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from difflib import SequenceMatcher def similarity(a, b): return SequenceMatcher(None, a, b).ratio()

import time class DiStopWatch(Skill): """ Stopwatch skill for voice-controlled timing. Auto-announces at each minute milestone while running. """ def __init__(self): super().__init__() self.timer = Timer() self.last_announced_minute = 0...

import time class Timer: """ A simple timer class for tracking elapsed time. Supports start, pause, resume, reset, and formatted output. """ def __init__(self): """Initialize a new timer. Not started by default.""" self._start_time = None...

unitree also seems good for kit bashing. too bad its so pricy. maybe in a year or too they will be affordable with doll features

would love to hear about the technical aspects of how you built her. a devlog thread even.

class Pipe: def __init__(self): self.nulify: bool = False def nulify(self): self.nulify = True def is_nullified(self): temp = self.nulify self.nulify = False return temp def input(self, ear: str, skin: str, eye: str, stave:str)->str...

import threading import time from typing import List, Tuple, Optional import cv2 import numpy as np # ----------------------------------------- # RECTANGLE CLASS (IMAGE COORDS) # ----------------------------------------- class Rectangle: def __init__(self, x1: int, y1: int, x2: int, y2...

updated Python version with per‑object movement (each bounding box tracked individually), using parallel lists import threading import time import torch from torchvision import models, transforms from PIL import Image import cv2 from ultralytics import YOLO from typing import List #...

with directions import threading import time import torch from torchvision import models, transforms from PIL import Image import cv2 from ultralytics import YOLO from typing import List, Optional # ----------------------------------------- # RECTANGLE CLASS #...

full shit in the ass mode with list of recognitions, detections: import threading import time import torch from torchvision import models, transforms from PIL import Image import cv2 from ultralytics import YOLO from typing import List # ----------------------------------------- # RECTANGLE...

with image detection: import threading import time import torch from torchvision import models, transforms from PIL import Image import cv2 from ultralytics import YOLO from typing import List # ----------------------------------------- # RECTANGLE CLASS #...

pip install torch torchvision pillow Create a new file Create image_recog_threaded.py. import threading import time import torch from torchvision import models, transforms from PIL import Image import cv2 # ----------------------------- # GLOBAL STATIC RESULT VARIABLE #...

class DiRailPunk(Skill): # this skill swaps between two sets of contradicting skills def __init__(self): super().__init__() self.set_skill_type(3) # backgroundable skill self.trg: TrgHP = TrgHP() self.lim = 2 self.r1: Responder = Responder("wink"...

class Sarcophagus: def __init__(self): """Initialize the sarcophagus with an empty set of shielded items.""" self._shielded_items: set[str] = set() @property def shielded_items(self) -> set[str]: """Return a copy of the shielded items set (read-only access).""" return...

class TrgHP: def __init__(self, low: int=-10, high: int=10, regen: int=5): self.hp = 4 self.low = low self.high = high self.regen: TrgEveryNMinutes = TrgEveryNMinutes(regen) self.hit: set[str] = {"shut up", "quiet", "be quiet", "silence"} def...

fruits = ["apple", "banana", "orange", "grape", "mango"] vegetables = ["carrot", "tomato", "cucumber", "onion", "pepper"] def contains_fruit_and_vegi(text: str) -> bool: text = text.lower() has_fruit = any(f in text for f in fruits) has_vegi = any(v in text for v in vegetables)...