
getloadlist()
elif(msg['cmd'] == 'splistrequest'):
getsplist()
elif(msg['cmd'] == 'loadselect'):
vars.loadselect = msg["data"]
elif(msg['cmd'] == 'spselect'):
vars.spselect = msg["data"]
elif(msg['cmd'] == 'loadrequest'):
loadRequest(fileops.storypath(vars.loadselect))
elif(msg['cmd'] == 'sprequest'):
spRequest(vars.spselect)
elif(msg['cmd'] == 'deletestory'):
deletesave(msg['data'])
elif(msg['cmd'] == 'renamestory'):
renamesave(msg['data'], msg['newname'])
elif(msg['cmd'] == 'clearoverwrite'):
vars.svowname = ""
vars.saveow = False
elif(msg['cmd'] == 'seqsel'):
selectsequence(msg['data'])
elif(msg['cmd'] == 'setnumseq'):
vars.numseqs = int(msg['data'])
emit('from_server', {'cmd': 'setlabelnumseq', 'data': msg['data']})
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setwidepth'):
vars.widepth = int(msg['data'])
emit('from_server', {'cmd': 'setlabelwidepth', 'data': msg['data']})
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setuseprompt'):
vars.useprompt = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setadventure'):
vars.adventure = msg['data']
settingschanged()
refresh_settings()
elif(msg['cmd'] == 'setdynamicscan'):
vars.dynamicscan = msg['data']
settingschanged()
refresh_settings()
elif(not vars.remote and msg['cmd'] == 'importwi'):
wiimportrequest()
#==================================================================#
# Send start message and tell Javascript to set UI state
#==================================================================#
def setStartState():
txt = "Welcome to KoboldAI! You are running "+getmodelname()+".
"
if(not vars.noai):
txt = txt + "Please load a game or enter a prompt below to begin!"
else:
txt = txt + "Please load or import a story to read. There is no AI in this mode."
emit('from_server', {'cmd': 'updatescreen', 'gamestarted': vars.gamestarted, 'data': txt}, broadcast=True)
emit('from_server', {'cmd': 'setgamestate', 'data': 'start'}, broadcast=True)
#==================================================================#
# Transmit applicable settings to SocketIO to build UI sliders/toggles
#==================================================================#
def sendsettings():
# Send settings for selected AI type
if(vars.model != "InferKit"):
for set in gensettings.gensettingstf:
emit('from_server', {'cmd': 'addsetting', 'data': set})
else:
for set in gensettings.gensettingsik:
emit('from_server', {'cmd': 'addsetting', 'data': set})
# Send formatting options
for frm in gensettings.formatcontrols:
emit('from_server', {'cmd': 'addformat', 'data': frm})
# Add format key to vars if it wasn't loaded with client.settings
if(not frm["id"] in vars.formatoptns):
vars.formatoptns[frm["id"]] = False;
#==================================================================#
# Take settings from vars and write them to client settings file
#==================================================================#
def savesettings():
# Build json to write
js = {}
js["apikey"] = vars.apikey
js["andepth"] = vars.andepth
js["temp"] = vars.temp
js["top_p"] = vars.top_p
js["top_k"] = vars.top_k
js["tfs"] = vars.tfs
js["rep_pen"] = vars.rep_pen
js["genamt"] = vars.genamt
js["max_length"] = vars.max_length
js["ikgen"] = vars.ikgen
js["formatoptns"] = vars.formatoptns
js["numseqs"] = vars.numseqs
js["widepth"] = vars.widepth
js["useprompt"] = vars.useprompt
js["adventure"] = vars.adventure
js["dynamicscan"] = vars.dynamicscan
# Write it
if not os.path.exists('settings'):
os.mkdir('settings')
file = open("settings/" + getmodelname().replace('/', '_') + ".settings", "w")
try:
file.write(json.dumps(js, indent=3))
finally:
file.close()
#==================================================================#
# Read settings from client file JSON and send to vars
#==================================================================#
def loadsettings():
if(path.exists("settings/" + getmodelname().replace('/', '_') + ".settings")):
# Read file contents into JSON object
file = open("settings/" + getmodelname().replace('/', '_') + ".settings", "r")
js = json.load(file)
# Copy file contents to vars
if("apikey" in js):
vars.apikey = js["apikey"]
if("andepth" in js):
vars.andepth = js["andepth"]
if("temp" in js):
vars.temp = js["temp"]
if("top_p" in js):
vars.top_p = js["top_p"]
if("top_k" in js):
vars.top_k = js["top_k"]
if("tfs" in js):
vars.tfs = js["tfs"]
if("rep_pen" in js):
vars.rep_pen = js["rep_pen"]
if("genamt" in js):
vars.genamt = js["genamt"]
if("max_length" in js):
vars.max_length = js["max_length"]
if("ikgen" in js):
vars.ikgen = js["ikgen"]
if("formatoptns" in js):
vars.formatoptns = js["formatoptns"]
if("numseqs" in js):
vars.numseqs = js["numseqs"]
if("widepth" in js):
vars.widepth = js["widepth"]
if("useprompt" in js):
vars.useprompt = js["useprompt"]
if("adventure" in js):
vars.adventure = js["adventure"]
if("dynamicscan" in js):
vars.dynamicscan = js["dynamicscan"]
file.close()
#==================================================================#
# Allow the models to override some settings
#==================================================================#
def loadmodelsettings():
if(path.exists(vars.custmodpth + "/config.json")):
model_config = open(vars.custmodpth + "/config.json", "r")
js = json.load(model_config)
if("badwordsids" in js):
vars.badwordsids = js["badwordsids"]
if("temp" in js):
vars.temp = js["temp"]
if("top_p" in js):
vars.top_p = js["top_p"]
if("top_k" in js):
vars.top_k = js["top_k"]
if("tfs" in js):
vars.tfs = js["tfs"]
if("rep_pen" in js):
vars.rep_pen = js["rep_pen"]
if("adventure" in js):
vars.adventure = js["adventure"]
if("dynamicscan" in js):
vars.dynamicscan = js["dynamicscan"]
if("formatoptns" in js):
vars.formatoptns = js["formatoptns"]
model_config.close()
#==================================================================#
# Don't save settings unless 2 seconds have passed without modification
#==================================================================#
@debounce(2)
def settingschanged():
print("{0}Saving settings!{1}".format(colors.GREEN, colors.END))
savesettings()
#==================================================================#
# Take input text from SocketIO and decide what to do with it
#==================================================================#
def actionsubmit(data, actionmode=0, force_submit=False):
# Ignore new submissions if the AI is currently busy
if(vars.aibusy):
return
set_aibusy(1)
vars.recentback = False
vars.recentedit = False
vars.actionmode = actionmode
# "Action" mode
if(actionmode == 1):
data = data.strip().lstrip('>')
data = re.sub(r'\n+', ' ', data)
if(len(data)):
data = f"\n\n> {data}\n"
# If we're not continuing, store a copy of the raw input
if(data != ""):
vars.lastact = data
if(not vars.gamestarted):
if(not force_submit and len(data.strip()) == 0):
set_aibusy(0)
return
# Start the game
vars.gamestarted = True
# Save this first action as the prompt
vars.prompt = data
if(not vars.noai):
# Clear the startup text from game screen
emit('from_server', {'cmd': 'updatescreen', 'gamestarted': False, 'data': 'Please wait, generating story...'}, broadcast=True)
calcsubmit(data) # Run the first action through the generator
emit('from_server', {'cmd': 'scrolldown', 'data': ''}, broadcast=True)
else:
refresh_story()
set_aibusy(0)
emit('from_server', {'cmd': 'scrolldown', 'data': ''}, broadcast=True)
else:
# Dont append submission if it's a blank/continue action
if(data != ""):
# Apply input formatting & scripts before sending to tokenizer
if(vars.actionmode == 0):
data = applyinputformatting(data)
# Store the result in the Action log
if(len(vars.prompt.strip()) == 0):
vars.prompt = data
else:
vars.actions.append(data)
update_story_chunk('last')
if(not vars.noai):
# Off to the tokenizer!
calcsubmit(data)
emit('from_server', {'cmd': 'scrolldown', 'data': ''}, broadcast=True)
else:
set_aibusy(0)
emit('from_server', {'cmd': 'scrolldown', 'data': ''}, broadcast=True)
#==================================================================#
#
#==================================================================#
def actionretry(data):
if(vars.noai):
emit('from_server', {'cmd': 'errmsg', 'data': "Retry function unavailable in Read Only mode."})
return
if(vars.aibusy):
return
# Remove last action if possible and resubmit
if(vars.gamestarted if vars.useprompt else len(vars.actions) > 0):
set_aibusy(1)
if(not vars.recentback and len(vars.actions) != 0 and len(vars.genseqs) == 0): # Don't pop if we're in the "Select sequence to keep" menu or if there are no non-prompt actions
last_key = vars.actions.get_last_key()
vars.actions.pop()
remove_story_chunk(last_key + 1)
vars.genseqs = []
calcsubmit('')
emit('from_server', {'cmd': 'scrolldown', 'data': ''}, broadcast=True)
vars.recentback = False
vars.recentedit = False
elif(not vars.useprompt):
emit('from_server', {'cmd': 'errmsg', 'data': "Please enable \"Always Add Prompt\" to retry with your prompt."})
#==================================================================#
#
#==================================================================#
def actionback():
if(vars.aibusy):
return
# Remove last index of actions and refresh game screen
if(len(vars.genseqs) == 0 and len(vars.actions) > 0):
last_key = vars.actions.get_last_key()
vars.actions.pop()
vars.recentback = True
remove_story_chunk(last_key + 1)
elif(len(vars.genseqs) == 0):
emit('from_server', {'cmd': 'errmsg', 'data': "Cannot delete the prompt."})
else:
vars.genseqs = []
#==================================================================#
#
#==================================================================#
def calcsubmitbudgetheader(txt, **kwargs):
# Scan for WorldInfo matches
winfo, found_entries = checkworldinfo(txt, **kwargs)
# Add a newline to the end of memory
if(vars.memory != "" and vars.memory[-1] != "\n"):
mem = vars.memory + "\n"
else:
mem = vars.memory
# Build Author's Note if set
if(vars.authornote != ""):
anotetxt = "\n[Author's note: "+vars.authornote+"]\n"
else:
anotetxt = ""
return winfo, mem, anotetxt, found_entries
def calcsubmitbudget(actionlen, winfo, mem, anotetxt, actions):
forceanote = False # In case we don't have enough actions to hit A.N. depth
anoteadded = False # In case our budget runs out before we hit A.N. depth
anotetkns = [] # Placeholder for Author's Note tokens
lnanote = 0 # Placeholder for Author's Note length
# Calculate token budget
prompttkns = tokenizer.encode(vars.comregex_ai.sub('', vars.prompt))
lnprompt = len(prompttkns)
memtokens = tokenizer.encode(mem)
lnmem = len(memtokens)
witokens = tokenizer.encode(winfo)
lnwi = len(witokens)
if(anotetxt != ""):
anotetkns = tokenizer.encode(anotetxt)
lnanote = len(anotetkns)
lnsp = vars.sp.shape[0] if vars.sp is not None else 0
if(vars.useprompt):
budget = vars.max_length - lnsp - lnprompt - lnmem - lnanote - lnwi - vars.genamt
else:
budget = vars.max_length - lnsp - lnmem - lnanote - lnwi - vars.genamt
if(actionlen == 0):
# First/Prompt action
subtxt = vars.memory + winfo + anotetxt + vars.comregex_ai.sub('', vars.prompt)
lnsub = lnsp + lnmem + lnwi + lnprompt + lnanote
return subtxt, lnsub+1, lnsub+vars.genamt
else:
tokens = []
# Check if we have the action depth to hit our A.N. depth
if(anotetxt != "" and actionlen < vars.andepth):
forceanote = True
# Get most recent action tokens up to our budget
n = 0
for key in reversed(actions):
chunk = vars.comregex_ai.sub('', actions[key])
if(budget <= 0):
break
acttkns = tokenizer.encode(chunk)
tknlen = len(acttkns)
if(tknlen < budget):
tokens = acttkns + tokens
budget -= tknlen
else:
count = budget * -1
tokens = acttkns[count:] + tokens
budget = 0
break
# Inject Author's Note if we've reached the desired depth
if(n == vars.andepth-1):
if(anotetxt != ""):
tokens = anotetkns + tokens # A.N. len already taken from bdgt
anoteadded = True
n += 1
# If we're not using the prompt every time and there's still budget left,
# add some prompt.
if(not vars.useprompt):
if(budget > 0):
prompttkns = prompttkns[-budget:]
else:
prompttkns = []
# Did we get to add the A.N.? If not, do it here
if(anotetxt != ""):
if((not anoteadded) or forceanote):
tokens = memtokens + witokens + anotetkns + prompttkns + tokens
else:
tokens = memtokens + witokens + prompttkns + tokens
else:
# Prepend Memory, WI, and Prompt before action tokens
tokens = memtokens + witokens + prompttkns + tokens
# Send completed bundle to generator
ln = len(tokens) + lnsp
return tokenizer.decode(tokens), ln+1, ln+vars.genamt
#==================================================================#
# Take submitted text and build the text to be given to generator
#==================================================================#
def calcsubmit(txt):
anotetxt = "" # Placeholder for Author's Note text
forceanote = False # In case we don't have enough actions to hit A.N. depth
anoteadded = False # In case our budget runs out before we hit A.N. depth
actionlen = len(vars.actions)
winfo, mem, anotetxt, found_entries = calcsubmitbudgetheader(txt)
# For all transformers models
if(vars.model != "InferKit"):
subtxt, min, max = calcsubmitbudget(actionlen, winfo, mem, anotetxt, vars.actions)
if(actionlen == 0):
if(not vars.model in ["Colab", "OAI", "TPUMeshTransformerGPTJ"]):
generate(subtxt, min, max, found_entries=found_entries)
elif(vars.model == "Colab"):
sendtocolab(subtxt, min, max)
elif(vars.model == "OAI"):
oairequest(subtxt, min, max)
elif(vars.model == "TPUMeshTransformerGPTJ"):
tpumtjgenerate(subtxt, min, max, found_entries=found_entries)
else:
if(not vars.model in ["Colab", "OAI", "TPUMeshTransformerGPTJ"]):
generate(subtxt, min, max, found_entries=found_entries)
elif(vars.model == "Colab"):
sendtocolab(subtxt, min, max)
elif(vars.model == "OAI"):
oairequest(subtxt, min, max)
elif(vars.model == "TPUMeshTransformerGPTJ"):
tpumtjgenerate(subtxt, min, max, found_entries=found_entries)
# For InferKit web API
else:
# Check if we have the action depth to hit our A.N. depth
if(anotetxt != "" and actionlen < vars.andepth):
forceanote = True
if(vars.useprompt):
budget = vars.ikmax - len(vars.comregex_ai.sub('', vars.prompt)) - len(anotetxt) - len(mem) - len(winfo) - 1
else:
budget = vars.ikmax - len(anotetxt) - len(mem) - len(winfo) - 1
subtxt = ""
prompt = vars.comregex_ai.sub('', vars.prompt)
n = 0
for key in reversed(vars.actions):
chunk = vars.actions[key]
if(budget <= 0):
break
actlen = len(chunk)
if(actlen < budget):
subtxt = chunk + subtxt
budget -= actlen
else:
count = budget * -1
subtxt = chunk[count:] + subtxt
budget = 0
break
# If we're not using the prompt every time and there's still budget left,
# add some prompt.
if(not vars.useprompt):
if(budget > 0):
prompt = vars.comregex_ai.sub('', vars.prompt)[-budget:]
else:
prompt = ""
# Inject Author's Note if we've reached the desired depth
if(n == vars.andepth-1):
if(anotetxt != ""):
subtxt = anotetxt + subtxt # A.N. len already taken from bdgt
anoteadded = True
n += 1
# Did we get to add the A.N.? If not, do it here
if(anotetxt != ""):
if((not anoteadded) or forceanote):
subtxt = mem + winfo + anotetxt + prompt + subtxt
else:
subtxt = mem + winfo + prompt + subtxt
else:
subtxt = mem + winfo + prompt + subtxt
# Send it!
ikrequest(subtxt)
#==================================================================#
# Send text to generator and deal with output
#==================================================================#
def generate(txt, minimum, maximum, found_entries=None):
if(found_entries is None):
found_entries = set()
found_entries = tuple(found_entries.copy() for _ in range(vars.numseqs))
print("{0}Min:{1}, Max:{2}, Txt:{3}{4}".format(colors.YELLOW, minimum, maximum, txt, colors.END))
# Store context in memory to use it for comparison with generated content
vars.lastctx = txt
# Clear CUDA cache if using GPU
if(vars.hascuda and (vars.usegpu or vars.breakmodel)):
gc.collect()
torch.cuda.empty_cache()
# Submit input text to generator
try:
gen_in = tokenizer.encode(txt, return_tensors="pt", truncation=True).long()
if(vars.sp is not None):
soft_tokens = torch.arange(
model.config.vocab_size,
model.config.vocab_size + vars.sp.shape[0],
)
gen_in = torch.cat((soft_tokens[None], gen_in), dim=-1)
if(vars.hascuda and vars.usegpu):
gen_in = gen_in.to(0)
elif(vars.hascuda and vars.breakmodel):
gen_in = gen_in.to(breakmodel.primary_device)
else:
gen_in = gen_in.to('cpu')
model.kai_scanner_head_length = gen_in.shape[-1]
model.kai_scanner_excluded_world_info = found_entries
actions = vars.actions
if(vars.dynamicscan):
actions = actions.copy()
with torch.no_grad():
already_generated = 0
numseqs = vars.numseqs
while True:
genout = generator(
gen_in,
do_sample=True,
min_length=minimum,
max_length=maximum-already_generated,
repetition_penalty=vars.rep_pen,
bad_words_ids=vars.badwordsids,
use_cache=True,
num_return_sequences=numseqs
)
already_generated += len(genout[0]) - len(gen_in[0])
if(not model.kai_scanner.any_new_entries):
break
assert genout.ndim >= 2
assert genout.shape[0] == vars.numseqs
encoded = []
for i in range(vars.numseqs):
txt = tokenizer.decode(genout[i, -already_generated:])
winfo, mem, anotetxt, _found_entries = calcsubmitbudgetheader(txt, force_use_txt=True)
found_entries[i].update(_found_entries)
txt, _, _ = calcsubmitbudget(len(actions), winfo, mem, anotetxt, actions)
encoded.append(tokenizer.encode(txt, return_tensors="pt", truncation=True)[0].long().to(genout.device))
max_length = len(max(encoded, key=len))
encoded = torch.stack(tuple(torch.nn.functional.pad(e, (max_length - len(e), 0), value=model.config.pad_token_id or model.config.eos_token_id) for e in encoded))
genout = torch.cat(
(
encoded,
genout[..., -already_generated:],
),
dim=-1
)
if(vars.sp is not None):
soft_tokens = torch.arange(
model.config.vocab_size,
model.config.vocab_size + vars.sp.shape[0],
device=genout.device,
)
genout = torch.cat((soft_tokens.tile(vars.numseqs, 1), genout), dim=-1)
diff = genout.shape[-1] - gen_in.shape[-1]
minimum += diff
maximum += diff
gen_in = genout
model.kai_scanner_head_length = encoded.shape[-1]
numseqs = 1
except Exception as e:
emit('from_server', {'cmd': 'errmsg', 'data': 'Error occured during generator call, please check console.'}, broadcast=True)
print("{0}{1}{2}".format(colors.RED, e, colors.END))
set_aibusy(0)
return
# Need to manually strip and decode tokens if we're not using a pipeline
#already_generated = -(len(gen_in[0]) - len(tokens))
genout = [{"generated_text": tokenizer.decode(tokens[-already_generated:])} for tokens in genout]
if(len(genout) == 1):
genresult(genout[0]["generated_text"])
else:
genselect(genout)
# Clear CUDA cache again if using GPU
if(vars.hascuda and (vars.usegpu or vars.breakmodel)):
del genout
gc.collect()
torch.cuda.empty_cache()
set_aibusy(0)
#==================================================================#
# Deal with a single return sequence from generate()
#==================================================================#
def genresult(genout):
print("{0}{1}{2}".format(colors.CYAN, genout, colors.END))
# Format output before continuing
genout = applyoutputformatting(genout)
# Add formatted text to Actions array and refresh the game screen
if(len(vars.prompt.strip()) == 0):
vars.prompt = genout
else:
vars.actions.append(genout)
update_story_chunk('last')
emit('from_server', {'cmd': 'texteffect', 'data': vars.actions.get_last_key() if len(vars.actions) else 0}, broadcast=True)
#==================================================================#
# Send generator sequences to the UI for selection
#==================================================================#
def genselect(genout):
i = 0
for result in genout:
# Apply output formatting rules to sequences
result["generated_text"] = applyoutputformatting(result["generated_text"])
print("{0}[Result {1}]\n{2}{3}".format(colors.CYAN, i, result["generated_text"], colors.END))
i += 1
# Store sequences in memory until selection is made
vars.genseqs = genout
# Send sequences to UI for selection
emit('from_server', {'cmd': 'genseqs', 'data': genout}, broadcast=True)
#==================================================================#
# Send selected sequence to action log and refresh UI
#==================================================================#
def selectsequence(n):
if(len(vars.genseqs) == 0):
return
vars.actions.append(vars.genseqs[int(n)]["generated_text"])
update_story_chunk('last')
emit('from_server', {'cmd': 'texteffect', 'data': vars.actions.get_last_key() if len(vars.actions) else 0}, broadcast=True)
emit('from_server', {'cmd': 'hidegenseqs', 'data': ''}, broadcast=True)
vars.genseqs = []
#==================================================================#
# Send transformers-style request to ngrok/colab host
#==================================================================#
def sendtocolab(txt, min, max):
# Log request to console
print("{0}Tokens:{1}, Txt:{2}{3}".format(colors.YELLOW, min-1, txt, colors.END))
# Store context in memory to use it for comparison with generated content
vars.lastctx = txt
# Build request JSON data
reqdata = {
'text': txt,
'min': min,
'max': max,
'rep_pen': vars.rep_pen,
'temperature': vars.temp,
'top_p': vars.top_p,
'top_k': vars.top_k,
'tfs': vars.tfs,
'numseqs': vars.numseqs,
'retfultxt': False
}
# Create request
req = requests.post(
vars.colaburl,
json = reqdata
)
# Deal with the response
if(req.status_code == 200):
js = req.json()["data"]
# Try to be backwards compatible with outdated colab
if("text" in js):
genout = [getnewcontent(js["text"])]
else:
genout = js["seqs"]
if(len(genout) == 1):
genresult(genout[0])
else:
# Convert torch output format to transformers
seqs = []
for seq in genout:
seqs.append({"generated_text": seq})
genselect(seqs)
# Format output before continuing
#genout = applyoutputformatting(getnewcontent(genout))
# Add formatted text to Actions array and refresh the game screen
#vars.actions.append(genout)
#refresh_story()
#emit('from_server', {'cmd': 'texteffect', 'data': vars.actions.get_last_key() if len(vars.actions) else 0})
set_aibusy(0)
else:
errmsg = "Colab API Error: Failed to get a reply from the server. Please check the colab console."
print("{0}{1}{2}".format(colors.RED, errmsg, colors.END))
emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True)
set_aibusy(0)
#==================================================================#
# Send text to TPU mesh transformer backend
#==================================================================#
def tpumtjgenerate(txt, minimum, maximum, found_entries=None):
if(found_entries is None):
found_entries = set()
found_entries = tuple(found_entries.copy() for _ in range(vars.numseqs))
print("{0}Min:{1}, Max:{2}, Txt:{3}{4}".format(colors.YELLOW, minimum, maximum, txt, colors.END))
# Submit input text to generator
try:
if(vars.dynamicscan):
raise ValueError("Dynamic world info scanning is not supported by the TPU backend yet")
soft_tokens = None
if(vars.sp is not None):
soft_tokens = np.arange(
tpu_mtj_backend.params["n_vocab"] + tpu_mtj_backend.params["n_vocab_padding"],
tpu_mtj_backend.params["n_vocab"] + tpu_mtj_backend.params["n_vocab_padding"] + vars.sp_length,
dtype=np.uint32
)
genout = tpu_mtj_backend.infer(
txt,
gen_len = maximum-minimum+1,
temp=vars.temp,
top_p=vars.top_p,
top_k=vars.top_k,
tfs=vars.tfs,
numseqs=vars.numseqs,
repetition_penalty=vars.rep_pen,
soft_embeddings=vars.sp,
soft_tokens=soft_tokens,
)
except Exception as e:
emit('from_server', {'cmd': 'errmsg', 'data': 'Error occured during generator call, please check console.'}, broadcast=True)
print("{0}{1}{2}".format(colors.RED, e, colors.END))
set_aibusy(0)
return
genout = [{"generated_text": txt} for txt in genout]
if(len(genout) == 1):
genresult(genout[0]["generated_text"])
else:
genselect(genout)
set_aibusy(0)
#==================================================================#
# Replaces returns and newlines with HTML breaks
#==================================================================#
def formatforhtml(txt):
return txt.replace("\\r\\n", "
").replace("\\r", "
").replace("\\n", "
").replace("\r\n", "
").replace('\n', '
').replace('\r', '
')
#==================================================================#
# Strips submitted text from the text returned by the AI
#==================================================================#
def getnewcontent(txt):
# If the submitted context was blank, then everything is new
if(vars.lastctx == ""):
return txt
# Tokenize the last context and the generated content
ctxtokens = tokenizer.encode(vars.lastctx)
txttokens = tokenizer.encode(txt)
dif = (len(txttokens) - len(ctxtokens)) * -1
# Remove the context from the returned text
newtokens = txttokens[dif:]
return tokenizer.decode(newtokens)
#==================================================================#
# Applies chosen formatting options to text submitted to AI
#==================================================================#
def applyinputformatting(txt):
# Add sentence spacing
if(vars.formatoptns["frmtadsnsp"]):
txt = utils.addsentencespacing(txt, vars)
return txt
#==================================================================#
# Applies chosen formatting options to text returned from AI
#==================================================================#
def applyoutputformatting(txt):
# Use standard quotes and apostrophes
txt = utils.fixquotes(txt)
# Adventure mode clipping of all characters after '>'
if(vars.adventure):
txt = vars.acregex_ai.sub('', txt)
# Trim incomplete sentences
if(vars.formatoptns["frmttriminc"]):
txt = utils.trimincompletesentence(txt)
# Replace blank lines
if(vars.formatoptns["frmtrmblln"]):
txt = utils.replaceblanklines(txt)
# Remove special characters
if(vars.formatoptns["frmtrmspch"]):
txt = utils.removespecialchars(txt, vars)
# Single Line Mode
if(vars.formatoptns["singleline"]):
txt = utils.singlelineprocessing(txt, vars)
return txt
#==================================================================#
# Sends the current story content to the Game Screen
#==================================================================#
def refresh_story():
text_parts = ['', vars.comregex_ui.sub(lambda m: '\n'.join('' + l + '' for l in m.group().split('\n')), html.escape(vars.prompt)), '']
for idx in vars.actions:
item = vars.actions[idx]
idx += 1
item = html.escape(item)
item = vars.comregex_ui.sub(lambda m: '\n'.join('' + l + '' for l in m.group().split('\n')), item) # Add special formatting to comments
item = vars.acregex_ui.sub('\\1', item) # Add special formatting to adventure actions
text_parts.extend(('', item, ''))
emit('from_server', {'cmd': 'updatescreen', 'gamestarted': vars.gamestarted, 'data': formatforhtml(''.join(text_parts))}, broadcast=True)
#==================================================================#
# Signals the Game Screen to update one of the chunks
#==================================================================#
def update_story_chunk(idx: Union[int, str]):
if idx == 'last':
if len(vars.actions) <= 1:
# In this case, we are better off just refreshing the whole thing as the
# prompt might not have been shown yet (with a "Generating story..."
# message instead).
refresh_story()
return
idx = (vars.actions.get_last_key() if len(vars.actions) else 0) + 1
if idx == 0:
text = vars.prompt
else:
# Actions are 0 based, but in chunks 0 is the prompt.
# So the chunk index is one more than the corresponding action index.
text = vars.actions[idx - 1]
item = html.escape(text)
item = vars.comregex_ui.sub(lambda m: '\n'.join('' + l + '' for l in m.group().split('\n')), item) # Add special formatting to comments
item = vars.acregex_ui.sub('\\1', item) # Add special formatting to adventure actions
chunk_text = f'{formatforhtml(item)}'
emit('from_server', {'cmd': 'updatechunk', 'data': {'index': idx, 'html': chunk_text}}, broadcast=True)
#==================================================================#
# Signals the Game Screen to remove one of the chunks
#==================================================================#
def remove_story_chunk(idx: int):
emit('from_server', {'cmd': 'removechunk', 'data': idx}, broadcast=True)
#==================================================================#
# Sends the current generator settings to the Game Menu
#==================================================================#
def refresh_settings():
# Suppress toggle change events while loading state
emit('from_server', {'cmd': 'allowtoggle', 'data': False}, broadcast=True)
if(vars.model != "InferKit"):
emit('from_server', {'cmd': 'updatetemp', 'data': vars.temp}, broadcast=True)
emit('from_server', {'cmd': 'updatetopp', 'data': vars.top_p}, broadcast=True)
emit('from_server', {'cmd': 'updatetopk', 'data': vars.top_k}, broadcast=True)
emit('from_server', {'cmd': 'updatetfs', 'data': vars.tfs}, broadcast=True)
emit('from_server', {'cmd': 'updatereppen', 'data': vars.rep_pen}, broadcast=True)
emit('from_server', {'cmd': 'updateoutlen', 'data': vars.genamt}, broadcast=True)
emit('from_server', {'cmd': 'updatetknmax', 'data': vars.max_length}, broadcast=True)
emit('from_server', {'cmd': 'updatenumseq', 'data': vars.numseqs}, broadcast=True)
else:
emit('from_server', {'cmd': 'updatetemp', 'data': vars.temp}, broadcast=True)
emit('from_server', {'cmd': 'updatetopp', 'data': vars.top_p}, broadcast=True)
emit('from_server', {'cmd': 'updateikgen', 'data': vars.ikgen}, broadcast=True)
emit('from_server', {'cmd': 'updateanotedepth', 'data': vars.andepth}, broadcast=True)
emit('from_server', {'cmd': 'updatewidepth', 'data': vars.widepth}, broadcast=True)
emit('from_server', {'cmd': 'updateuseprompt', 'data': vars.useprompt}, broadcast=True)
emit('from_server', {'cmd': 'updateadventure', 'data': vars.adventure}, broadcast=True)
emit('from_server', {'cmd': 'updatedynamicscan', 'data': vars.dynamicscan}, broadcast=True)
emit('from_server', {'cmd': 'updatefrmttriminc', 'data': vars.formatoptns["frmttriminc"]}, broadcast=True)
emit('from_server', {'cmd': 'updatefrmtrmblln', 'data': vars.formatoptns["frmtrmblln"]}, broadcast=True)
emit('from_server', {'cmd': 'updatefrmtrmspch', 'data': vars.formatoptns["frmtrmspch"]}, broadcast=True)
emit('from_server', {'cmd': 'updatefrmtadsnsp', 'data': vars.formatoptns["frmtadsnsp"]}, broadcast=True)
emit('from_server', {'cmd': 'updatesingleline', 'data': vars.formatoptns["singleline"]}, broadcast=True)
# Allow toggle events again
emit('from_server', {'cmd': 'allowtoggle', 'data': True}, broadcast=True)
#==================================================================#
# Sets the logical and display states for the AI Busy condition
#==================================================================#
def set_aibusy(state):
if(state):
vars.aibusy = True
emit('from_server', {'cmd': 'setgamestate', 'data': 'wait'}, broadcast=True)
else:
vars.aibusy = False
emit('from_server', {'cmd': 'setgamestate', 'data': 'ready'}, broadcast=True)
#==================================================================#
#
#==================================================================#
def editrequest(n):
if(n == 0):
txt = vars.prompt
else:
txt = vars.actions[n-1]
vars.editln = n
emit('from_server', {'cmd': 'setinputtext', 'data': txt}, broadcast=True)
emit('from_server', {'cmd': 'enablesubmit', 'data': ''}, broadcast=True)
#==================================================================#
#
#==================================================================#
def editsubmit(data):
vars.recentedit = True
if(vars.editln == 0):
vars.prompt = data
else:
vars.actions[vars.editln-1] = data
vars.mode = "play"
update_story_chunk(vars.editln)
emit('from_server', {'cmd': 'texteffect', 'data': vars.editln}, broadcast=True)
emit('from_server', {'cmd': 'editmode', 'data': 'false'})
#==================================================================#
#
#==================================================================#
def deleterequest():
vars.recentedit = True
# Don't delete prompt
if(vars.editln == 0):
# Send error message
pass
else:
del vars.actions[vars.editln-1]
vars.mode = "play"
remove_story_chunk(vars.editln)
emit('from_server', {'cmd': 'editmode', 'data': 'false'})
#==================================================================#
#
#==================================================================#
def inlineedit(chunk, data):
vars.recentedit = True
chunk = int(chunk)
if(chunk == 0):
if(len(data.strip()) == 0):
return
vars.prompt = data
else:
vars.actions[chunk-1] = data
update_story_chunk(chunk)
emit('from_server', {'cmd': 'texteffect', 'data': chunk}, broadcast=True)
emit('from_server', {'cmd': 'editmode', 'data': 'false'}, broadcast=True)
#==================================================================#
#
#==================================================================#
def inlinedelete(chunk):
vars.recentedit = True
chunk = int(chunk)
# Don't delete prompt
if(chunk == 0):
# Send error message
update_story_chunk(chunk)
emit('from_server', {'cmd': 'errmsg', 'data': "Cannot delete the prompt."})
emit('from_server', {'cmd': 'editmode', 'data': 'false'}, broadcast=True)
else:
del vars.actions[chunk-1]
remove_story_chunk(chunk)
emit('from_server', {'cmd': 'editmode', 'data': 'false'}, broadcast=True)
#==================================================================#
# Toggles the game mode for memory editing and sends UI commands
#==================================================================#
def togglememorymode():
if(vars.mode == "play"):
vars.mode = "memory"
emit('from_server', {'cmd': 'memmode', 'data': 'true'}, broadcast=True)
emit('from_server', {'cmd': 'setinputtext', 'data': vars.memory}, broadcast=True)
emit('from_server', {'cmd': 'setanote', 'data': vars.authornote}, broadcast=True)
elif(vars.mode == "memory"):
vars.mode = "play"
emit('from_server', {'cmd': 'memmode', 'data': 'false'}, broadcast=True)
#==================================================================#
# Toggles the game mode for WI editing and sends UI commands
#==================================================================#
def togglewimode():
if(vars.mode == "play"):
vars.mode = "wi"
emit('from_server', {'cmd': 'wimode', 'data': 'true'}, broadcast=True)
elif(vars.mode == "wi"):
# Commit WI fields first
requestwi()
# Then set UI state back to Play
vars.mode = "play"
emit('from_server', {'cmd': 'wimode', 'data': 'false'}, broadcast=True)
sendwi()
#==================================================================#
#
#==================================================================#
def addwiitem():
ob = {"key": "", "keysecondary": "", "content": "", "num": len(vars.worldinfo), "init": False, "selective": False, "constant": False}
vars.worldinfo.append(ob);
emit('from_server', {'cmd': 'addwiitem', 'data': ob}, broadcast=True)
#==================================================================#
#
#==================================================================#
def sendwi():
# Cache len of WI
ln = len(vars.worldinfo)
# Clear contents of WI container
emit('from_server', {'cmd': 'clearwi', 'data': ''}, broadcast=True)
# If there are no WI entries, send an empty WI object
if(ln == 0):
addwiitem()
else:
# Send contents of WI array
for wi in vars.worldinfo:
ob = wi
emit('from_server', {'cmd': 'addwiitem', 'data': ob}, broadcast=True)
# Make sure last WI item is uninitialized
if(vars.worldinfo[-1]["init"]):
addwiitem()
#==================================================================#
# Request current contents of all WI HTML elements
#==================================================================#
def requestwi():
list = []
for wi in vars.worldinfo:
list.append(wi["num"])
emit('from_server', {'cmd': 'requestwiitem', 'data': list})
#==================================================================#
# Renumber WI items consecutively
#==================================================================#
def organizewi():
if(len(vars.worldinfo) > 0):
count = 0
for wi in vars.worldinfo:
wi["num"] = count
count += 1
#==================================================================#
# Extract object from server and send it to WI objects
#==================================================================#
def commitwi(ar):
for ob in ar:
vars.worldinfo[ob["num"]]["key"] = ob["key"]
vars.worldinfo[ob["num"]]["keysecondary"] = ob["keysecondary"]
vars.worldinfo[ob["num"]]["content"] = ob["content"]
vars.worldinfo[ob["num"]]["selective"] = ob["selective"]
vars.worldinfo[ob["num"]]["constant"] = ob.get("constant", False)
# Was this a deletion request? If so, remove the requested index
if(vars.deletewi >= 0):
del vars.worldinfo[vars.deletewi]
organizewi()
# Send the new WI array structure
sendwi()
# And reset deletewi index
vars.deletewi = -1
#==================================================================#
#
#==================================================================#
def deletewi(num):
if(num < len(vars.worldinfo)):
# Store index of deletion request
vars.deletewi = num
# Get contents of WI HTML inputs
requestwi()
#==================================================================#
# Look for WI keys in text to generator
#==================================================================#
def checkworldinfo(txt, force_use_txt=False):
original_txt = txt
# Dont go any further if WI is empty
if(len(vars.worldinfo) == 0):
return "", set()
# Cache actions length
ln = len(vars.actions)
# Don't bother calculating action history if widepth is 0
if(vars.widepth > 0):
depth = vars.widepth
# If this is not a continue, add 1 to widepth since submitted
# text is already in action history @ -1
if(not force_use_txt and (txt != "" and vars.prompt != txt)):
txt = ""
depth += 1
if(ln > 0):
chunks = collections.deque()
i = 0
for key in reversed(vars.actions):
chunk = vars.actions[key]
chunks.appendleft(chunk)
i += 1
if(i == depth):
break
if(ln >= depth):
txt = "".join(chunks)
elif(ln > 0):
txt = vars.comregex_ai.sub('', vars.prompt) + "".join(chunks)
elif(ln == 0):
txt = vars.comregex_ai.sub('', vars.prompt)
if(force_use_txt):
txt += original_txt
# Scan text for matches on WI keys
wimem = ""
found_entries = set()
for wi in vars.worldinfo:
if(wi.get("constant", False)):
wimem = wimem + wi["content"] + "\n"
found_entries.add(id(wi))
continue
if(wi["key"] != ""):
# Split comma-separated keys
keys = wi["key"].split(",")
keys_secondary = wi.get("keysecondary", "").split(",")
for k in keys:
ky = k
# Remove leading/trailing spaces if the option is enabled
if(vars.wirmvwhtsp):
ky = k.strip()
if ky in txt:
if wi.get("selective", False) and len(keys_secondary):
found = False
for ks in keys_secondary:
ksy = ks
if(vars.wirmvwhtsp):
ksy = ks.strip()
if ksy in txt:
wimem = wimem + wi["content"] + "\n"
found_entries.add(id(wi))
found = True
break
if found:
break
else:
wimem = wimem + wi["content"] + "\n"
found_entries.add(id(wi))
break
return wimem, found_entries
#==================================================================#
# Commit changes to Memory storage
#==================================================================#
def memsubmit(data):
# Maybe check for length at some point
# For now just send it to storage
vars.memory = data
vars.mode = "play"
emit('from_server', {'cmd': 'memmode', 'data': 'false'}, broadcast=True)
# Ask for contents of Author's Note field
emit('from_server', {'cmd': 'getanote', 'data': ''})
#==================================================================#
# Commit changes to Author's Note
#==================================================================#
def anotesubmit(data):
# Maybe check for length at some point
# For now just send it to storage
vars.authornote = data
#==================================================================#
# Assembles game data into a request to InferKit API
#==================================================================#
def ikrequest(txt):
# Log request to console
print("{0}Len:{1}, Txt:{2}{3}".format(colors.YELLOW, len(txt), txt, colors.END))
# Build request JSON data
reqdata = {
'forceNoEnd': True,
'length': vars.ikgen,
'prompt': {
'isContinuation': False,
'text': txt
},
'startFromBeginning': False,
'streamResponse': False,
'temperature': vars.temp,
'topP': vars.top_p
}
# Create request
req = requests.post(
vars.url,
json = reqdata,
headers = {
'Authorization': 'Bearer '+vars.apikey
}
)
# Deal with the response
if(req.status_code == 200):
genout = req.json()["data"]["text"]
print("{0}{1}{2}".format(colors.CYAN, genout, colors.END))
vars.actions.append(genout)
update_story_chunk('last')
emit('from_server', {'cmd': 'texteffect', 'data': vars.actions.get_last_key() if len(vars.actions) else 0}, broadcast=True)
set_aibusy(0)
else:
# Send error message to web client
er = req.json()
if("error" in er):
code = er["error"]["extensions"]["code"]
elif("errors" in er):
code = er["errors"][0]["extensions"]["code"]
errmsg = "InferKit API Error: {0} - {1}".format(req.status_code, code)
emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True)
set_aibusy(0)
#==================================================================#
# Assembles game data into a request to OpenAI API
#==================================================================#
def oairequest(txt, min, max):
# Log request to console
print("{0}Len:{1}, Txt:{2}{3}".format(colors.YELLOW, len(txt), txt, colors.END))
# Store context in memory to use it for comparison with generated content
vars.lastctx = txt
# Build request JSON data
reqdata = {
'prompt': txt,
'max_tokens': max,
'temperature': vars.temp,
'top_p': vars.top_p,
'n': 1,
'stream': False
}
req = requests.post(
vars.oaiurl,
json = reqdata,
headers = {
'Authorization': 'Bearer '+vars.oaiapikey,
'Content-Type': 'application/json'
}
)
# Deal with the response
if(req.status_code == 200):
genout = req.json()["choices"][0]["text"]
print("{0}{1}{2}".format(colors.CYAN, genout, colors.END))
vars.actions.append(genout)
update_story_chunk('last')
emit('from_server', {'cmd': 'texteffect', 'data': vars.actions.get_last_key() if len(vars.actions) else 0}, broadcast=True)
set_aibusy(0)
else:
# Send error message to web client
er = req.json()
if("error" in er):
type = er["error"]["type"]
message = er["error"]["message"]
errmsg = "OpenAI API Error: {0} - {1}".format(type, message)
emit('from_server', {'cmd': 'errmsg', 'data': errmsg}, broadcast=True)
set_aibusy(0)
#==================================================================#
# Forces UI to Play mode
#==================================================================#
def exitModes():
if(vars.mode == "edit"):
emit('from_server', {'cmd': 'editmode', 'data': 'false'}, broadcast=True)
elif(vars.mode == "memory"):
emit('from_server', {'cmd': 'memmode', 'data': 'false'}, broadcast=True)
elif(vars.mode == "wi"):
emit('from_server', {'cmd': 'wimode', 'data': 'false'}, broadcast=True)
vars.mode = "play"
#==================================================================#
# Launch in-browser save prompt
#==================================================================#
def saveas(name):
# Check if filename exists already
name = utils.cleanfilename(name)
if(not fileops.saveexists(name) or (vars.saveow and vars.svowname == name)):
# All clear to save
e = saveRequest(fileops.storypath(name))
vars.saveow = False
vars.svowname = ""
if(e is None):
emit('from_server', {'cmd': 'hidesaveas', 'data': ''})
else:
print("{0}{1}{2}".format(colors.RED, str(e), colors.END))
emit('from_server', {'cmd': 'popuperror', 'data': str(e)})
else:
# File exists, prompt for overwrite
vars.saveow = True
vars.svowname = name
emit('from_server', {'cmd': 'askforoverwrite', 'data': ''})
#==================================================================#
# Launch in-browser story-delete prompt
#==================================================================#
def deletesave(name):
name = utils.cleanfilename(name)
e = fileops.deletesave(name)
if(e is None):
if(vars.smandelete):
emit('from_server', {'cmd': 'hidepopupdelete', 'data': ''})
getloadlist()
else:
emit('from_server', {'cmd': 'popuperror', 'data': "The server denied your request to delete this story"})
else:
print("{0}{1}{2}".format(colors.RED, str(e), colors.END))
emit('from_server', {'cmd': 'popuperror', 'data': str(e)})
#==================================================================#
# Launch in-browser story-rename prompt
#==================================================================#
def renamesave(name, newname):
# Check if filename exists already
name = utils.cleanfilename(name)
newname = utils.cleanfilename(newname)
if(not fileops.saveexists(newname) or name == newname or (vars.saveow and vars.svowname == newname)):
e = fileops.renamesave(name, newname)
vars.saveow = False
vars.svowname = ""
if(e is None):
if(vars.smanrename):
emit('from_server', {'cmd': 'hidepopuprename', 'data': ''})
getloadlist()
else:
emit('from_server', {'cmd': 'popuperror', 'data': "The server denied your request to rename this story"})
else:
print("{0}{1}{2}".format(colors.RED, str(e), colors.END))
emit('from_server', {'cmd': 'popuperror', 'data': str(e)})
else:
# File exists, prompt for overwrite
vars.saveow = True
vars.svowname = newname
emit('from_server', {'cmd': 'askforoverwrite', 'data': ''})
#==================================================================#
# Save the currently running story
#==================================================================#
def save():
# Check if a file is currently open
if(".json" in vars.savedir):
saveRequest(vars.savedir)
else:
emit('from_server', {'cmd': 'saveas', 'data': ''})
#==================================================================#
# Save the story via file browser
#==================================================================#
def savetofile():
savpath = fileops.getsavepath(vars.savedir, "Save Story As", [("Json", "*.json")])
saveRequest(savpath)
#==================================================================#
# Save the story to specified path
#==================================================================#
def saveRequest(savpath):
if(savpath):
# Leave Edit/Memory mode before continuing
exitModes()
# Save path for future saves
vars.savedir = savpath
txtpath = os.path.splitext(savpath)[0] + ".txt"
# Build json to write
js = {}
js["gamestarted"] = vars.gamestarted
js["prompt"] = vars.prompt
js["memory"] = vars.memory
js["authorsnote"] = vars.authornote
js["actions"] = tuple(vars.actions.values())
js["worldinfo"] = []
# Extract only the important bits of WI
for wi in vars.worldinfo:
if(wi["constant"] or wi["key"] != ""):
js["worldinfo"].append({
"key": wi["key"],
"keysecondary": wi["keysecondary"],
"content": wi["content"],
"selective": wi["selective"],
"constant": wi["constant"]
})
txt = vars.prompt + "".join(vars.actions.values())
# Write it
try:
file = open(savpath, "w")
except Exception as e:
return e
try:
file.write(json.dumps(js, indent=3))
except Exception as e:
file.close()
return e
file.close()
try:
file = open(txtpath, "w")
except Exception as e:
return e
try:
file.write(txt)
except Exception as e:
file.close()
return e
file.close()
filename = path.basename(savpath)
if(filename.endswith('.json')):
filename = filename[:-5]
vars.laststory = filename
emit('from_server', {'cmd': 'setstoryname', 'data': vars.laststory}, broadcast=True)
print("{0}Story saved to {1}!{2}".format(colors.GREEN, path.basename(savpath), colors.END))
#==================================================================#
# Show list of saved stories
#==================================================================#
def getloadlist():
emit('from_server', {'cmd': 'buildload', 'data': fileops.getstoryfiles()})
#==================================================================#
# Show list of soft prompts
#==================================================================#
def getsplist():
if(vars.allowsp):
emit('from_server', {'cmd': 'buildsp', 'data': fileops.getspfiles(vars.modeldim)})
#==================================================================#
# Load a saved story via file browser
#==================================================================#
def loadfromfile():
loadpath = fileops.getloadpath(vars.savedir, "Select Story File", [("Json", "*.json")])
loadRequest(loadpath)
#==================================================================#
# Load a stored story from a file
#==================================================================#
def loadRequest(loadpath, filename=None):
if(loadpath):
# Leave Edit/Memory mode before continuing
exitModes()
# Read file contents into JSON object
if(isinstance(loadpath, str)):
with open(loadpath, "r") as file:
js = json.load(file)
if(filename is None):
filename = path.basename(loadpath)
else:
js = loadpath
if(filename is None):
filename = "untitled.json"
# Copy file contents to vars
vars.gamestarted = js["gamestarted"]
vars.prompt = js["prompt"]
vars.memory = js["memory"]
vars.worldinfo = []
vars.lastact = ""
vars.lastctx = ""
del vars.actions
vars.actions = structures.KoboldStoryRegister()
actions = collections.deque(js["actions"])
if(len(vars.prompt.strip()) == 0):
while(len(actions)):
action = actions.popleft()
if(len(action.strip()) != 0):
vars.prompt = action
break
else:
vars.gamestarted = False
if(vars.gamestarted):
for s in actions:
vars.actions.append(s)
# Try not to break older save files
if("authorsnote" in js):
vars.authornote = js["authorsnote"]
else:
vars.authornote = ""
if("worldinfo" in js):
num = 0
for wi in js["worldinfo"]:
vars.worldinfo.append({
"key": wi["key"],
"keysecondary": wi.get("keysecondary", ""),
"content": wi["content"],
"num": num,
"init": True,
"selective": wi.get("selective", False),
"constant": wi.get("constant", False)
})
num += 1
# Save path for save button
vars.savedir = loadpath
# Clear loadselect var
vars.loadselect = ""
# Refresh game screen
_filename = filename
if(filename.endswith('.json')):
_filename = filename[:-5]
vars.laststory = _filename
emit('from_server', {'cmd': 'setstoryname', 'data': vars.laststory}, broadcast=True)
sendwi()
emit('from_server', {'cmd': 'setmemory', 'data': vars.memory}, broadcast=True)
emit('from_server', {'cmd': 'setanote', 'data': vars.authornote}, broadcast=True)
refresh_story()
emit('from_server', {'cmd': 'setgamestate', 'data': 'ready'}, broadcast=True)
emit('from_server', {'cmd': 'hidegenseqs', 'data': ''}, broadcast=True)
print("{0}Story loaded from {1}!{2}".format(colors.GREEN, filename, colors.END))
#==================================================================#
# Load a soft prompt from a file
#==================================================================#
def spRequest(filename):
if(len(filename) == 0):
vars.sp = None
vars.sp_length = 0
return
global np
if 'np' not in globals():
import numpy as np
z, version, shape, fortran_order, dtype = fileops.checksp(filename, vars.modeldim)
assert isinstance(z, zipfile.ZipFile)
z.close()
with np.load(fileops.sppath(filename), allow_pickle=False) as f:
tensor = f['tensor.npy']
# If the tensor is in bfloat16 format, convert it to float32
if(tensor.dtype == 'V2'):
tensor.dtype = np.uint16
tensor = np.uint32(tensor) << 16
tensor.dtype = np.float32
if(tensor.dtype != np.float16):
tensor = np.float32(tensor)
assert not np.isinf(tensor).any() and not np.isnan(tensor).any()
vars.sp_length = tensor.shape[0]
if(vars.model in ("TPUMeshTransformerGPTJ",)):
rows = tensor.shape[0]
padding_amount = -(rows % -tpu_mtj_backend.params["cores_per_replica"])
tensor = np.pad(tensor, ((0, padding_amount), (0, 0)))
tensor = tensor.reshape(
tpu_mtj_backend.params["cores_per_replica"],
-1,
tpu_mtj_backend.params["d_model"],
)
vars.sp = np.float32(tensor)
else:
vars.sp = torch.from_numpy(tensor)
#==================================================================#
# Import an AIDungon game exported with Mimi's tool
#==================================================================#
def importRequest():
importpath = fileops.getloadpath(vars.savedir, "Select AID CAT File", [("Json", "*.json")])
if(importpath):
# Leave Edit/Memory mode before continuing
exitModes()
# Read file contents into JSON object
file = open(importpath, "rb")
vars.importjs = json.load(file)
# If a bundle file is being imported, select just the Adventures object
if type(vars.importjs) is dict and "stories" in vars.importjs:
vars.importjs = vars.importjs["stories"]
# Clear Popup Contents
emit('from_server', {'cmd': 'clearpopup', 'data': ''}, broadcast=True)
# Initialize vars
num = 0
vars.importnum = -1
# Get list of stories
for story in vars.importjs:
ob = {}
ob["num"] = num
if(story["title"] != "" and story["title"] != None):
ob["title"] = story["title"]
else:
ob["title"] = "(No Title)"
if(story["description"] != "" and story["description"] != None):
ob["descr"] = story["description"]
else:
ob["descr"] = "(No Description)"
if("actions" in story):
ob["acts"] = len(story["actions"])
elif("actionWindow" in story):
ob["acts"] = len(story["actionWindow"])
emit('from_server', {'cmd': 'addimportline', 'data': ob})
num += 1
# Show Popup
emit('from_server', {'cmd': 'popupshow', 'data': True})
#==================================================================#
# Import an AIDungon game selected in popup
#==================================================================#
def importgame():
if(vars.importnum >= 0):
# Cache reference to selected game
ref = vars.importjs[vars.importnum]
# Copy game contents to vars
vars.gamestarted = True
# Support for different versions of export script
if("actions" in ref):
if(len(ref["actions"]) > 0):
vars.prompt = ref["actions"][0]["text"]
else:
vars.prompt = ""
elif("actionWindow" in ref):
if(len(ref["actionWindow"]) > 0):
vars.prompt = ref["actionWindow"][0]["text"]
else:
vars.prompt = ""
else:
vars.prompt = ""
vars.memory = ref["memory"]
vars.authornote = ref["authorsNote"] if type(ref["authorsNote"]) is str else ""
vars.actions = structures.KoboldStoryRegister()
vars.worldinfo = []
vars.lastact = ""
vars.lastctx = ""
# Get all actions except for prompt
if("actions" in ref):
if(len(ref["actions"]) > 1):
for act in ref["actions"][1:]:
vars.actions.append(act["text"])
elif("actionWindow" in ref):
if(len(ref["actionWindow"]) > 1):
for act in ref["actionWindow"][1:]:
vars.actions.append(act["text"])
# Get just the important parts of world info
if(ref["worldInfo"] != None):
if(len(ref["worldInfo"]) > 1):
num = 0
for wi in ref["worldInfo"]:
vars.worldinfo.append({
"key": wi["keys"],
"keysecondary": wi.get("keysecondary", ""),
"content": wi["entry"],
"num": num,
"init": True,
"selective": wi.get("selective", False),
"constant": wi.get("constant", False)
})
num += 1
# Clear import data
vars.importjs = {}
# Reset current save
vars.savedir = getcwd()+"\stories"
# Refresh game screen
vars.laststory = None
emit('from_server', {'cmd': 'setstoryname', 'data': vars.laststory}, broadcast=True)
sendwi()
emit('from_server', {'cmd': 'setmemory', 'data': vars.memory}, broadcast=True)
emit('from_server', {'cmd': 'setanote', 'data': vars.authornote}, broadcast=True)
refresh_story()
emit('from_server', {'cmd': 'setgamestate', 'data': 'ready'}, broadcast=True)
emit('from_server', {'cmd': 'hidegenseqs', 'data': ''}, broadcast=True)
#==================================================================#
# Import an aidg.club prompt and start a new game with it.
#==================================================================#
def importAidgRequest(id):
exitModes()
urlformat = "https://prompts.aidg.club/api/"
req = requests.get(urlformat+id)
if(req.status_code == 200):
js = req.json()
# Import game state
vars.gamestarted = True
vars.prompt = js["promptContent"]
vars.memory = js["memory"]
vars.authornote = js["authorsNote"]
vars.actions = structures.KoboldStoryRegister()
vars.worldinfo = []
vars.lastact = ""
vars.lastctx = ""
num = 0
for wi in js["worldInfos"]:
vars.worldinfo.append({
"key": wi["keys"],
"keysecondary": wi.get("keysecondary", ""),
"content": wi["entry"],
"num": num,
"init": True,
"selective": wi.get("selective", False),
"constant": wi.get("constant", False)
})
num += 1
# Reset current save
vars.savedir = getcwd()+"\stories"
# Refresh game screen
vars.laststory = None
emit('from_server', {'cmd': 'setstoryname', 'data': vars.laststory}, broadcast=True)
sendwi()
emit('from_server', {'cmd': 'setmemory', 'data': vars.memory}, broadcast=True)
emit('from_server', {'cmd': 'setanote', 'data': vars.authornote}, broadcast=True)
refresh_story()
emit('from_server', {'cmd': 'setgamestate', 'data': 'ready'}, broadcast=True)
#==================================================================#
# Import World Info JSON file
#==================================================================#
def wiimportrequest():
importpath = fileops.getloadpath(vars.savedir, "Select World Info File", [("Json", "*.json")])
if(importpath):
file = open(importpath, "rb")
js = json.load(file)
if(len(js) > 0):
# If the most recent WI entry is blank, remove it.
if(not vars.worldinfo[-1]["init"]):
del vars.worldinfo[-1]
# Now grab the new stuff
num = len(vars.worldinfo)
for wi in js:
vars.worldinfo.append({
"key": wi["keys"],
"keysecondary": wi.get("keysecondary", ""),
"content": wi["entry"],
"num": num,
"init": True,
"selective": wi.get("selective", False),
"constant": wi.get("constant", False)
})
num += 1
print("{0}".format(vars.worldinfo[0]))
# Refresh game screen
sendwi()
#==================================================================#
# Starts a new story
#==================================================================#
def newGameRequest():
# Leave Edit/Memory mode before continuing
exitModes()
# Clear vars values
vars.gamestarted = False
vars.prompt = ""
vars.memory = ""
vars.actions = structures.KoboldStoryRegister()
vars.authornote = ""
vars.worldinfo = []
vars.lastact = ""
vars.lastctx = ""
# Reset current save
vars.savedir = getcwd()+"\stories"
# Refresh game screen
vars.laststory = None
emit('from_server', {'cmd': 'setstoryname', 'data': vars.laststory}, broadcast=True)
sendwi()
emit('from_server', {'cmd': 'setmemory', 'data': vars.memory}, broadcast=True)
emit('from_server', {'cmd': 'setanote', 'data': vars.authornote}, broadcast=True)
setStartState()
def randomGameRequest(topic):
newGameRequest()
vars.memory = "You generate the following " + topic + " story concept :"
actionsubmit("", force_submit=True)
vars.memory = ""
#==================================================================#
# Final startup commands to launch Flask app
#==================================================================#
if __name__ == "__main__":
# Load settings from client.settings
loadmodelsettings()
loadsettings()
# Start Flask/SocketIO (Blocking, so this must be last method!)
#socketio.run(app, host='0.0.0.0', port=5000)
if(vars.remote):
from flask_cloudflared import _run_cloudflared
cloudflare = _run_cloudflared(5000)
with open('cloudflare.log', 'w') as cloudflarelog:
cloudflarelog.write("KoboldAI has finished loading and is available in the following link : " + cloudflare)
print(format(colors.GREEN) + "KoboldAI has finished loading and is available in the following link : " + cloudflare + format(colors.END))
socketio.run(app, host='0.0.0.0', port=5000)
else:
import webbrowser
webbrowser.open_new('http://localhost:5000')
print("{0}Server started!\rYou may now connect with a browser at http://127.0.0.1:5000/{1}".format(colors.GREEN, colors.END))
socketio.run(app)