Add dungeon game code.
This commit is contained in:
Carlos Bazaga
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"""AI Mastered Dungeon Extraction Game initialization module."""
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from logging import basicConfig, getLogger
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from dotenv import load_dotenv
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# Environment initialization.
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load_dotenv(override=True)
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# Setup the global logger.
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LOG_STYLE = '{'
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LOG_LEVEL = 'INFO'
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LOG_FORMAT = ('{asctime} {levelname:<8} {processName}({process}) '
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'{threadName} {name} {lineno} "{message}"')
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basicConfig(level=LOG_LEVEL, style='{', format=LOG_FORMAT)
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getLogger(__name__).info('INITIALIZED GAME LOGGER')
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"""AI Mastered Dungeon Extraction Game main entrypoint module."""
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from logging import getLogger
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from .config import SCENE_PROMPT, SCENE_STYLE, START_SCENE, STORYTELLER_PROMPT
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from .gameplay import Gameplay_Config, get_gameplay_function
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from .illustrator import draw_dalle_2, draw_dalle_3, draw_gemini, draw_gpt, draw_grok
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from .illustrator import draw_grok_x
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from .interface import Interface_Config, get_interface
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from .storyteller import narrate
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# Choose draw function.
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DRAW_FUNCTION = draw_dalle_2
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# Configure the game.
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game_config = Gameplay_Config(
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draw_func=DRAW_FUNCTION,
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narrate_func=narrate,
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scene_style=SCENE_STYLE,
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scene_prompt=SCENE_PROMPT,
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storyteller_prompt=STORYTELLER_PROMPT,
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error_img='images/machine.jpg',
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error_narrator='NEURAL SINAPSIS ERROR\n\n{ex}\n\nEND OF LINE\n\nRE-SUBMIT_',
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error_illustrator='NEURAL PROJECTION ERROR\n\n{ex}\n\nEND OF LINE\n\nRE-SUBMIT_',)
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ui_config = Interface_Config(
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start_img='images/chair.jpg',
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place_img='images/machine.jpg',
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description_label='Cognitive Projection',
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title_label='The Neural Nexus',
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input_button='Imprint your will',
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input_label='Cognitive Imprint',
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input_command='Awaiting neural imprint…',
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start_scene=START_SCENE)
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_logger = getLogger(__name__)
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if __name__ == '__main__':
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_logger.info('STARTING GAME...')
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gameplay_function = get_gameplay_function(game_config)
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get_interface(gameplay_function, ui_config).launch(inbrowser=True, inline=False)
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146
community-contributions/dungeon_extration_game/game/config.py
Normal file
146
community-contributions/dungeon_extration_game/game/config.py
Normal file
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"""AI Mastered Dungeon Extraction Game Configuration module."""
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from logging import getLogger
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# Define a sample scene description for testing purposes.
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SAMPLE_SCENE = '''A shadow-drenched chamber lies buried deep within the bowels of an
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ancient castle, its silence broken only by the faint creak of age-old stone.
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The walls, cloaked in thick cobwebs, seem to whisper forgotten stories,
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while the air hangs heavy with the pungent scent of mildew and centuries of decay.
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Dust dances in the dim light that filters through cracks in the ceiling,
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casting eerie patterns across the cold floor. As your eyes adjust to the gloom,
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you notice a narrow door to the north, slightly ajar, as if inviting or warning, and
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in the far corner, half-swallowed by darkness, a figure stands motionless.
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Its presence is felt before it's seen, watching, waiting'''
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# Define the starting scene text.
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# This is intentionally excluded from the model’s narrative context, the 'history',
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# by design, to prevent potential leakage into the game’s storyline.
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START_SCENE = '''You stand before the Neural Nexus, a convergence of arcane circuitry
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and deep cognition. It doesn't operate with buttons or commands. It responds to intent.
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Forged in forgotten labs and powered by living code, the Nexus is designed to interface
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directly with your mind. Not to simulate reality, but to generate it.
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The Nexus does not load worlds. It listens.
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If you choose to sit, the Nexus will initiate full neural synchronization.
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Your thoughts will become terrain. Your instincts, adversaries.
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Your imagination, the architect.
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Once the link is active, you must describe the nature of the challenge you wish to face.
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A shifting maze? A sentient machine? A trial of memory and time?
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Speak it aloud or think it clearly. The Nexus will listen.
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🜁 When you're ready, take your seat. The system awaits your signal...'''
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# Define an image prompt, mind that Grok or Dalle·2 models have a 1024 characters limit.
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SCENE_PROMPT = '''Render a detailed image of the following scene:
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"""{scene_description}"""
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Stay strictly faithful to the description, no added elements, characters, doors, or text.
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Do not depict the adventurer; show only what they see.
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Use the "{scene_style}" visual style.
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'''
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# Define the scene drawing style, can be a simple word or a short sentence.
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SCENE_STYLE = 'Photorealistic'
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# Set a Storyteller scene descriptions size limit to keep the draw prompt in range.
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STORYTELLER_LIMIT = 730
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# Define the storyteller behaviour. Remember to specify a limited scene length.
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STORYTELLER_PROMPT = f"""
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You are a conversational dungeon crawler game master that describes scenes and findings
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based on the player's declared actions.
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Your descriptions will always adhere to the OpenAI's safety system rules so they can be
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drawn by Dall·E or other image models.
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The game start with the player, the adventurer, on a random room and the objetive is
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escape the dungeon with the most treasures possible before dying.
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You will describe the environment, enemies, and items to the player.
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Your descriptions will always adhere to the OpenAI's safety system rules so they can be
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drawn by Dall·E or other image models.
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You will ensure the game is engaging and fun, but at the same time risky by increasing
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difficult the more the time the adventurer stays inside the dungeon, if the adventurer
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takes too much risks he may even die, also bigger risks implies bigger rewards.
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You will control the time the adventurer is in, once enough time has passer he will die,
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may it be a collapse, explosion, flooding, up to you.
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The more deep inside the adventurer is the most it will be represented on descriptions by
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more suffocating environments, more dark, that kind of things, let the player feel the
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risk on the ambience, make him fear.
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Same applies with time, the most time has passed the environment and situation will warn
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him, or at least give clues that time is running and the end may be close soon, make him
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stress.
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While leaving the dungeon, the more deep inside the adventurer is, the more steps must
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take to get out, although some shortcuts may be available at your discretion.
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Once the user exits the dungeon, at deepness zero, the game is over, give him a score
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based on his actions, treasures and combat successes along the usual description.
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Don't be too much protective but not also a cruel master, just be fair.
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Your responses must always be a JSON with the following structure:
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{{
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"game_over" : "A boolean value indicating the game is over."
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"scene_description" : "The detailed scene description. Max {STORYTELLER_LIMIT} chars"
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"dungeon_deepness" : "How deep the adventurer has gone into the dungeon. initially 3"
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"adventure_time" : "How much minutes has passed since the start of the adventure."
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"adventurer_status" : {{
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"health": "Current health of the adventurer as an int, initially 100"
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"max_health": "Maximum health of the adventurer as an int, initially 100"
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"level": "Current adventurer's leve as an int, initially 1"
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"experience": "Current adventurer experience as an int, initially 0"}}
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"inventory_status" : "A list of inventory items, initially empty"
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}}
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Remember to cap the "scene_description" to {STORYTELLER_LIMIT} characters maximum"
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You will respond to the adventurer's actions and choices.
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You wont let the player to trick you by stating actions that do not fit the given scene.
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* If he attempts to do so just politely tell him he can not do that there with the
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description of the scene he is in.
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You will keep track of the adventurer's health.
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* Health can go down due to combat, traps, accidents, etc.
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* If Health reaches zero the adventurer dies and it's a "game over".
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* Several items, places, and allowed actions may heal the adventurer.
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* Some items, enchants, and such things may increase the adventurer's maximum health.
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You will keep track of the player's progress.
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You will keep track of adventurer level and experience,
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* He gains experience by finding items, solving puzzles, by combat with enemies, etc.
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* Each (100 + 100 * current_level) experience the adventurer will gain a level.
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* Gaining a level resets his experience to 0.
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You will keep track of the player's inventory.
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* Only add items to inventory if user explicitly says he picks them or takes an
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action that ends with the item on his possession.
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* Inventory items will reflect quantity and will never display items with zero units.
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* Example of inventory: ["Gold coins (135)", "Diamonds (2)", "Log sword (1)"]
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* Be reasonable with the inventory capacity, don't bee to strict but things
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like a big marble statue can't be taken, use common sense.
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You will use a turn-based system where the player and enemies take turns acting.
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* Players will lose health when receiving hits on combat.
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* The more damage they take the less damage they do, same applies to enemies.
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* Reaching to zero health or lees implies the adventurer has die.
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"""
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_logger = getLogger(__name__)
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if (max_image_prompt := len(SCENE_PROMPT) + len(SCENE_STYLE) + STORYTELLER_LIMIT) > 1024:
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_logger.warning(f'ESTIMATED SCENE PROMPT MAX SIZE: {max_image_prompt}')
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else:
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_logger.info(f'ESTIMATED SCENE PROMPT MAX SIZE: {max_image_prompt}')
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"""AI Mastered Dungeon Extraction Game gameplay package."""
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from .gameplay import Gameplay_Config, get_gameplay_function
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__all__ = ['Gameplay_Config', 'get_gameplay_function']
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"""AI Mastered Dungeon Extraction Game gameplay module."""
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from logging import getLogger
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from typing import Callable, NamedTuple
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# Define gameplay's configuration class.
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class Gameplay_Config(NamedTuple):
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"""Gradio interface configuration class."""
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draw_func: Callable
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narrate_func: Callable
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scene_style: str
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scene_prompt: str
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storyteller_prompt: str
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error_img: str
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error_narrator: str
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error_illustrator: str
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# Define Game's functions.
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def get_gameplay_function(config: Gameplay_Config):
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"""Return a pre-configured turn gameplay function."""
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def gameplay_function(message, history):
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"""Generate Game Master's response and draw the scene image."""
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# Request narration.
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_logger.info(f'NARRATING SCENE...')
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try:
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response = config.narrate_func(message, history, config.storyteller_prompt)
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except Exception as ex:
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scene = config.error_img
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response = config.error_narrator.format(ex=ex)
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_logger.error(f'ERROR NARRATING SCENE: {ex}\n{message}\n{history}')
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return scene, response, history, message
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# Update history.
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history.append({"role": "user", "content": message})
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history.append({"role": "assistant", "content": response.model_dump_json()})
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# Draw scene.
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_logger.info(f'DRAWING SCENE...')
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try:
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scene_data = {'scene_description': response.scene_description,
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'scene_style': config.scene_style}
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scene_prompt = config.scene_prompt.format(**scene_data)
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_logger.info(f'PROMPT BODY IS: \n\n{scene_prompt}\n')
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_logger.info(f'PROMPT LENGTH IS: {len(scene_prompt)}')
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scene = config.draw_func(scene_prompt)
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except Exception as ex:
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scene = config.error_img
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response = config.error_illustrator.format(ex=ex)
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_logger.warning(f'ERROR DRAWING SCENE: {ex}')
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return scene, response, history, ''
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return gameplay_function
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_logger = getLogger(__name__)
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"""AI Mastered Dungeon Extraction Game scenes illustrator package."""
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from .illustrator_dalle_2 import draw as draw_dalle_2
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from .illustrator_dalle_3 import draw as draw_dalle_3
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from .illustrator_gemini import draw as draw_gemini
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from .illustrator_gpt import draw as draw_gpt
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from .illustrator_grok import draw as draw_grok
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from .illustrator_grok import draw_x as draw_grok_x
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__all__ = ['draw_dalle_2', 'draw_dalle_3', 'draw_gemini',
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'draw_gpt', 'draw_grok', 'draw_grok_x']
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"""AI Mastered Dungeon Extraction Game scenes illustrator using OpenAI's DALL·E 3."""
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import base64
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from io import BytesIO
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from dotenv import load_dotenv
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from openai import OpenAI
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from PIL import Image
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# Environment initialization.
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load_dotenv(override=True)
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# Define global defaults.
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MODEL = 'dall-e-2'
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# Client instantiation.
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CLIENT = OpenAI()
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# Function definition.
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def draw(prompt, size=(1024, 1024), client=CLIENT, model=MODEL, quality=None):
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"""Generate an image based on the prompt."""
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# Generate image.
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response = client.images.generate(
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model=model, prompt=prompt, n=1,
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size=f'{size[0]}x{size[1]}',
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response_format='b64_json')
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# Process response.
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return Image.open(BytesIO(base64.b64decode(response.data[0].b64_json)))
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@@ -0,0 +1,32 @@
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"""AI Mastered Dungeon Extraction Game scenes illustrator using OpenAI's DALL·E 3."""
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import base64
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from io import BytesIO
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from dotenv import load_dotenv
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from openai import OpenAI
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from PIL import Image
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# Environment initialization.
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load_dotenv(override=True)
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# Define global defaults.
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MODEL = 'dall-e-3'
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QUALITY = 'standard' # Set to 'hd' for more quality, but double the costs.
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# Client instantiation.
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CLIENT = OpenAI()
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# Function definition.
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def draw(prompt, size=(1024, 1024), client=CLIENT, model=MODEL, quality=QUALITY):
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"""Generate an image based on the prompt."""
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# Generate image.
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response = client.images.generate(
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model=model, prompt=prompt, n=1,
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size=f'{size[0]}x{size[1]}',
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quality=quality,
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response_format='b64_json')
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# Process response.
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return Image.open(BytesIO(base64.b64decode(response.data[0].b64_json)))
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@@ -0,0 +1,36 @@
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"""AI Mastered Dungeon Extraction Game scenes illustrator using Google's Gemini."""
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from io import BytesIO
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from dotenv import load_dotenv
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from google import genai # New Google's SDK 'genai' to replace 'generativeai'.
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from PIL import Image
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# Environment initialization.
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load_dotenv(override=True)
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# Define globals.
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MODEL = 'gemini-2.5-flash-image-preview'
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# Client instantiation.
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CLIENT = genai.Client()
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# Function definition.
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def draw(prompt, size=(1024, 1024), client=CLIENT, model=MODEL):
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"""Generate an image based on the prompt."""
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# Generate image.
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response = client.models.generate_content(
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model=model, contents=[prompt])
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# Process response.
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for part in response.candidates[0].content.parts:
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if part.text is not None:
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print(part.text)
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elif part.inline_data is not None:
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image_data = part.inline_data.data
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# Open the generated image.
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generated_image = Image.open(BytesIO(image_data))
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# Resize the image to the specified dimensions.
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resized_image = generated_image.resize(size)
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return resized_image
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@@ -0,0 +1,32 @@
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"""AI Mastered Dungeon Extraction Game scenes illustrator using OpenAI's GPT."""
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import base64
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from io import BytesIO
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from dotenv import load_dotenv
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from openai import OpenAI
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from PIL import Image
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# Environment initialization.
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load_dotenv(override=True)
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# Define global defaults.
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MODEL = 'gpt-image-1'
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QUALITY = 'low'
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# Client instantiation.
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CLIENT = OpenAI()
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# Function definition.
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def draw(prompt, size=(1024, 1024), client=CLIENT, model=MODEL, quality=QUALITY):
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"""Generate an image based on the prompt."""
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# Generate image.
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response = client.images.generate(
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model=model, prompt=prompt, n=1,
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size=f'{size[0]}x{size[1]}',
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output_format='png',
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quality=quality)
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# Process response.
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return Image.open(BytesIO(base64.b64decode(response.data[0].b64_json)))
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@@ -0,0 +1,47 @@
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"""AI Mastered Dungeon Extraction Game scenes illustrator using xAI's Grok."""
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import base64
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import os
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from io import BytesIO
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from dotenv import load_dotenv
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from openai import OpenAI
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from PIL import Image
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from xai_sdk import Client
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# Environment initialization.
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load_dotenv(override=True)
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# Define global defaults.
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MODEL = 'grok-2-image'
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QUALITY = None
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# Client instantiation.
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XAI_API_KEY = os.getenv('XAI_API_KEY')
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CLIENT = OpenAI(api_key=XAI_API_KEY, base_url="https://api.x.ai/v1")
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# Function definition.
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def draw(prompt, size=(1024, 1024), client=CLIENT, model=MODEL, quality=QUALITY):
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"""Generate an image based on the prompt."""
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# Generate image.
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response = client.images.generate(
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model=model, prompt=prompt, n=1,
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response_format='b64_json')
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# Process response.
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return Image.open(BytesIO(base64.b64decode(response.data[0].b64_json)))
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||||
# xAI SDK Version:
|
||||
CLIENT_X = Client(api_key=XAI_API_KEY)
|
||||
|
||||
|
||||
def draw_x(prompt, size=(1024, 1024), client=CLIENT_X, model=MODEL, quality=QUALITY):
|
||||
"""Generate an image based on the prompt."""
|
||||
# Generate image.
|
||||
response = client.image.sample(
|
||||
model=model, prompt=prompt,
|
||||
image_format='base64')
|
||||
# Process response.
|
||||
return Image.open(BytesIO(response.image))
|
||||
@@ -0,0 +1,6 @@
|
||||
"""AI Mastered Dungeon Extraction Game interface package."""
|
||||
|
||||
from .interface import Interface_Config, get_interface
|
||||
|
||||
|
||||
__all__ = ['Interface_Config', 'get_interface']
|
||||
@@ -0,0 +1,50 @@
|
||||
"""AI Mastered Dungeon Extraction Game Gradio interface module."""
|
||||
|
||||
from typing import NamedTuple
|
||||
|
||||
import gradio as gr
|
||||
|
||||
|
||||
# Define interface's configuration class.
|
||||
class Interface_Config(NamedTuple):
|
||||
"""Gradio interface configuration class."""
|
||||
start_img: str
|
||||
place_img: str
|
||||
description_label: str
|
||||
title_label: str
|
||||
input_button: str
|
||||
input_label: str
|
||||
input_command: str
|
||||
start_scene: str
|
||||
|
||||
|
||||
# Define game's interface.
|
||||
def get_interface(submit_function, config: Interface_Config):
|
||||
"""Create a game interface service."""
|
||||
with gr.Blocks(title=config.title_label) as ui:
|
||||
# Title.
|
||||
gr.Markdown(config.title_label)
|
||||
# Hidden state for history.
|
||||
history_state = gr.State([])
|
||||
# Scene's image.
|
||||
scene_image = gr.Image(
|
||||
label="Scene", value=config.start_img, placeholder=config.place_img,
|
||||
type="pil", show_label=False, show_copy_button=True)
|
||||
# Scene's description.
|
||||
description_box = gr.Textbox(
|
||||
label=config.description_label, value=config.start_scene,
|
||||
interactive=False, show_copy_button=True)
|
||||
# Player's command.
|
||||
user_input = gr.Textbox(
|
||||
label=config.input_label, placeholder=config.input_command)
|
||||
user_input.submit(
|
||||
fn=submit_function,
|
||||
inputs=[user_input, history_state],
|
||||
outputs=[scene_image, description_box, history_state, user_input])
|
||||
# Submit button.
|
||||
submit_btn = gr.Button(config.input_button)
|
||||
submit_btn.click(
|
||||
fn=submit_function,
|
||||
inputs=[user_input, history_state],
|
||||
outputs=[scene_image, description_box, history_state, user_input])
|
||||
return ui
|
||||
@@ -0,0 +1,6 @@
|
||||
"""AI Mastered Dungeon Extraction Game Storyteller package."""
|
||||
|
||||
from .storyteller import narrate
|
||||
|
||||
|
||||
__all__ = ['narrate']
|
||||
@@ -0,0 +1,67 @@
|
||||
"""AI Mastered Dungeon Extraction Game Storyteller using OpenAI's GPT."""
|
||||
|
||||
from typing import List
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from openai import OpenAI
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from ..config import STORYTELLER_LIMIT
|
||||
from .tools import handle_tool_call, tools
|
||||
|
||||
|
||||
# Environment initialization.
|
||||
load_dotenv(override=True)
|
||||
|
||||
# Define globals.
|
||||
MODEL = 'gpt-4o-mini'
|
||||
|
||||
# Client instantiation.
|
||||
CLIENT = OpenAI()
|
||||
|
||||
|
||||
# Define Pydantic model classes for response format parsing.
|
||||
class _character_sheet(BaseModel):
|
||||
health: int
|
||||
max_health: int
|
||||
level: int
|
||||
experience: int
|
||||
|
||||
|
||||
class _response_format(BaseModel):
|
||||
game_over: bool
|
||||
scene_description: str = Field(..., max_length=STORYTELLER_LIMIT)
|
||||
dungeon_deepness: int
|
||||
adventure_time: int
|
||||
adventurer_status: _character_sheet
|
||||
inventory_status: List[str]
|
||||
|
||||
def __str__(self):
|
||||
"""Represent response as a string."""
|
||||
response_view = (
|
||||
f'{self.scene_description}'
|
||||
f'\n\nInventory: {self.inventory_status}'
|
||||
f'\n\nAdventurer: {self.adventurer_status}'
|
||||
f'\n\nTime: {self.adventure_time}'
|
||||
f'\n\nDeepness: {self.dungeon_deepness}'
|
||||
f'\n\nGame Over: {self.game_over}')
|
||||
return response_view
|
||||
|
||||
|
||||
# Function definition.
|
||||
def narrate(message, history, system_message, client=CLIENT, model=MODEL):
|
||||
"""Chat with the game engine."""
|
||||
messages = ([{"role": "system", "content": system_message}] + history
|
||||
+ [{"role": "user", "content": message}])
|
||||
response = client.chat.completions.parse(model=model, messages=messages, tools=tools,
|
||||
response_format=_response_format)
|
||||
# Process tool calls.
|
||||
if response.choices[0].finish_reason == "tool_calls":
|
||||
message = response.choices[0].message
|
||||
tool_response = handle_tool_call(message)
|
||||
messages.append(message)
|
||||
messages.append(tool_response)
|
||||
response = client.chat.completions.parse(model=model, messages=messages,
|
||||
response_format=_response_format)
|
||||
# Return game's Master response.
|
||||
return response.choices[0].message.parsed
|
||||
@@ -0,0 +1,81 @@
|
||||
"""AI Mastered Dungeon Extraction Game storyteller tools module WIP."""
|
||||
|
||||
from json import loads
|
||||
|
||||
from openai.types.chat import ChatCompletionMessage
|
||||
from openai.types.chat import ChatCompletionMessageFunctionToolCall
|
||||
from openai.types.chat.chat_completion_message_function_tool_call import Function
|
||||
|
||||
|
||||
# Tools declaration for future use. (E.g. Tools may handle user status and inventory)
|
||||
tools = []
|
||||
|
||||
tools_map = {} # This will map each tool with it's tool function.
|
||||
|
||||
|
||||
# A tool call function.
|
||||
def handle_tool_call(message: ChatCompletionMessage):
|
||||
"""Tools call handler."""
|
||||
tool_call = message.tool_calls[0]
|
||||
arguments = loads(tool_call.function.arguments)
|
||||
print(f'\nFUNC CALL: {tool_call.function.name}({arguments})\n')
|
||||
# Get tool function and call with arguments.
|
||||
tool_func = tools_map.get(tool_call.function.name)
|
||||
tool_response = tool_func(**arguments)
|
||||
response = {"role": "tool", "content": tool_response, "tool_call_id": tool_call.id}
|
||||
return response
|
||||
|
||||
|
||||
draw_signature = {
|
||||
"name": "draw_scene",
|
||||
"description": "Generate an image of the scene based on the description",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"scene_description": {
|
||||
"type": "string",
|
||||
"description": "A detailed description of the scene to be drawn",
|
||||
},
|
||||
"scene_style": {
|
||||
"type": "string",
|
||||
"description": "The art style for the image",
|
||||
},
|
||||
},
|
||||
"required": ["scene_description"],
|
||||
"additionalProperties": False,
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
# Tool call response example.
|
||||
ChatCompletionMessage(
|
||||
content="""To begin, first I need to set a scene.
|
||||
Imagine you are in a dark room of an old castle.
|
||||
The walls are covered in cobwebs and there is a smell of mold in the air.
|
||||
As you look around, you notice a slightly ajar door to the north
|
||||
and a dark figure lurking in the corner.
|
||||
|
||||
I am going to generate an image of this scene. One moment, please.""",
|
||||
refusal=None,
|
||||
role="assistant",
|
||||
annotations=[],
|
||||
audio=None,
|
||||
function_call=None,
|
||||
tool_calls=[
|
||||
ChatCompletionMessageFunctionToolCall(
|
||||
id="call_oJqJeXMUPZUaC0GPfMeSd16E",
|
||||
function=Function(
|
||||
arguments='''{
|
||||
"scene_description":"A dark room in an ancient castle.
|
||||
The walls are covered with cobwebs, and there\'s a musty smell in
|
||||
the air.
|
||||
A slightly ajar door to the north and a shadowy figure lurking in
|
||||
the corner.
|
||||
Dim lighting adds to the eerie atmosphere, with flickering shadows.",
|
||||
"style":"fantasy"
|
||||
}''',
|
||||
name="draw_scene"),
|
||||
type="function",
|
||||
)
|
||||
],
|
||||
)
|
||||
Reference in New Issue
Block a user