#==================================================================# # KoboldAI Client # Version: 1.15.0 # By: KoboldAIDev #==================================================================# # External packages from os import path, getcwd import re import tkinter as tk from tkinter import messagebox import json import requests import html import argparse import sys import gc # KoboldAI import fileops import gensettings from utils import debounce import utils import breakmodel #==================================================================# # 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 = [ ["GPT Neo 1.3B", "EleutherAI/gpt-neo-1.3B", "8GB"], ["GPT Neo 2.7B", "EleutherAI/gpt-neo-2.7B", "16GB"], ["GPT-2", "gpt2", "1.2GB"], ["GPT-2 Med", "gpt2-medium", "2GB"], ["GPT-2 Large", "gpt2-large", "16GB"], ["GPT-2 XL", "gpt2-xl", "16GB"], ["InferKit API (requires API key)", "InferKit", ""], ["Custom Neo (eg Neo-horni)", "NeoCustom", ""], ["Custom GPT-2 (eg CloverEdition)", "GPT2Custom", ""], ["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 = [] # Array of 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 = [] # Tokenized array of badwords 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 = {} # Container for state of formatting options importnum = -1 # Selection on import popup list importjs = {} # Temporary storage for import data loadselect = "" # Temporary storage for filename to load svowname = "" # Filename that was flagged for overwrite confirm saveow = False # Whether or not overwrite confirm has been displayed genseqs = [] # Temporary storage for generated sequences useprompt = True # 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) 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) actionmode = 1 adventure = 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 #==================================================================# # Startup #==================================================================# # Parsing Parameters parser = argparse.ArgumentParser(description="KoboldAI Server") parser.add_argument("--remote", action='store_true', help="Optimizes KoboldAI for Remote Play") parser.add_argument("--model", help="Specify the Model Type to skip the Menu") parser.add_argument("--path", help="Specify the Path for local models (For model NeoCustom or GPT2Custom)") parser.add_argument("--cpu", action='store_true', help="By default unattended launches are on the GPU use this option to force CPU usage.") parser.add_argument("--breakmodel", action='store_true', help="For models that support GPU-CPU hybrid generation, use this feature instead of GPU or CPU generation") parser.add_argument("--breakmodel_layers", type=int, help="Specify the number of layers to commit to system RAM if --breakmodel is used") args = parser.parse_args() vars.model = args.model; if args.remote: vars.remote = True; # 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 Client!\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"]): # Test for GPU support import torch print("{0}Looking for GPU support...{1}".format(colors.PURPLE, colors.END), end="") vars.hascuda = torch.cuda.is_available() vars.bmsupported = vars.model in ("EleutherAI/gpt-neo-1.3B", "EleutherAI/gpt-neo-2.7B", "NeoCustom") if(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(args.cpu): vars.usegpu = False vars.breakmodel = False if(vars.bmsupported and args.breakmodel): vars.usegpu = False vars.breakmodel = True elif(vars.hascuda): if(vars.bmsupported): print(colors.YELLOW + "You're using a model that supports GPU-CPU hybrid generation!\nCurrently only GPT-Neo models and GPT-J-6B support this feature.") print("{0}Use GPU or CPU for generation?: (Default GPU){1}".format(colors.CYAN, colors.END)) if(vars.bmsupported): print(f" 1 - GPU\n 2 - CPU\n 3 - Both (slower than GPU-only but uses less VRAM)\n") else: print(" 1 - GPU\n 2 - CPU\n") 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): vars.breakmodel = False vars.usegpu = True genselected = True elif(genselect.isnumeric() and int(genselect) == 2): vars.breakmodel = False vars.usegpu = False genselected = True elif(vars.bmsupported and genselect.isnumeric() and int(genselect) == 3): vars.breakmodel = True 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("client.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 file = open("client.settings", "w") try: js = {"apikey": vars.apikey} file.write(json.dumps(js, indent=3)) finally: file.close() else: # Otherwise open it up file = open("client.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("client.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("client.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 file = open("client.settings", "w") try: js = {"oaiapikey": vars.oaiapikey} file.write(json.dumps(js, indent=3)) finally: file.close() else: # Otherwise open it up file = open("client.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("client.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 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"]): if(not vars.noai): print("{0}Initializing transformers, please wait...{1}".format(colors.PURPLE, colors.END)) from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel, GPTNeoForCausalLM, GPTNeoModel, AutoModel # If custom GPT Neo model was chosen if(vars.model == "NeoCustom"): model = GPTNeoForCausalLM.from_pretrained(vars.custmodpth) tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth) # Is CUDA available? If so, use GPU, otherwise fall back to CPU if(vars.hascuda): if(vars.usegpu): generator = pipeline('text-generation', model=model, tokenizer=tokenizer, device=0) elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel) n_layers = model.config.num_layers breakmodel.total_blocks = n_layers model.half().to('cpu') gc.collect() model.transformer.wte.to(breakmodel.gpu_device) model.transformer.ln_f.to(breakmodel.gpu_device) if(hasattr(model, 'lm_head')): model.lm_head.to(breakmodel.gpu_device) if(not hasattr(model.config, 'rotary') or not model.config.rotary): model.transformer.wpe.to(breakmodel.gpu_device) gc.collect() if(args.breakmodel_layers is not None): breakmodel.ram_blocks = max(0, min(n_layers, args.breakmodel_layers)) else: print(colors.CYAN + "\nHow many layers would you like to put into system RAM?") print("The more of them you put into system RAM, the slower it will run,") print("but it will require less VRAM") print("(roughly proportional to number of layers).") print(f"This model has{colors.YELLOW} {n_layers} {colors.CYAN}layers.{colors.END}\n") while(True): layerselect = input("# of layers> ") if(layerselect.isnumeric() and 0 <= int(layerselect) <= n_layers): breakmodel.ram_blocks = int(layerselect) break else: print(f"{colors.RED}Please enter an integer between 0 and {n_layers}.{colors.END}") print(f"{colors.PURPLE}Will commit{colors.YELLOW} {breakmodel.ram_blocks} {colors.PURPLE}of{colors.YELLOW} {n_layers} {colors.PURPLE}layers to system RAM.{colors.END}") GPTNeoModel.forward = breakmodel.new_forward generator = model.generate else: generator = pipeline('text-generation', model=model, tokenizer=tokenizer) else: generator = pipeline('text-generation', model=model, tokenizer=tokenizer) # If custom GPT2 model was chosen elif(vars.model == "GPT2Custom"): model = GPT2LMHeadModel.from_pretrained(vars.custmodpth) tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth) # Is CUDA available? If so, use GPU, otherwise fall back to CPU if(vars.hascuda and vars.usegpu): generator = pipeline('text-generation', model=model, tokenizer=tokenizer, device=0) else: generator = pipeline('text-generation', model=model, tokenizer=tokenizer) # 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) if(vars.hascuda): if(vars.usegpu): generator = pipeline('text-generation', model=vars.model, device=0) elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel) model = AutoModel.from_pretrained(vars.model) n_layers = model.config.num_layers breakmodel.total_blocks = n_layers model.half().to('cpu') gc.collect() model.transformer.wte.to(breakmodel.gpu_device) model.transformer.ln_f.to(breakmodel.gpu_device) if(hasattr(model, 'lm_head')): model.lm_head.to(breakmodel.gpu_device) if(not hasattr(model.config, 'rotary') or not model.config.rotary): model.transformer.wpe.to(breakmodel.gpu_device) gc.collect() if(args.breakmodel_layers is not None): breakmodel.ram_blocks = max(0, min(n_layers, args.breakmodel_layers)) else: print(colors.CYAN + "\nHow many layers would you like to put into system RAM?") print("The more of them you put into system RAM, the slower it will run,") print("but it will require less VRAM") print("(roughly proportional to number of layers).") print(f"This model has{colors.YELLOW} {n_layers} {colors.CYAN}layers.{colors.END}\n") while(True): layerselect = input("# of layers> ") if(layerselect.isnumeric() and 0 <= int(layerselect) <= n_layers): breakmodel.ram_blocks = int(layerselect) break else: print(f"{colors.RED}Please enter an integer between 0 and {n_layers}.{colors.END}") print(f"{colors.PURPLE}Will commit{colors.YELLOW} {breakmodel.ram_blocks} {colors.PURPLE}of{colors.YELLOW} {n_layers} {colors.PURPLE}layers to system RAM.{colors.END}") GPTNeoModel.forward = breakmodel.new_forward generator = model.generate else: generator = pipeline('text-generation', model=vars.model) else: generator = pipeline('text-generation', model=vars.model) # Suppress Author's Note by flagging square brackets 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") # Set up Flask routes @app.route('/') @app.route('/index') def index(): return render_template('index.html') #============================ 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'}) if(vars.remote): emit('from_server', {'cmd': 'runs_remotely'}) if(not vars.gamestarted): setStartState() sendsettings() refresh_settings() sendwi() vars.mode = "play" else: # Game in session, send current game data and ready state to browser refresh_story() sendsettings() refresh_settings() sendwi() if(vars.mode == "play"): if(not vars.aibusy): emit('from_server', {'cmd': 'setgamestate', 'data': 'ready'}, broadcast=True) else: emit('from_server', {'cmd': 'setgamestate', 'data': 'wait'}, broadcast=True) elif(vars.mode == "edit"): emit('from_server', {'cmd': 'editmode', 'data': 'true'}, broadcast=True) elif(vars.mode == "memory"): emit('from_server', {'cmd': 'memmode', 'data': 'true'}, broadcast=True) elif(vars.mode == "wi"): emit('from_server', {'cmd': 'wimode', 'data': 'true'}, broadcast=True) #==================================================================# # Event triggered when browser SocketIO sends data to the server #==================================================================# @socketio.on('message') def get_message(msg): print("{0}Data recieved:{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(msg['cmd'] == 'savetofile'): savetofile() elif(msg['cmd'] == 'loadfromfile'): loadfromfile() elif(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'] == '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'] == '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'] == 'loadselect'): vars.loadselect = msg["data"] elif(msg['cmd'] == 'loadrequest'): loadRequest(getcwd()+"/stories/"+vars.loadselect+".json") 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() refresh_story() elif(msg['cmd'] == 'importwi'): wiimportrequest() #==================================================================# # Send start message and tell Javascript to set UI state #==================================================================# def setStartState(): txt = "Welcome to KoboldAI Client! You are running "+vars.model+".
" 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 # Write it file = open("client.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("client.settings")): # Read file contents into JSON object file = open("client.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"] file.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): # Ignore new submissions if the AI is currently busy if(vars.aibusy): return set_aibusy(1) vars.actionmode = actionmode # "Action" mode if(actionmode == 1): data = data.strip().lstrip('>') data = re.sub(r'\n+', ' ', 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): # 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': vars.gamestarted, '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 vars.actions.append(data) if(not vars.noai): # Off to the tokenizer! calcsubmit(data) emit('from_server', {'cmd': 'scrolldown', 'data': ''}, broadcast=True) else: refresh_story() 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 set_aibusy(1) # Remove last action if possible and resubmit if(len(vars.actions) > 0): vars.actions.pop() refresh_story() calcsubmit('') #==================================================================# # #==================================================================# def actionback(): if(vars.aibusy): return # Remove last index of actions and refresh game screen if(len(vars.actions) > 0): vars.actions.pop() refresh_story() #==================================================================# # Take submitted text and build the text to be given to generator #==================================================================# def calcsubmit(txt): anotetxt = "" # Placeholder for Author's Note text lnanote = 0 # Placeholder for Author's Note length 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) # Scan for WorldInfo matches winfo = checkworldinfo(txt) # 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" # For all transformers models if(vars.model != "InferKit"): anotetkns = [] # Placeholder for Author's Note tokens # Calculate token budget prompttkns = tokenizer.encode(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) if(vars.useprompt): budget = vars.max_length - lnprompt - lnmem - lnanote - lnwi - vars.genamt else: budget = vars.max_length - lnmem - lnanote - lnwi - vars.genamt if(actionlen == 0): # First/Prompt action subtxt = vars.memory + winfo + anotetxt + vars.prompt lnsub = lnmem + lnwi + lnprompt + lnanote if(not vars.model in ["Colab", "OAI"]): generate(subtxt, lnsub+1, lnsub+vars.genamt) elif(vars.model == "Colab"): sendtocolab(subtxt, lnsub+1, lnsub+vars.genamt) elif(vars.model == "OAI"): oairequest(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 for n in range(actionlen): if(budget <= 0): break acttkns = tokenizer.encode(vars.actions[(-1-n)]) 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 # 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) if(not vars.model in ["Colab", "OAI"]): generate ( tokenizer.decode(tokens), ln+1, ln+vars.genamt ) elif(vars.model == "Colab"): sendtocolab( tokenizer.decode(tokens), ln+1, ln+vars.genamt ) elif(vars.model == "OAI"): oairequest( tokenizer.decode(tokens), ln+1, ln+vars.genamt ) # 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.prompt) - len(anotetxt) - len(mem) - len(winfo) - 1 else: budget = vars.ikmax - len(anotetxt) - len(mem) - len(winfo) - 1 subtxt = "" prompt = vars.prompt for n in range(actionlen): if(budget <= 0): break actlen = len(vars.actions[(-1-n)]) if(actlen < budget): subtxt = vars.actions[(-1-n)] + subtxt budget -= actlen else: count = budget * -1 subtxt = vars.actions[(-1-n)][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.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 # 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, min, max): print("{0}Min:{1}, Max:{2}, Txt:{3}{4}".format(colors.YELLOW, min, max, 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: top_p = vars.top_p if vars.top_p > 0.0 else None top_k = vars.top_k if vars.top_k > 0 else None tfs = vars.tfs if vars.tfs > 0.0 else None # generator() only accepts a torch tensor of tokens (long datatype) as # its first argument if we're using breakmodel, otherwise a string # is fine if(vars.hascuda and vars.breakmodel): gen_in = tokenizer.encode(txt, return_tensors="pt", truncation=True).long().to(breakmodel.gpu_device) else: gen_in = txt with torch.no_grad(): genout = generator( gen_in, do_sample=True, min_length=min, max_length=max, repetition_penalty=vars.rep_pen, top_p=top_p, top_k=top_k, tfs=tfs, temperature=vars.temp, bad_words_ids=vars.badwordsids, use_cache=True, return_full_text=False, num_return_sequences=vars.numseqs ) 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 if(vars.hascuda and vars.breakmodel): genout = [{"generated_text": tokenizer.decode(tokens[len(gen_in[0])-len(tokens):])} 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 vars.actions.append(genout) refresh_story() emit('from_server', {'cmd': 'texteffect', 'data': len(vars.actions)}, 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) # Refresh story for any input text refresh_story() #==================================================================# # 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"]) refresh_story() emit('from_server', {'cmd': 'texteffect', 'data': len(vars.actions)}, 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': len(vars.actions)}) 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) #==================================================================# # Replaces returns and newlines with HTML breaks #==================================================================# def formatforhtml(txt): return txt.replace("\\r", "
").replace("\\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) return txt #==================================================================# # Sends the current story content to the Game Screen #==================================================================# def refresh_story(): text_parts = ['', html.escape(vars.prompt), ''] for idx, item in enumerate(vars.actions, start=1): if vars.adventure: # Add special formatting to adventure actions item = vars.acregex_ui.sub('\\1', html.escape(item)) text_parts.extend(('', item, '')) emit('from_server', {'cmd': 'updatescreen', 'gamestarted': vars.gamestarted, 'data': formatforhtml(''.join(text_parts))}, 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': '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) # 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): if(vars.editln == 0): vars.prompt = data else: vars.actions[vars.editln-1] = data vars.mode = "play" refresh_story() emit('from_server', {'cmd': 'texteffect', 'data': vars.editln}, broadcast=True) emit('from_server', {'cmd': 'editmode', 'data': 'false'}) #==================================================================# # #==================================================================# def deleterequest(): # Don't delete prompt if(vars.editln == 0): # Send error message pass else: del vars.actions[vars.editln-1] vars.mode = "play" refresh_story() emit('from_server', {'cmd': 'editmode', 'data': 'false'}) #==================================================================# # #==================================================================# def inlineedit(chunk, data): chunk = int(chunk) if(chunk == 0): vars.prompt = data else: vars.actions[chunk-1] = data refresh_story() emit('from_server', {'cmd': 'texteffect', 'data': chunk}, broadcast=True) emit('from_server', {'cmd': 'editmode', 'data': 'false'}, broadcast=True) #==================================================================# # #==================================================================# def inlinedelete(chunk): chunk = int(chunk) # Don't delete prompt if(chunk == 0): # Send error message refresh_story() 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] refresh_story() 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}) 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} 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}, broadcast=True) #==================================================================# # 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"] # 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): # Dont go any further if WI is empty if(len(vars.worldinfo) == 0): return # 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(txt != "" and vars.prompt != txt): txt = "" depth += 1 if(ln >= depth): txt = "".join(vars.actions[(depth*-1):]) elif(ln > 0): txt = vars.prompt + "".join(vars.actions[(depth*-1):]) elif(ln == 0): txt = vars.prompt # Scan text for matches on WI keys wimem = "" for wi in vars.worldinfo: 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 = True break if found: break else: wimem = wimem + wi["content"] + "\n" break return wimem #==================================================================# # 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': ''}, broadcast=True) #==================================================================# # 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) refresh_story() emit('from_server', {'cmd': 'texteffect', 'data': len(vars.actions)}, 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) refresh_story() emit('from_server', {'cmd': 'texteffect', 'data': len(vars.actions)}, 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 saveRequest(getcwd()+"/stories/"+name+".json") emit('from_server', {'cmd': 'hidesaveas', 'data': ''}) vars.saveow = False vars.svowname = "" else: # File exists, prompt for overwrite vars.saveow = True vars.svowname = name 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 # Build json to write js = {} js["gamestarted"] = vars.gamestarted js["prompt"] = vars.prompt js["memory"] = vars.memory js["authorsnote"] = vars.authornote js["actions"] = vars.actions js["worldinfo"] = [] # Extract only the important bits of WI for wi in vars.worldinfo: if(wi["key"] != ""): js["worldinfo"].append({ "key": wi["key"], "keysecondary": wi["keysecondary"], "content": wi["content"], "selective": wi["selective"] }) # Write it file = open(savpath, "w") try: file.write(json.dumps(js, indent=3)) finally: file.close() print("{0}Story saved to {1}!{2}".format(colors.GREEN, path.basename(savpath), colors.END)) #==================================================================# # Load a saved story via file browser #==================================================================# def getloadlist(): emit('from_server', {'cmd': 'buildload', 'data': fileops.getstoryfiles()}) #==================================================================# # 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): if(loadpath): # Leave Edit/Memory mode before continuing exitModes() # Read file contents into JSON object file = open(loadpath, "r") js = json.load(file) # Copy file contents to vars vars.gamestarted = js["gamestarted"] vars.prompt = js["prompt"] vars.memory = js["memory"] vars.actions = js["actions"] vars.worldinfo = [] vars.lastact = "" vars.lastctx = "" # 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) }) num += 1 file.close() # Save path for save button vars.savedir = loadpath # Clear loadselect var vars.loadselect = "" # Refresh game screen sendwi() 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, path.basename(loadpath), colors.END)) #==================================================================# # 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 = [] 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) }) num += 1 # Clear import data vars.importjs = {} # Reset current save vars.savedir = getcwd()+"\stories" # Refresh game screen sendwi() 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 = [] 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) }) num += 1 # Reset current save vars.savedir = getcwd()+"\stories" # Refresh game screen sendwi() 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) }) 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 = [] vars.authornote = "" vars.worldinfo = [] vars.lastact = "" vars.lastctx = "" # Reset current save vars.savedir = getcwd()+"\stories" # Refresh game screen sendwi() setStartState() def randomGameRequest(topic): newGameRequest() vars.memory = "You generate the following " + topic + " story concept :" actionsubmit("") vars.memory = "" #==================================================================# # Final startup commands to launch Flask app #==================================================================# if __name__ == "__main__": # Load settings from client.settings loadsettings() # Start Flask/SocketIO (Blocking, so this must be last method!) 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, host='0.0.0.0', port=5000) if(vars.remote): from flask_cloudflared import start_cloudflared start_cloudflared(5000) socketio.run(app, host='0.0.0.0', port=5000) else: import webbrowser webbrowser.open_new('http://localhost:5000') socketio.run(app)