#!/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 import contextlib 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", "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').float() 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("--ngrok", action='store_true', help="Optimizes KoboldAI for Remote Play using Ngrok") 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; if args.ngrok: 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", "EleutherAI/gpt-j-6B", "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 try: from transformers import GPTJModel except: pass 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 # Allow bad words filter to ban <|endoftext|> token import transformers.generation_logits_process def new_init(self, bad_words_ids: List[List[int]], eos_token_id: int): return new_init.old_init(self, bad_words_ids, -1) new_init.old_init = transformers.generation_logits_process.NoBadWordsLogitsProcessor.__init__ transformers.generation_logits_process.NoBadWordsLogitsProcessor.__init__ = new_init # 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} @contextlib.contextmanager def maybe_use_float16(always_use=False): if(always_use or (vars.hascuda and (vars.usegpu or vars.breakmodel))): original_dtype = torch.get_default_dtype() torch.set_default_dtype(torch.float16) yield True torch.set_default_dtype(original_dtype) else: yield False # If custom GPT Neo model was chosen if(vars.model == "NeoCustom"): model_config = open(vars.custmodpth + "/config.json", "r") js = json.load(model_config) with(maybe_use_float16()): 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) with(maybe_use_float16()): model = GPT2LMHeadModel.from_pretrained(vars.custmodpth, cache_dir="cache/", **maybe_low_cpu_mem_usage()) tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth, cache_dir="cache/") 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): with(maybe_use_float16()): 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) with(maybe_use_float16()): 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 None): global np if 'np' not in globals(): import numpy as np tensor = np.zeros((1, tpu_mtj_backend.params["d_model"]), dtype=np.float32) rows = tensor.shape[0] padding_amount = tpu_mtj_backend.params["seq"] - (tpu_mtj_backend.params["seq"] % -tpu_mtj_backend.params["cores_per_replica"]) - rows 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 = tensor 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 = tpu_mtj_backend.params["seq"] - (tpu_mtj_backend.params["seq"] % -tpu_mtj_backend.params["cores_per_replica"]) - rows 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): if(args.ngrok): from flask_ngrok import _run_ngrok cloudflare = _run_ngrok() else: 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)