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import base64 import contextlib import io import json import os.path as osp import PIL.Image from labelme import PY2 from labelme import QT4 from labelme import __version__ from labelme import utils from labelme.logger import logger PIL.Image.MAX_IMAGE_PIXELS = None @contextlib.contextmanager def open(name, mode): assert mode in ["r", "w"] if PY2: mode += "b" encoding = None else: encoding = "utf-8" yield io.open(name, mode, encoding=encoding) return class LabelFileError(Exception): pass class LabelFile(object): suffix = ".json" def __init__(self, filename=None): self.shapes = [] self.imagePath = None self.imageData = None
long_description=LONG_DESCRIPTION, url='https://youtube-wrapper.readthedocs.io/en/latest/index.html', author='Lyle Okoth', author_email='[email protected]', license='MIT', install_requires=install_requires, keywords=key_words, classifiers=[ 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.10', 'Programming Language :: Python :: 3.11', 'Operating System :: OS Independent' ], )
self.prevhVertex = self.hVertex self.hVertex = None self.prevhShape = self.hShape = shape self.prevhEdge = self.hEdge self.hEdge = None self.setToolTip( self.tr("Click & drag to move shape '%s'") % shape.label ) self.setStatusTip(self.toolTip()) self.overrideCursor(CURSOR_GRAB) self.update() break else: # Nothing found, clear highlights, reset state. self.unHighlight() self.vertexSelected.emit(self.hVertex is not None) def addPointToEdge(self): shape = self.prevhShape index = self.prevhEdge point = self.prevMovePoint if shape is None or index is None or point is None: return shape.insertPoint(index, point) shape.highlightVertex(index, shape.MOVE_VERTEX) self.hShape = shape self.hVertex = index self.hEdge = None self.movingShape = True def removeSelectedPoint(self): shape = self.prevhShape index = self.prevhVertex if shape is None or index is None: return shape.removePoint(index) shape.highlightClear() self.hShape = shape self.prevhVertex = None self.movingShape = True # Save changes
if out_file: out_file = osp.abspath(out_file) if osp.exists(out_file): raise RuntimeError("File exists: %s" % out_file) else: open(osp.abspath(out_file), "w") cmd = ( "docker run -it --rm" " -e DISPLAY={0}:0" " -e QT_X11_NO_MITSHM=1" " -v /tmp/.X11-unix:/tmp/.X11-unix" " -v {1}:{2}" " -w /home/developer" ) in_file_a = osp.abspath(in_file) in_file_b = osp.join("/home/developer", osp.basename(in_file)) cmd = cmd.format( ip, in_file_a, in_file_b, ) if out_file: out_file_a = osp.abspath(out_file) out_file_b = osp.join("/home/developer", osp.basename(out_file)) cmd += " -v {0}:{1}".format(out_file_a, out_file_b) cmd += " wkentaro/labelme labelme {0}".format(in_file_b) if out_file: cmd += " -O {0}".format(out_file_b) subprocess.call(shlex.split(cmd)) if out_file: try: json.load(open(out_file)) return out_file except Exception: if open(out_file).read() == "": os.remove(out_file) raise RuntimeError("Annotation is cancelled.")
import torch from tqdm import tqdm from torch.nn import CrossEntropyLoss from torch import optim from torchvision.transforms import Compose, Resize, RandomCrop, ToTensor, Normalize from torch.utils.tensorboard import SummaryWriter from utils import save_checkpoint, load_checkpoint, print_examples from create_dataset import get_loader from model import CNNToRNN def train(): transforms = Compose( [ Resize((356, 356)), RandomCrop((299, 299)), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ] ) train_loader, dataset = get_loader( images_dir="raw-data/Images", captions_file="raw-data/captions.txt", transforms=transforms, num_workers=2, ) torch.backends.cudnn.benchmark = True device = torch.device("cuda" if torch.cuda.is_available() else "cpu") load_model = False save_model = False train_CNN = False # Hyperparameters embed_size = 256 hidden_size = 256 vocab_size = len(dataset.vocabulary) num_layers = 1 learning_rate = 3e-4
createLineMode=createLineMode, createPointMode=createPointMode, createLineStripMode=createLineStripMode, createAiPolygonMode=createAiPolygonMode, createAiMaskMode=createAiMaskMode, zoom=zoom, zoomIn=zoomIn, zoomOut=zoomOut, zoomOrg=zoomOrg, keepPrevScale=keepPrevScale, fitWindow=fitWindow, fitWidth=fitWidth, brightnessContrast=brightnessContrast, zoomActions=zoomActions, openNextImg=openNextImg, openPrevImg=openPrevImg, fileMenuActions=(open_, opendir, save, saveAs, close, quit), tool=(), # XXX: need to add some actions here to activate the shortcut editMenu=( edit, duplicate, copy, paste, delete, None, undo, undoLastPoint, None, removePoint, None, toggle_keep_prev_mode, ), # menu shown at right click menu=( createMode, createRectangleMode, createCircleMode, createLineMode, createPointMode,
llm=self.chat_model, prompt=self.mapping_prompt.chat_prompt, output_parser=self.mapping_prompt.parser, verbose=debug, output_key="mapping_list", ) overall_chain = SequentialChain( chains=[travel_agent, parser], input_variables=["query", "format_instructions"], output_variables=["agent_suggestion", "mapping_list"], verbose=debug, ) return overall_chain def suggest_travel(self, query): """ Parameters ---------- query Returns ------- """ self.logger.info("Validating query") t1 = time.time() self.logger.info( "Calling validation (model is {}) on user input".format( self.chat_model.model_name ) ) validation_result = self.validation_chain( { "query": query, "format_instructions": self.validation_prompt.parser.get_format_instructions(), } )
# Scrapy settings for slidesmodel project # # For simplicity, this file contains only settings considered important or # commonly used. You can find more settings consulting the documentation: # # https://docs.scrapy.org/en/latest/topics/settings.html # https://docs.scrapy.org/en/latest/topics/downloader-middleware.html # https://docs.scrapy.org/en/latest/topics/spider-middleware.html BOT_NAME = "slidesmodel" SPIDER_MODULES = ["slidesmodel.spiders"] NEWSPIDER_MODULE = "slidesmodel.spiders" # Crawl responsibly by identifying yourself (and your website) on the user-agent #USER_AGENT = "slidesmodel (+http://www.yourdomain.com)" # Obey robots.txt rules ROBOTSTXT_OBEY = False # Configure maximum concurrent requests performed by Scrapy (default: 16) #CONCURRENT_REQUESTS = 32 # Configure a delay for requests for the same website (default: 0) # See https://docs.scrapy.org/en/latest/topics/settings.html#download-delay # See also autothrottle settings and docs #DOWNLOAD_DELAY = 3 # The download delay setting will honor only one of: #CONCURRENT_REQUESTS_PER_DOMAIN = 16 #CONCURRENT_REQUESTS_PER_IP = 16 # Disable cookies (enabled by default) #COOKIES_ENABLED = False # Disable Telnet Console (enabled by default) #TELNETCONSOLE_ENABLED = False # Override the default request headers: #DEFAULT_REQUEST_HEADERS = {
from langchain.tools import tool from .helpers import advanced_video_search from youtube.models import Search class FindProductVideoTools(): @tool def find_product_video_id(product: str) -> str: """Useful when you need to find a product review video from youtube.""" query: str = f'reviews of the latest {product}' search_results: list[Search] = advanced_video_search(query) return search_results[0].resource_id
# flake8: noqa from . import draw_json from . import draw_label_png from . import export_json from . import on_docker
ef generate_bookmarks(users: list[User], posts: list[Post], bookmarks_count: int = 100) -> list[Bookmark]: """Generate bookmarks.""" bookmarks: list[Bookmark] = [] ids = set() for _ in range(bookmarks_count): author_id: str = random.choice(users).id post_id: str = random.choice(posts).id bookmark: bookmark = Bookmark(author_id=author_id, post_id=post_id) if (author_id, post_id) not in ids: bookmarks.append(bookmark) ids.add((author_id, post_id)) return bookmarks def generate_comments(users: list[User], posts: list[Post], comments_count: int = 500) -> list[Like]: """Generate likes.""" comments: list[Comment] = [] ids = set() for _ in range(comments_count): author_id: str = random.choice(users).id post_id: str = random.choice(posts).id comment: comment = Comment( id='Comment_' + str(uuid4()), author_id=author_id, post_id=post_id, comment_text=fake.text() ) if (author_id, post_id) not in ids: comments.append(comment) ids.add((author_id, post_id)) return comments
from crewai import Agent from tools import FindProductVideoTools, FindProductReviewTools from langchain.llms.openai import OpenAI from langchain.chat_models import ChatOpenAI class ProductReviewAgents(): def research_analyst(self): return Agent( role='Product Video Researcher', goal="""Find the best product review videos from youtube""", backstory="""Known for your indepth knowledge of various videos that analyze different products on youtube. Now you have to find the best video that reviews the given product.""", llm=OpenAI(temperature=0.7), verbose=True, tools=[ FindProductVideoTools.find_product_video_id, FindProductReviewTools.find_product_reviews ] )
return None def addRecentFile(self, filename): if filename in self.recentFiles: self.recentFiles.remove(filename) elif len(self.recentFiles) >= self.maxRecent: self.recentFiles.pop() self.recentFiles.insert(0, filename) # Callbacks def undoShapeEdit(self): self.canvas.restoreShape() self.labelList.clear() self.loadShapes(self.canvas.shapes) self.actions.undo.setEnabled(self.canvas.isShapeRestorable) def tutorial(self): url = "https://github.com/wkentaro/labelme/tree/main/examples/tutorial" # NOQA webbrowser.open(url) def toggleDrawingSensitive(self, drawing=True): """Toggle drawing sensitive. In the middle of drawing, toggling between modes should be disabled. """ self.actions.editMode.setEnabled(not drawing) self.actions.undoLastPoint.setEnabled(drawing) self.actions.undo.setEnabled(not drawing) self.actions.delete.setEnabled(not drawing) def toggleDrawMode(self, edit=True, createMode="polygon"): draw_actions = { "polygon": self.actions.createMode, "rectangle": self.actions.createRectangleMode, "circle": self.actions.createCircleMode, "point": self.actions.createPointMode, "line": self.actions.createLineMode, "linestrip": self.actions.createLineStripMode, "ai_polygon": self.actions.createAiPolygonMode,
def __init__(self): super().__init__( encoder_path=gdown.cached_download( url="https://github.com/wkentaro/labelme/releases/download/sam-20230416/sam_vit_h_4b8939.quantized.encoder.onnx", # NOQA md5="958b5710d25b198d765fb6b94798f49e", ), decoder_path=gdown.cached_download( url="https://github.com/wkentaro/labelme/releases/download/sam-20230416/sam_vit_h_4b8939.quantized.decoder.onnx", # NOQA md5="a997a408347aa081b17a3ffff9f42a80", ), ) class EfficientSamVitT(EfficientSam): name = "EfficientSam (speed)" def __init__(self): super().__init__( encoder_path=gdown.cached_download( url="https://github.com/labelmeai/efficient-sam/releases/download/onnx-models-20231225/efficient_sam_vitt_encoder.onnx", # NOQA md5="2d4a1303ff0e19fe4a8b8ede69c2f5c7", ), decoder_path=gdown.cached_download( url="https://github.com/labelmeai/efficient-sam/releases/download/onnx-models-20231225/efficient_sam_vitt_decoder.onnx", # NOQA md5="be3575ca4ed9b35821ac30991ab01843", ), ) class EfficientSamVitS(EfficientSam): name = "EfficientSam (accuracy)" def __init__(self): super().__init__( encoder_path=gdown.cached_download( url="https://github.com/labelmeai/efficient-sam/releases/download/onnx-models-20231225/efficient_sam_vits_encoder.onnx", # NOQA md5="7d97d23e8e0847d4475ca7c9f80da96d", ), decoder_path=gdown.cached_download(
from sqlalchemy import create_engine from sqlalchemy.orm import DeclarativeBase, MappedAsDataclass from sqlalchemy.orm import sessionmaker from ...config.config import BaseConfig from contextlib import contextmanager from flask import current_app class Base(MappedAsDataclass, DeclarativeBase): pass SQLALCHEMY_DATABASE_URI = BaseConfig().db_conn_string engine = create_engine(SQLALCHEMY_DATABASE_URI) Session = sessionmaker(bind=engine, autocommit=False, autoflush=False) def create_all(): Base.metadata.create_all(bind=engine) def drop_all(): Base.metadata.drop_all(bind=engine) @contextmanager def get_db(): try: db = Session() yield db finally: db.close()
with open(file_path, "r", encoding="utf-8") as f: all_comments: list[str] = json.load(fp=f) cleaned_comments: list[str] = list(map(clean_text, all_comments)) comments: list[str] = choices(population=cleaned_comments, k=10) docs: list[Document] = [ Document(page_content=comment) for comment in comments if is_acceptable_len(comment) ] comments: list[dict[str, str | int]] = [ {"doc_id": i + 1, "comment": docs[i].page_content} for i in range(len(docs)) ] data_dir = "./agent_nelly/data_analysis/data" features_dir = "features" save_features_dir = path.join(data_dir, features_dir, "features.json") with open(save_features_dir, 'r') as f: topics: list[str] = json.load(f) class CustomerCommentData(BaseModel): doc_id: int = Field(description="The doc_id from the input") topics: list[str] = Field( description="List of the relevant topics for the customer review. Include only topics from the list provided.", default_factory=list, ) sentiment: str = Field( description="Sentiment of the topic", enum=["positive", "neutral", "negative"] ) class CommentsParser(BaseModel): comment: list[CustomerCommentData] = Field(description="A list of the customer comment data", default_factory=list) output_parser = PydanticOutputParser(pydantic_object=CommentsParser) format_instructions = output_parser.get_format_instructions()
from .helpers import create_gslide_client, create_drive_client from typing import Any from .helpers import get_youtube_client from ..libraries.youtube import YouTube gslide_client: Any = create_gslide_client() drive_client: Any = create_drive_client() youtube_client: YouTube = get_youtube_client()
import imgviz import numpy as np import skimage from labelme.logger import logger def _get_contour_length(contour): contour_start = contour contour_end = np.r_[contour[1:], contour[0:1]] return np.linalg.norm(contour_end - contour_start, axis=1).sum() def compute_polygon_from_mask(mask): contours = skimage.measure.find_contours(np.pad(mask, pad_width=1)) if len(contours) == 0: logger.warning("No contour found, so returning empty polygon.") return np.empty((0, 2), dtype=np.float32) contour = max(contours, key=_get_contour_length) POLYGON_APPROX_TOLERANCE = 0.004 polygon = skimage.measure.approximate_polygon( coords=contour, tolerance=np.ptp(contour, axis=0).max() * POLYGON_APPROX_TOLERANCE, ) polygon = np.clip(polygon, (0, 0), (mask.shape[0] - 1, mask.shape[1] - 1)) polygon = polygon[:-1] # drop last point that is duplicate of first point if 0: import PIL.Image image_pil = PIL.Image.fromarray(imgviz.gray2rgb(imgviz.bool2ubyte(mask))) imgviz.draw.line_(image_pil, yx=polygon, fill=(0, 255, 0)) for point in polygon: imgviz.draw.circle_(image_pil, center=point, diameter=10, fill=(0, 255, 0)) imgviz.io.imsave("contour.jpg", np.asarray(image_pil)) return polygon[:, ::-1] # yx -> xy
""")) product_crew = ProductReviewCrew(company) result = product_crew.run() print("\n\n########################") print("## Here is the Report") print("########################\n") print(result)
yield Request(link, callback=self.parse_slide, meta={"slide_item": slide_item}) # next_page = response.css('a.next.page-numbers::attr(href)').get() # if next_page and int(next_page.split('/')[-2]) < 2: # self.logger.warning(f"Crawling page number %d", int(next_page.split('/')[-2])) # yield Request(next_page, callback=self.parse) next_page = response.css('a.next.page-numbers::attr(href)').get() if next_page: self.logger.warning(f"Crawling page number %d", int(next_page.split('/')[-2])) yield Request(next_page, callback=self.parse) def parse_slide(self, response: Response, **kwargs: Any) -> Any: slide_item = response.meta["slide_item"] loader = ItemLoader(item=slide_item, response=response) loader.add_css(field_name="tags", css=".Sm-tags a.mr-2::text") loader.add_css(field_name="description", css=".product-text p") loader.add_css(field_name="slides_count", css='h4 small::text') loader.add_css(field_name="colors", css='li.color a::text') loader.add_css(field_name="image_urls", css='a.preview-link img::attr(src)') # add slide link yield loader.load_item()
from collections import deque from collections.abc import Iterator from os import listdir, path from queue import Queue from .config import Config from .helpers import read_src class DirectoryIterator(Iterator): def __init__(self, config: Config) -> None: super().__init__() self._folders_ignore = set(config.directories_ignore) self._files_ignore = set(config.files_ignore) self._queue = deque(config.root_directory) # adding the individual chars def __iter__(self) -> Iterator: return super().__iter__() def __next__(self) -> list[str]: if self._queue: files: list[str] = list() for _ in range(len(self._queue)): directory: str = self._queue.popleft() for entry in listdir(directory): entry_path: str = path.join(directory, entry) if ( path.isfile(entry_path) and self._is_python_file(entry_path) and entry not in self._files_ignore ): files.append(entry_path) elif path.isdir(entry_path) and entry not in self._folders_ignore: self._queue.append(entry_path) return files else: raise StopIteration() def _is_python_file(self, file_path: str) -> bool: return file_path.split(".")[-1] == "py"
return items def get_channel_details(channel: Search) -> Channel: """Get channel details""" response: YouTubeListResponse = youtube_client.find_channel_by_id( channel_id=channel.resource_id ) channel: Channel = response.items[0] return channel def parse_channel_details(channel: Channel) -> dict: return { "title": channel.snippet.title, "description": channel.snippet.description, "date": str(channel.snippet.published_at.date()), "subscribers": channel.statistics.subscribers_count, "videos": channel.statistics.videos_count, } def get_channels(product: str, max_results: int = 10) -> list[dict]: channels: list[Search] = search_youtube_channels(product=product, max_results=max_results) channels: list[Channel] = map(get_channel_details, channels) channels: list[dict] = map(parse_channel_details, channels) return channels def save_data(file_path: str, data: list) -> None: with open(file_path, 'w') as f: json.dump(data, f, indent=4) def load_data(file_path: str) -> dict: with open(file_path, 'r') as f: data: list[dict] = json.load(f) return data def create_channels_table(table_data: list[dict]) -> Table: table: Table = Table(row_styles=["dim", ""],leading=1, box=box.MINIMAL_DOUBLE_HEAD,
from langchain.tools import tool from .helpers import list_video_comments from youtube.models import Comment class FindProductReviewTools(): @tool def find_product_reviews(video_id: str) -> str: """Useful when you need to find a product reviews from youtube video comments.""" comments: list[Comment] = list_video_comments(video_id) comments: list[str] = [comment.snippet.text_display for comment in comments] return ' '.join(comments)
def popUp(self, text=None, move=True, flags=None, group_id=None, description=None): if self._fit_to_content["row"]: self.labelList.setMinimumHeight( self.labelList.sizeHintForRow(0) * self.labelList.count() + 2 ) if self._fit_to_content["column"]: self.labelList.setMinimumWidth(self.labelList.sizeHintForColumn(0) + 2) # if text is None, the previous label in self.edit is kept if text is None: text = self.edit.text() # description is always initialized by empty text c.f., self.edit.text if description is None: description = "" self.editDescription.setPlainText(description) if flags: self.setFlags(flags) else: self.resetFlags(text) self.edit.setText(text) self.edit.setSelection(0, len(text)) if group_id is None: self.edit_group_id.clear() else: self.edit_group_id.setText(str(group_id)) items = self.labelList.findItems(text, QtCore.Qt.MatchFixedString) if items: if len(items) != 1: logger.warning("Label list has duplicate '{}'".format(text)) self.labelList.setCurrentItem(items[0]) row = self.labelList.row(items[0]) self.edit.completer().setCurrentRow(row) self.edit.setFocus(QtCore.Qt.PopupFocusReason) if move: self.move(QtGui.QCursor.pos()) if self.exec_(): return ( self.edit.text(), self.getFlags(), self.getGroupId(),
def is_acceptable_len(text: str, l: int = 20) -> bool: return len(text.split()) >= l with open(file_path, "r", encoding="utf-8") as f: all_comments: list[str] = json.load(fp=f) cleaned_comments: list[str] = list(map(clean_text, all_comments)) comments: list[str] = choices(population=cleaned_comments, k=10) docs: list[Document] = [ Document(page_content=comment) for comment in comments if is_acceptable_len(comment) ] comments: list[dict[str, str | int]] = [ {"doc_id": i + 1, "comment": docs[i].page_content} for i in range(len(docs)) ] data_dir = "./agent_nelly/data_analysis/data" features_dir = "features" save_features_dir = path.join(data_dir, features_dir, "features.json") with open(save_features_dir, 'r') as f: topics: list[str] = json.load(f) comment: dict = choice(comments) sentiment_msg: str = """ Below is a customer comment in JSON format with the following keys: 1. doc_id - identifier of the comment 2. comment - the user comment Please analyze the comment and identify the sentiment. The sentiment can be negative, neutral or positive. Only return a single string, the sentiment. Comment: ``` {comment} ```
# This package will contain the spiders of your Scrapy project # # Please refer to the documentation for information on how to create and manage # your spiders.
shape.description = description self._update_shape_color(shape) if shape.group_id is None: item.setText( '{} <font color="#{:02x}{:02x}{:02x}">●</font>'.format( html.escape(shape.label), *shape.fill_color.getRgb()[:3] ) ) else: item.setText("{} ({})".format(shape.label, shape.group_id)) self.setDirty() if self.uniqLabelList.findItemByLabel(shape.label) is None: item = self.uniqLabelList.createItemFromLabel(shape.label) self.uniqLabelList.addItem(item) rgb = self._get_rgb_by_label(shape.label) self.uniqLabelList.setItemLabel(item, shape.label, rgb) def fileSearchChanged(self): self.importDirImages( self.lastOpenDir, pattern=self.fileSearch.text(), load=False, ) def fileSelectionChanged(self): items = self.fileListWidget.selectedItems() if not items: return item = items[0] if not self.mayContinue(): return currIndex = self.imageList.index(str(item.text())) if currIndex < len(self.imageList): filename = self.imageList[currIndex] if filename: self.loadFile(filename)
from selenium import webdriver from selenium.webdriver.common.keys import Keys from selenium.webdriver.common.by import By from selenium.webdriver.chrome.service import Service as ChromeService from webdriver_manager.chrome import ChromeDriverManager from selenium.webdriver.chrome.options import Options i options = Options() options.add_argument("--headless=new") driver = webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()), options=options) driver.get("https://leetcode.com/problems/remove-linked-list-elements") paragraphs = driver.find_elements(By.TAG_NAME, "p") print(paragraphs) driver.quit()
features, labels, test_size=0.2, random_state=42, stratify=labels ) return (train_features, train_labels), (test_features, test_labels) def save_features(self) -> DataFrame: pass def save_labels(self) -> DataFrame: pass def train_model(self, model: Model) -> float: (train_features, train_labels), (test_features, test_labels) = self.get_train_test_data() pipeline: Pipeline = Pipeline(steps=[ ('preprocessor', self.preprocessor), ('classifier', model.model) ]) logging.info('Queing the model "%s" for training.', model.name) res: AsyncResult = train_model_task.delay(pipeline, train_features, train_labels, test_features, test_labels, model.name, model.save_path) self.train_task_ids.append(res.id) return res.id def run(self) -> None: self._train_results = chord((train_model_task.s( self.create_train_config(model=model.model, name=model.classifier_name, save_path=model.save_path) ) for model in self.models), send_training_report_task.s())() def get_results(self) -> list[Model]: """Get the training result.""" logging.info('Getting the training results') print(self._train_results.get()) def get_best_models(self, start: int = 0, end: int = -1) -> Model: best_models = redis.zrange(name=app_config.accuracy_channel, start=start, end=end, withscores=True) return best_models def tune_best_models(self) -> None: logging.info('Tuning the best models.') best_models = self.get_best_models(start=-3, end=-1) logging.info(best_models)
from queue import Queue from threading import Thread from .config import Config from .file_processor import ( generate_function_docstrings, queue_unprocessed_functions_methods, generate_class_docstrings, ) from .helpers import get_all_modules def generate_docstrings( config: Config, module_path_queue: Queue, functions_source_queue: Queue, class_source_queue: Queue, failed_modules_queue: Queue, ) -> None: """Generate docstrings for classes and methods.""" queue_modules: Thread = Thread( target=get_all_modules, name='get_all_modules', args=(config, module_path_queue), ) queue_modules.start() for _ in range(1): get_functions_source_thread: Thread = Thread( target=queue_unprocessed_functions_methods, args=(functions_source_queue, class_source_queue, module_path_queue), daemon=True, ) get_functions_source_thread.start() for _ in range(1): generate_functions_docstring_thread: Thread = Thread( target=generate_function_docstrings, args=(functions_source_queue, config), daemon=True,
from youtube import YouTube client_secrets_file = "/home/lyle/Downloads/search.json" youtube_client = YouTube(client_secret_file=client_secrets_file) youtube_client_object = youtube_client.authenticate() youtube_client.youtube_client = youtube_client_object
from setuptools import find_packages, setup from pip._vendor import tomli # For consistent encoding from codecs import open from os import path # The directory containing this file HERE = path.abspath(path.dirname(__file__)) # Get the long description from the README file with open(path.join(HERE, 'README.md'), encoding='utf-8') as f: LONG_DESCRIPTION = f.read() with open('pyproject.toml', 'r') as f: VERSION = tomli.load(f)['tool']['commitizen']['version'] DESCRIPTION = 'A python library that wraps around the Google calendar API. You can use it to schedule events using google calendar.' key_words = [ 'calendar', 'google-calendar', 'schedule events' ] install_requires = [ 'oryks-google-oauth', 'pydantic', 'pydantic-settings', 'pytz' ] setup( name='oryks-google-calendar', packages=find_packages( include=[ 'google_calendar', 'google_calendar.models', 'google_calendar.schemas', 'google_calendar.resources', ] ),
from datetime import datetime from sqlalchemy.orm import Mapped, mapped_column, relationship from ..database import Base from sqlalchemy import ForeignKey class View(Base): __tablename__ = 'views' id: Mapped[str] = mapped_column(primary_key=True) author_id: Mapped[str] = mapped_column(ForeignKey('users.id')) post_id: Mapped[str] = mapped_column(ForeignKey('posts.id')) view_date: Mapped[datetime] = mapped_column(default_factory=datetime.utcnow) author = relationship('User', back_populates='views') post = relationship('Post', back_populates='views')
src_tree: AST = parse_src(docstring) func_node: FunctionDef = src_tree.body[0] doc_str: str = ast.get_docstring(func_node) except Exception: return super().parse(docstring) else: return doc_str model_parser: Parser = DefaultParser() def parse_function_docstr(func_dcstr: str) -> str: return model_parser.parse(docstring=func_dcstr)
from typing import Any from scrapy import Spider from scrapy.http import Response from scrapy import Request # from slidesmodel.items import SlidesModelItem from scrapy.loader import ItemLoader from scrapy.utils.project import get_project_settings import json class SlidesModelspider(Spider): name: str = "problems" def __init__(self, name: str | None = None, **kwargs: Any): super().__init__(name, **kwargs) # self.start_urls: list[str] = self.load_start_urls() self.start_urls: list[str] = [ "https://www.techiedelight.com/data-structures-and-algorithms-problems/" ] def parse(self, response: Response, **kwargs: Any) -> Any: self.logger.info("This is my first spider.") problem_links = response.css('.post-problems li') # from random import choices # problem_links = choices(population=problem_links, k=100) # for problem_link in problem_links: # # title = problem_link.css('a::text')[0].get() # link = problem_link.css('a::attr(href)')[0].get() # # yield{ # # "link": link, # # "problem": problem # # } # yield Request(link, callback=self.parse_problem) link = "https://www.techiedelight.com/single-source-shortest-paths-bellman-ford-algorithm/" yield Request(link, callback=self.parse_problem) # for slide in slides: # loader: ItemLoader = ItemLoader(item=SlidesModelItem(), selector=slide) # loader.add_css("title", ".item a::text") # loader.add_css("category", ".category::text")
"ai_mask": self.actions.createAiMaskMode, } self.canvas.setEditing(edit) self.canvas.createMode = createMode if edit: for draw_action in draw_actions.values(): draw_action.setEnabled(True) else: for draw_mode, draw_action in draw_actions.items(): draw_action.setEnabled(createMode != draw_mode) self.actions.editMode.setEnabled(not edit) def setEditMode(self): self.toggleDrawMode(True) def updateFileMenu(self): current = self.filename def exists(filename): return osp.exists(str(filename)) menu = self.menus.recentFiles menu.clear() files = [f for f in self.recentFiles if f != current and exists(f)] for i, f in enumerate(files): icon = utils.newIcon("labels") action = QtWidgets.QAction( icon, "&%d %s" % (i + 1, QtCore.QFileInfo(f).fileName()), self ) action.triggered.connect(functools.partial(self.loadRecent, f)) menu.addAction(action) def popLabelListMenu(self, point): self.menus.labelList.exec_(self.labelList.mapToGlobal(point)) def validateLabel(self, label): # no validation if self._config["validate_label"] is None: return True
from setuptools import find_packages, setup from pip._vendor import tomli # For consistent encoding from codecs import open from os import path # The directory containing this file HERE = path.abspath(path.dirname(__file__)) # Get the long description from the README file with open(path.join(HERE, 'README.md'), encoding='utf-8') as f: LONG_DESCRIPTION = f.read() with open('pyproject.toml', 'r') as f: VERSION = tomli.load(f)['tool']['commitizen']['version'] DESCRIPTION = 'A python library for working with Google Drive.' key_words = [ 'drive', 'google-drive', 'google-drive-api', 'upload files to Google Drive', ] install_requires = [ 'oryks-google-oauth', 'pydantic', 'pydantic-settings' ] setup( name='oryks-google-drive', packages=find_packages( include=[ 'google_drive', 'google_drive.exceptions', 'google_drive.models', 'google_drive.schemas', 'google_drive.resources' ] ),
import whisper model = whisper.load_model("medium.en") result = model.transcribe("code.wav") print(result["text"])
from scrapy import Item, Field from itemloaders.processors import TakeFirst, MapCompose, Join import re def remove_html_tags(description: str) -> str: html_pattern = "<(?:\"[^\"]*\"['\"]*|'[^']*'['\"]*|[^'\">])+>" return re.sub(html_pattern, '', description) def remove_unicode_chars(text: str) -> str: return text.replace(u"\xa0", "") def num_of_slides(text: str) -> int: vals = [val for val in list(text) if val.isdigit()] return "".join(vals) class SlidesModelItem(Item): title = Field(output_processor=TakeFirst()) category = Field(output_processor=TakeFirst()) description = Field( input_processor=MapCompose(remove_html_tags, remove_unicode_chars), output_processor=Join() ) tags = Field() slides_count = Field( input_processor=MapCompose(num_of_slides), output_processor=TakeFirst() ) colors = Field() image_urls = Field() images = Field()
topic_assign_msg: str = """ Below is a list of customer comments in JSON format with the following keys: 1. doc_id - identifier of the comment 2. comment - the user comment Please analyze the provided comments and identify the main topics and sentiment. Include only the topics mentioned in the following text: Text: {topics} {format_instructions} user comments: ```{comments}``` """ topic_assign_tmpl = PromptTemplate( template=topic_assign_msg, input_variables=["topics", "comments", "format_instructions"], ) with open('analysis.json', 'r') as f: data = json.load(f) i = data[-1]["comment_id"] + 1 from time import sleep import json for _ in range(10): d = comments[i: i+3] x = {} for s in d: x[s['doc_id']] = s['comment'] i += 3 inputs = { "topics": topics, "format_instructions": format_instructions, "comments": json.dumps(d), } # print(d) # print(c)
"""This module declares the app configuration. The classes include: BaseConfig: Has all the configurations shared by all the environments. """ import os from dotenv import load_dotenv load_dotenv() class BaseConfig: """Base configuration.""" DEBUG = True TESTING = False SECRET_KEY = os.environ.get( "SECRET_KEY", "df0331cefc6c2b9a5d0208a726a5d1c0fd37324feba25506" ) class DevelopmentConfig(BaseConfig): """Development confuguration.""" DEBUG = True TESTING = False SECRET_KEY = os.environ.get( "SECRET_KEY", "df0331cefc6c2b9a5d0208a726a5d1c0fd37324feba25506" ) class TestingConfig(BaseConfig): """Testing configuration.""" TESTING = True SECRET_KEY = os.environ.get("SECRET_KEY", "secret-key")
class DirectoryIterator: def __init__(self, config: Config): self.config: Config = config self.queue: deque[str] = deque(self.config.path) def __iter__(self) -> Iterator: return self def __next__(self) -> list[str]: files: list[str] = list() if self.queue: for _ in range(len(self.queue)): root_dir: str = self.queue.popleft() if root_dir.split('/')[-1] in self.config.directories_ignore: continue entries: list[str] = listdir(root_dir) for entry in entries: entry_path: str = path.join(root_dir, entry) if path.isfile(entry_path): if ( entry_path not in self.config.files_ignore and entry.split('.')[-1] == 'py' ): files.append(entry_path) elif entry not in self.config.directories_ignore: self.queue.append(entry_path) return files else: raise StopIteration()
class GetPosts(BaseModel): offset: Optional[int] = 0 limit: Optional[int] = 10 class PostAuthor(BaseModel): id: str profile_picture: str name: str class PostLike(BaseModel): liked: bool liked_by: Optional[list[PostAuthor]] = Field(default_factory=list) key_like: Optional[PostAuthor] = None likes_count: Optional[int] = Field(default=0) class KeyComment(BaseModel): author: PostAuthor text: str comments_count: int class PostSchema(BaseModel): id: str text: str image: str author: PostAuthor date_published: str location: str like: PostLike bookmarked: bool key_comment: Optional[KeyComment] = None
from dotenv import load_dotenv load_dotenv() from flask.cli import FlaskGroup from api import create_app app = create_app() cli = FlaskGroup(create_app=create_app) if __name__ == "__main__": cli()
optional: CommentThreadOptionalParameters = CommentThreadOptionalParameters( maxResults=25 ) request: YouTubeRequest = YouTubeRequest( part=part, filter=filter, optional_parameters=optional ) comment_iterator: Iterator = youtube_client.get_comments_iterator(request) done: bool = False comment_count: int = 0 for comment_threads in comment_iterator: comments: list[str] = [] if done: break for comment_thread in comment_threads: comment: Comment = comment_thread.snippet.top_level_comment comments.append(comment.snippet.text_display) comment_count += 1 if comment_count > max_results: done = True break with open("comments.json", "r", encoding="utf-8") as f: existing_comments: list[str] = json.load(f) with open("comments.json", "w", encoding="utf-8") as f: existing_comments += comments json.dump(existing_comments, fp=f, indent=2) return comment_count client_secrets_file = "/home/lyle/Downloads/search.json" youtube_client = YouTube(client_secret_file=client_secrets_file) youtube_client_object = youtube_client.authenticate() youtube_client.youtube_client = youtube_client_object # print(get_video_id(video_title='iPhone 15 Pro Review: The Good, The Bad, & The Ugly!')) print(list_video_comments(video_id="cBpGq-vDr2Y"))
def setShape(self, shape): self.setData(shape, Qt.UserRole) def shape(self): return self.data(Qt.UserRole) def __hash__(self): return id(self) def __repr__(self): return '{}("{}")'.format(self.__class__.__name__, self.text()) class StandardItemModel(QtGui.QStandardItemModel): itemDropped = QtCore.Signal() def removeRows(self, *args, **kwargs): ret = super().removeRows(*args, **kwargs) self.itemDropped.emit() return ret class LabelListWidget(QtWidgets.QListView): itemDoubleClicked = QtCore.Signal(LabelListWidgetItem) itemSelectionChanged = QtCore.Signal(list, list) def __init__(self): super(LabelListWidget, self).__init__() self._selectedItems = [] self.setWindowFlags(Qt.Window) self.setModel(StandardItemModel()) self.model().setItemPrototype(LabelListWidgetItem()) self.setItemDelegate(HTMLDelegate()) self.setSelectionMode(QtWidgets.QAbstractItemView.ExtendedSelection) self.setDragDropMode(QtWidgets.QAbstractItemView.InternalMove) self.setDefaultDropAction(Qt.MoveAction) self.doubleClicked.connect(self.itemDoubleClickedEvent)
from qtpy import QtWidgets class ColorDialog(QtWidgets.QColorDialog): def __init__(self, parent=None): super(ColorDialog, self).__init__(parent) self.setOption(QtWidgets.QColorDialog.ShowAlphaChannel) # The Mac native dialog does not support our restore button. self.setOption(QtWidgets.QColorDialog.DontUseNativeDialog) # Add a restore defaults button. # The default is set at invocation time, so that it # works across dialogs for different elements. self.default = None self.bb = self.layout().itemAt(1).widget() self.bb.addButton(QtWidgets.QDialogButtonBox.RestoreDefaults) self.bb.clicked.connect(self.checkRestore) def getColor(self, value=None, title=None, default=None): self.default = default if title: self.setWindowTitle(title) if value: self.setCurrentColor(value) return self.currentColor() if self.exec_() else None def checkRestore(self, button): if ( self.bb.buttonRole(button) & QtWidgets.QDialogButtonBox.ResetRole and self.default ): self.setCurrentColor(self.default)
import os from .config import Config from flask import Flask def set_configuration(app: Flask): """Set the application configuration. The application configuration will depend on the environment i.e Test, Development, Staging or Production. Parameters ---------- app: flask.Flask A flask app instance Returns ------- bool: Whether the config was set up successfully. """ config_name = os.environ.get("FLASK_ENV") app.config.from_object(Config[config_name]) return True
from .register_blueprints import register_blueprints
optional_parameters: SearchOptionalParameters = SearchOptionalParameters( q=video_title, maxResults=1, type=["video"] ) search_request: YouTubeRequest = YouTubeRequest( part=part, optional_parameters=optional_parameters ) search_results: YouTubeResponse = youtube_client.search(search_request) search_result: Search = search_results.items[0] return search_result.resource_id def get_video_details(video: Search) -> Video: """Get video details""" response: YouTubeListResponse = youtube_client.find_video_by_id(video.resource_id) video: Video = response.items[0] return video def parse_video_details(video: Video) -> dict: return { "title": video.snippet.title, "description": video.snippet.description, "date": str(video.snippet.published_at), "views": video.statistics.views_count, "comments": video.statistics.comments_count, "likes": video.statistics.likes_count, } def get_videos(product: str, channel: str) -> list[dict]: videos: list[Search] = video_search(product=product, channel_title=channel) videos: list[Video] = map(get_video_details, videos) videos: list[dict] = map(parse_video_details, videos) return videos def create_videos_table(table_data: list[dict]) -> Table: table: Table = Table(row_styles=["dim", ""],leading=1, box=box.MINIMAL_DOUBLE_HEAD, title="[bold italic gold1]Youtube videos reviewing Iphone 15 pro[/bold italic gold1]") table.add_column(header="[b]Video Title", justify="left", style="dark_orange") table.add_column(header="Views", justify="left", style="light_coral")
def validate_config_item(key, value): if key == "validate_label" and value not in [None, "exact"]: raise ValueError( "Unexpected value for config key 'validate_label': {}".format(value) ) if key == "shape_color" and value not in [None, "auto", "manual"]: raise ValueError( "Unexpected value for config key 'shape_color': {}".format(value) ) if key == "labels" and value is not None and len(value) != len(set(value)): raise ValueError( "Duplicates are detected for config key 'labels': {}".format(value) ) def get_config(config_file_or_yaml=None, config_from_args=None): # 1. default config config = get_default_config() # 2. specified as file or yaml if config_file_or_yaml is not None: config_from_yaml = yaml.safe_load(config_file_or_yaml) if not isinstance(config_from_yaml, dict): with open(config_from_yaml) as f: logger.info("Loading config file from: {}".format(config_from_yaml)) config_from_yaml = yaml.safe_load(f) update_dict(config, config_from_yaml, validate_item=validate_config_item) # 3. command line argument or specified config file if config_from_args is not None: update_dict(config, config_from_args, validate_item=validate_config_item) return config
channel_names: list[str] = get_channel_names() playlist_name: str = 'Daily Videos' playlist_items: list[str] = workflow(youtube, channel_names) # print(get_channel_id('Asianometry')) # print(redis.setex(name='PL_26vmg8W_AcEEl_Bo2AhziS-93r6b8bu:DqkZCzjdtbw', time=1, value='')) # print(redis.setex(name='PL_26vmg8W_AcEEl_Bo2AhziS-93r6b8bu:VzW_BtXSw6A', time=1, value='')) # print(redis.get(name='PL_26vmg8W_AcEEl_Bo2AhziS-93r6b8bu:DqkZCzjdtbw')) # print(find_latest_video('UC1LpsuAUaKoMzzJSEt5WImw', youtube)) # channels: list[Channel] = get_all_channels(get_db) # latest_videos: list[Video] = [find_latest_video(channel.id, youtube) for channel in channels] # videos: list[Video] = Video.find().all() # for channel in channels: # redis.setex(f'latest:{channel.id}', value='video_str', time=1) # for video in latest_videos: # pl_id: str = 'PL_26vmg8W_AcEEl_Bo2AhziS-93r6b8bu' # redis.setex(name=f'{pl_id}:{video.resource_id}', time=1, value='') # for video in videos: # video.expire(num_seconds=1)
from crewai import Task from textwrap import dedent class ProductReviewTasks(): def research(self, agent, product): return Task(description=dedent(f""" Collect and summarize the most recent comments from the products review from youtube. Maje sure to capture the sentiment of each comment, what the user liked, did not like as well as other features that they wish were present. Your final answer MUST be a report that includes a comprehensive summary of the reviews, capturing the most loved features. {self.__tip_section()} Selected product by the customer: {product} """), agent=agent ) def __tip_section(self): return "If you do your BEST WORK, I'll give you a $10,000 commision!"
long_description=LONG_DESCRIPTION, url='https://youtube-assistant.readthedocs.io/en/latest/', author='Lyle Okoth', author_email='[email protected]', license='MIT', install_requires=install_requires, keywords=key_words, classifiers=[ 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.11', 'Programming Language :: Python :: 3.12', 'Operating System :: OS Independent' ], )
fitWidth, None, brightnessContrast, ), ) self.menus.file.aboutToShow.connect(self.updateFileMenu) # Custom context menu for the canvas widget: utils.addActions(self.canvas.menus[0], self.actions.menu) utils.addActions( self.canvas.menus[1], ( action("&Copy here", self.copyShape), action("&Move here", self.moveShape), ), ) selectAiModel = QtWidgets.QWidgetAction(self) selectAiModel.setDefaultWidget(QtWidgets.QWidget()) selectAiModel.defaultWidget().setLayout(QtWidgets.QVBoxLayout()) # selectAiModelLabel = QtWidgets.QLabel(self.tr("AI Model")) selectAiModelLabel.setAlignment(QtCore.Qt.AlignCenter) selectAiModel.defaultWidget().layout().addWidget(selectAiModelLabel) # self._selectAiModelComboBox = QtWidgets.QComboBox() selectAiModel.defaultWidget().layout().addWidget(self._selectAiModelComboBox) model_names = [model.name for model in MODELS] self._selectAiModelComboBox.addItems(model_names) if self._config["ai"]["default"] in model_names: model_index = model_names.index(self._config["ai"]["default"]) else: logger.warning( "Default AI model is not found: %r", self._config["ai"]["default"], ) model_index = 0 self._selectAiModelComboBox.setCurrentIndex(model_index) self._selectAiModelComboBox.currentIndexChanged.connect(
# popUp() + key_Up def interact(): qtbot.keyClick(widget.edit, QtCore.Qt.Key_Up) # 'person' -> 'dog' # NOQA qtbot.keyClick(widget.edit, QtCore.Qt.Key_Enter) # NOQA qtbot.keyClick(widget.edit, QtCore.Qt.Key_Enter) # NOQA QtCore.QTimer.singleShot(500, interact) label, flags, group_id, description = widget.popUp() assert label == "dog" assert flags == {} assert group_id is None assert description == ""
# This package will contain the spiders of your Scrapy project # # Please refer to the documentation for information on how to create and manage # your spiders.
self.selectedShapes[i].selected = False self.selectedShapes[i] = shape else: for i, shape in enumerate(self.selectedShapesCopy): self.selectedShapes[i].points = shape.points self.selectedShapesCopy = [] self.repaint() self.storeShapes() return True def hideBackroundShapes(self, value): self.hideBackround = value if self.selectedShapes: # Only hide other shapes if there is a current selection. # Otherwise the user will not be able to select a shape. self.setHiding(True) self.update() def setHiding(self, enable=True): self._hideBackround = self.hideBackround if enable else False def canCloseShape(self): return self.drawing() and self.current and len(self.current) > 2 def mouseDoubleClickEvent(self, ev): if self.double_click != "close": return if ( self.createMode == "polygon" and self.canCloseShape() ) or self.createMode in ["ai_polygon", "ai_mask"]: self.finalise() def selectShapes(self, shapes): self.setHiding() self.selectionChanged.emit(shapes) self.update() def selectShapePoint(self, point, multiple_selection_mode): """Select the first shape created which contains this point."""
self.labelList.itemSelectionChanged.connect(self.labelSelectionChanged) self.labelList.itemDoubleClicked.connect(self.editLabel) self.labelList.itemChanged.connect(self.labelItemChanged) self.labelList.itemDropped.connect(self.labelOrderChanged) self.shape_dock = QtWidgets.QDockWidget(self.tr("Polygon Labels"), self) self.shape_dock.setObjectName("Labels") self.shape_dock.setWidget(self.labelList) self.uniqLabelList = UniqueLabelQListWidget() self.uniqLabelList.setToolTip( self.tr( "Select label to start annotating for it. " "Press 'Esc' to deselect." ) ) if self._config["labels"]: for label in self._config["labels"]: item = self.uniqLabelList.createItemFromLabel(label) self.uniqLabelList.addItem(item) rgb = self._get_rgb_by_label(label) self.uniqLabelList.setItemLabel(item, label, rgb) self.label_dock = QtWidgets.QDockWidget(self.tr("Label List"), self) self.label_dock.setObjectName("Label List") self.label_dock.setWidget(self.uniqLabelList) self.fileSearch = QtWidgets.QLineEdit() self.fileSearch.setPlaceholderText(self.tr("Search Filename")) self.fileSearch.textChanged.connect(self.fileSearchChanged) self.fileListWidget = QtWidgets.QListWidget() self.fileListWidget.itemSelectionChanged.connect(self.fileSelectionChanged) fileListLayout = QtWidgets.QVBoxLayout() fileListLayout.setContentsMargins(0, 0, 0, 0) fileListLayout.setSpacing(0) fileListLayout.addWidget(self.fileSearch) fileListLayout.addWidget(self.fileListWidget) self.file_dock = QtWidgets.QDockWidget(self.tr("File List"), self) self.file_dock.setObjectName("Files") fileListWidget = QtWidgets.QWidget() fileListWidget.setLayout(fileListLayout) self.file_dock.setWidget(fileListWidget)
# TODO(unknown): # - Zoom is too "steppy". LABEL_COLORMAP = imgviz.label_colormap() class MainWindow(QtWidgets.QMainWindow): FIT_WINDOW, FIT_WIDTH, MANUAL_ZOOM = 0, 1, 2 def __init__( self, config=None, filename=None, output=None, output_file=None, output_dir=None, ): if output is not None: logger.warning("argument output is deprecated, use output_file instead") if output_file is None: output_file = output # see labelme/config/default_config.yaml for valid configuration if config is None: config = get_config() self._config = config # set default shape colors Shape.line_color = QtGui.QColor(*self._config["shape"]["line_color"]) Shape.fill_color = QtGui.QColor(*self._config["shape"]["fill_color"]) Shape.select_line_color = QtGui.QColor( *self._config["shape"]["select_line_color"] ) Shape.select_fill_color = QtGui.QColor( *self._config["shape"]["select_fill_color"] ) Shape.vertex_fill_color = QtGui.QColor( *self._config["shape"]["vertex_fill_color"] )
full_chain = { "sentiment": sentiment_chain, "comment": lambda input: input['comment'], "topics": lambda input: input['topics'] } | branch res = full_chain.invoke({'comment': comment, "topics": topics}) print(comment) print(res)
self.canvas.setEnabled(True) # set zoom values is_initial_load = not self.zoom_values if self.filename in self.zoom_values: self.zoomMode = self.zoom_values[self.filename][0] self.setZoom(self.zoom_values[self.filename][1]) elif is_initial_load or not self._config["keep_prev_scale"]: self.adjustScale(initial=True) # set scroll values for orientation in self.scroll_values: if self.filename in self.scroll_values[orientation]: self.setScroll( orientation, self.scroll_values[orientation][self.filename] ) # set brightness contrast values dialog = BrightnessContrastDialog( utils.img_data_to_pil(self.imageData), self.onNewBrightnessContrast, parent=self, ) brightness, contrast = self.brightnessContrast_values.get( self.filename, (None, None) ) if self._config["keep_prev_brightness"] and self.recentFiles: brightness, _ = self.brightnessContrast_values.get( self.recentFiles[0], (None, None) ) if self._config["keep_prev_contrast"] and self.recentFiles: _, contrast = self.brightnessContrast_values.get( self.recentFiles[0], (None, None) ) if brightness is not None: dialog.slider_brightness.setValue(brightness) if contrast is not None: dialog.slider_contrast.setValue(contrast) self.brightnessContrast_values[self.filename] = (brightness, contrast) if brightness is not None or contrast is not None: dialog.onNewValue(None) self.paintCanvas() self.addRecentFile(self.filename)
# (from other spider middleware) raises an exception. # Should return either None or an iterable of Request or item objects. pass def process_start_requests(self, start_requests, spider): # Called with the start requests of the spider, and works # similarly to the process_spider_output() method, except # that it doesn’t have a response associated. # Must return only requests (not items). for r in start_requests: yield r def spider_opened(self, spider): spider.logger.info("Spider opened: %s" % spider.name) class SlidesgoDownloaderMiddleware: # Not all methods need to be defined. If a method is not defined, # scrapy acts as if the downloader middleware does not modify the # passed objects. @classmethod def from_crawler(cls, crawler): # This method is used by Scrapy to create your spiders. s = cls() crawler.signals.connect(s.spider_opened, signal=signals.spider_opened) return s def process_request(self, request, spider): # Called for each request that goes through the downloader # middleware. # Must either: # - return None: continue processing this request # - or return a Response object # - or return a Request object # - or raise IgnoreRequest: process_exception() methods of # installed downloader middleware will be called
self.client_secret_file = client_secret_file if not self.client_secret_file: raise ValueError('The client secret file must be provided.') api_service_name: str = 'drive' api_version: str = 'v3' credentials_dir: str = '.drive_credentials' scopes: list[str] = [ GoogleDriveScopes.metadata.value, GoogleDriveScopes.drive.value, GoogleDriveScopes.files.value, GoogleDriveScopes.activity.value, ] oauth: GoogleOAuth = GoogleOAuth( secrets_file=self.client_secret_file, scopes=scopes, api_service_name=api_service_name, api_version=api_version, credentials_dir=credentials_dir, ) self.drive_client = oauth.authenticate_google_server() return self.drive_client def create_file(self) -> None: """Creates a new file on drive.""" raise NotImplementedError() def upload_file(self) -> None: """Upload a file to drive.""" raise NotImplementedError() def resumable_upload(self) -> None: raise NotImplementedError()
def send_email_local(user_email_address: str, message: str) -> None: pass def send_email_aws_ses(user_email_address: str, message: str) -> None: pass def send_account_activation_email(user_email_address: str, message: str) -> None: pass def send_password_reset_email(user_email_address: str, message: str) -> None: pass def generate_account_activation_email(message: str) -> None: pass def generate_password_reset_email(message: str) -> None: pass
import os.path as osp import numpy as np import PIL.Image from labelme.utils import image as image_module from .util import data_dir from .util import get_img_and_data def test_img_b64_to_arr(): img, _ = get_img_and_data() assert img.dtype == np.uint8 assert img.shape == (907, 1210, 3) def test_img_arr_to_b64(): img_file = osp.join(data_dir, "annotated_with_data/apc2016_obj3.jpg") img_arr = np.asarray(PIL.Image.open(img_file)) img_b64 = image_module.img_arr_to_b64(img_arr) img_arr2 = image_module.img_b64_to_arr(img_b64) np.testing.assert_allclose(img_arr, img_arr2) def test_img_data_to_png_data(): img_file = osp.join(data_dir, "annotated_with_data/apc2016_obj3.jpg") with open(img_file, "rb") as f: img_data = f.read() png_data = image_module.img_data_to_png_data(img_data) assert isinstance(png_data, bytes)
from redis import Redis from config.config import app_config from celery import Celery from utils import extract_dataset from schemas import Model, TrainedModel, TunedModel import logging from schemas import Metrics from datetime import datetime from sklearn.metrics import accuracy_score, precision_score, f1_score, recall_score from time import perf_counter from sklearn.pipeline import Pipeline from experiment_param_grids import hyperparameters from sklearn.model_selection import GridSearchCV from sklearn.base import BaseEstimator from schemas.train_config import TrainConfig from os import path from utils import send_email redis: Redis = Redis(host=app_config.redis.redis_host, port=app_config.redis.redis_port, decode_responses=True) celery = Celery(__name__) celery.conf.broker_url = app_config.celery_broker_url celery.conf.result_backend = app_config.celery_result_backend celery.conf.event_serializer = 'pickle' # this event_serializer is optional. somehow i missed this when writing this solution and it still worked without. celery.conf.task_serializer = 'pickle' celery.conf.result_serializer = 'pickle' celery.conf.accept_content = ['application/json', 'application/x-python-serialize'] @celery.task(name='send_training_report_task') def send_training_report_task(training_result): try: logging.info('Sending the email') send_email() except Exception as e: logging.error(f'Unable to send email: {str(e)}') else: logging.info('Email sent') return training_result
import ast from ast import FunctionDef from queue import Queue from .helpers import read_src class FunctionVisitor(ast.NodeVisitor): def __init__(self, function_code_queue: Queue, file_path: str) -> None: super().__init__() self._function_code_queue = function_code_queue self._file_path = file_path def visit_FunctionDef(self, node: FunctionDef) -> None: function_code: str = ast.unparse(ast_obj=node) self._function_code_queue.put((self._file_path, function_code))
from celery import Celery from config import CeleryConfig celery_app: Celery = Celery(__name__) celery_app.config_from_object(CeleryConfig) celery_app.conf.beat_schedule = { 'clear-daily-playlist': { 'task': 'tasks.clear_daily_playlist', 'schedule': 10 } } celery_app.autodiscover_tasks(['tasks'])
self.tr("Zoom follows window width"), checkable=True, enabled=False, ) brightnessContrast = action( "&Brightness Contrast", self.brightnessContrast, None, "color", "Adjust brightness and contrast", enabled=False, ) # Group zoom controls into a list for easier toggling. zoomActions = ( self.zoomWidget, zoomIn, zoomOut, zoomOrg, fitWindow, fitWidth, ) self.zoomMode = self.FIT_WINDOW fitWindow.setChecked(Qt.Checked) self.scalers = { self.FIT_WINDOW: self.scaleFitWindow, self.FIT_WIDTH: self.scaleFitWidth, # Set to one to scale to 100% when loading files. self.MANUAL_ZOOM: lambda: 1, } edit = action( self.tr("&Edit Label"), self.editLabel, shortcuts["edit_label"], "edit", self.tr("Modify the label of the selected polygon"), enabled=False, ) fill_drawing = action(
def create_application_config(args: Namespace) -> Config: config: Config = Config( root_directory=set(args.path), overwrite_function_docstring=args.overwrite_function_docstring, documentation_style=args.documentation_style, ) config.directories_ignore.update(set(args.directories_ignore)) config.files_ignore.update(set(args.files_ignore)) return config
n_classes = 4 maizenet = MaizeNet(n_classes) maizenet.load_state_dict(torch.load(model_path, map_location=torch.device('cpu') )) return maizenet def preprocess_image(image): mean = np.array([0.5, 0.5, 0.5]) std = np.array([0.25, 0.25, 0.25]) data_transform = transforms.Compose([ transforms.RandomResizedCrop(224), # resize and crop image to 224 x 224 pixels transforms.RandomHorizontalFlip(), # flip the images horizontally transforms.ToTensor(), # convert to pytorch tensor data type transforms.Normalize(mean, std) # normalize the input image dataset. ]) transformed_image = data_transform(image).to('cpu') transformed_image = torch.unsqueeze(transformed_image, 0) return transformed_image def evaluate_image(image, model): transformed_image = preprocess_image(image) labels = ['Maize Leaf Rust', 'Northern Leaf Blight', 'Healthy', 'Gray Leaf Spot'] model.eval() prediction = F.softmax(model(transformed_image), dim = 1) data = { 'Maize Leaf Rust': round(float(prediction[0][0]), 4) * 100, 'Northern Leaf Blight': round(float(prediction[0][1]) * 100, 4), 'Healthy': round(float(prediction[0][2]), 4) * 100, 'Gray Leaf Spot': round(float(prediction[0][3]) * 100, 4) } prediction = prediction.argmax() return labels[prediction], data
# slide_item = loader.load_item() # link = slide.css(".item a::attr(href)").get() # self.logger.info("Parsing the slide") # yield Request(link, callback=self.parse_slide, meta={"slide_item": slide_item}) def parse_problem(self, response: Response, **kwargs: Any) -> Any: # slide_item = response.meta["slide_item"] # loader = ItemLoader(item=slide_item, response=response) # loader.add_css(field_name="tags", css=".Sm-tags a.mr-2::text") # loader.add_css(field_name="description", css=".product-text p") # loader.add_css(field_name="slides_count", css='h4 small::text') # loader.add_css(field_name="colors", css='li.color a::text') # loader.add_css(field_name="image_urls", css='a.preview-link img::attr(src)') # add slide link # yield loader.load_item() categories: list[dict] = [] cats = response.css('span.cat-links a') for cat in cats: category = cat.css('::text').get() category_link = cat.css('::attr(href)').get() categories.append({ "category": category, "link": category_link }) yield { "categories": categories, "title": response.css('h1::text').get(), # "problem": response.css('.post-content p').getall(), "conditions": response.css('.post-content ol').get(), # "io": response.css('.io').get(), # "solutions": response.css('h2::text').getall(), # "link": response.url, # "code": response.css('.c-line').getall(), "image": response.css('.post-content p img::attr(src)').get() }
#AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0 # Enable showing throttling stats for every response received: #AUTOTHROTTLE_DEBUG = False # Enable and configure HTTP caching (disabled by default) # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings #HTTPCACHE_ENABLED = True #HTTPCACHE_EXPIRATION_SECS = 0 #HTTPCACHE_DIR = "httpcache" #HTTPCACHE_IGNORE_HTTP_CODES = [] #HTTPCACHE_STORAGE = "scrapy.extensions.httpcache.FilesystemCacheStorage" # Set settings whose default value is deprecated to a future-proof value REQUEST_FINGERPRINTER_IMPLEMENTATION = "2.7" TWISTED_REACTOR = "twisted.internet.asyncioreactor.AsyncioSelectorReactor" FEED_EXPORT_ENCODING = "utf-8" IMAGES_URLS_FIELD = "image_urls" IMAGES_RESULT_FIELD = "images" IMAGES_STORE = "/home/lyle/oryks/scrapy-tutorial/slidesmodel/images" CONNECTION_STRING = "sqlite:////home/lyle/oryks/scrapy-tutorial/data/slides.db" START_URLS_PATH = "/home/lyle/oryks/scrapy-tutorial/links.json"
# utils.py from playwright.sync_api import sync_playwright import uuid from PIL import Image from PIL import Image import io from os import path import json index: int = 1 def take_screenshot_from_url(url, session_data): with sync_playwright() as playwright: webkit = playwright.webkit browser = webkit.launch() browser_context = browser.new_context(device_scale_factor=2) browser_context.add_cookies([session_data]) page = browser_context.new_page() page.goto(url) screenshot_bytes = page.locator(".code").screenshot() browser.close() return screenshot_bytes def save_data(image_bytes: bytes, code: str) -> None: file_name: str = str(uuid.uuid4()) image: Image = Image.open(io.BytesIO(image_bytes)) file_path: str = "data" image_path: str = path.join(file_path, f"{file_name}.png") image.save(image_path) code_path: str = path.join(file_path, "metadata.jsonl") metadata: dict = { "file_name": f"{file_name}.png", "code": code } with open(code_path, "a+", encoding="utf-8") as f: f.write(json.dumps(metadata) + "\n")
api_version=api_version, credentials_dir=credentials_dir, credentials_file_name=credentials_file_name ) gslides_client = auth.authenticate_google_server() return gslides_client def create_drive_client() -> Any: secrets_file: str = "/home/lyle/oryks/backend/api/libraries/drive.json" scopes: list[str] = [ GoogleDriveScopes.metadata.value, GoogleDriveScopes.drive.value, GoogleDriveScopes.files.value ] api_service_name: str = "drive" api_version: str = "v3" credentials_dir: str = GoogleDirectories.drive.value credentials_file_name: Optional[str] = 'credentials.json' auth: GoogleOAuth = GoogleOAuth( secrets_file=secrets_file, scopes=scopes, api_service_name=api_service_name, api_version=api_version, credentials_dir=credentials_dir, credentials_file_name=credentials_file_name ) drive_client = auth.authenticate_google_server() return drive_client def get_youtube_client() -> YouTube: client_secrets_file: str = "/home/lyle/oryks/backend/api/libraries/youtube.json" youtube: YouTube = YouTube(client_secret_file=client_secrets_file) return youtube youtube_client: YouTube = get_youtube_client()
from setuptools import find_packages, setup from pip._vendor import tomli # For consistent encoding from codecs import open from os import path # The directory containing this file HERE = path.abspath(path.dirname(__file__)) # Get the long description from the README file with open(path.join(HERE, 'README.md'), encoding='utf-8') as f: LONG_DESCRIPTION = f.read() with open('pyproject.toml', 'r') as f: VERSION = tomli.load(f)['tool']['commitizen']['version'] DESCRIPTION = 'A python library for authenticating requests for various google services including ``gmail``, ``youtube``, ``drive`` and ``calendar``.' key_words = [ 'google-auth', ] install_requires = [ 'google-api-python-client', 'google-auth-oauthlib', 'pydantic', 'pydantic-settings' ] setup( name='oryks-google-oauth', packages=find_packages( include=[ 'oryks_google_oauth', ] ), version=VERSION, description=DESCRIPTION, long_description_content_type='text/markdown',
# React to canvas signals. def shapeSelectionChanged(self, selected_shapes): self._noSelectionSlot = True for shape in self.canvas.selectedShapes: shape.selected = False self.labelList.clearSelection() self.canvas.selectedShapes = selected_shapes for shape in self.canvas.selectedShapes: shape.selected = True item = self.labelList.findItemByShape(shape) self.labelList.selectItem(item) self.labelList.scrollToItem(item) self._noSelectionSlot = False n_selected = len(selected_shapes) self.actions.delete.setEnabled(n_selected) self.actions.duplicate.setEnabled(n_selected) self.actions.copy.setEnabled(n_selected) self.actions.edit.setEnabled(n_selected == 1) def addLabel(self, shape): if shape.group_id is None: text = shape.label else: text = "{} ({})".format(shape.label, shape.group_id) label_list_item = LabelListWidgetItem(text, shape) self.labelList.addItem(label_list_item) if self.uniqLabelList.findItemByLabel(shape.label) is None: item = self.uniqLabelList.createItemFromLabel(shape.label) self.uniqLabelList.addItem(item) rgb = self._get_rgb_by_label(shape.label) self.uniqLabelList.setItemLabel(item, shape.label, rgb) self.labelDialog.addLabelHistory(shape.label) for action in self.actions.onShapesPresent: action.setEnabled(True) self._update_shape_color(shape) label_list_item.setText( '{} <font color="#{:02x}{:02x}{:02x}">●</font>'.format( html.escape(text), *shape.fill_color.getRgb()[:3] )
# part=part, # optional_parameters=optional_parameters # ) # search_results: YouTubeResponse = youtube.search(search_request) # print(search_results) # print(youtube.find_my_channel()) # part: CommentThreadPart = CommentThreadPart() # filter: CommentThreadFilter = CommentThreadFilter( # videoId='Tuc-rjJbsXU' # ) # optional: CommentThreadOptionalParameters = CommentThreadOptionalParameters( # maxResults=5 # ) # request:YouTubeRequest = YouTubeRequest( # part=part, # filter=filter, # optional_parameters=optional # ) # comment_iterator: Iterator = youtube.get_comments_iterator(request) # video_comments: list[Comment] = list() # for comment_threads in comment_iterator: # for comment_thread in comment_threads: # comment: Comment = comment_thread.snippet.top_level_comment # video_comments.append(comment) # print(video_comments) # comment_id: str = 'UgzdXi_vWhXLkBA_Pwt4AaABAg' # response = youtube.get_comment(comment_id) # print(response) # import json # with open('comment.json', 'w') as f: # json.dump(response, f, indent=4) # from youtube.resources.comment_thread.comment import CommentResource # import json # comment_res = CommentResource(youtube_client) # with open('comment.json', 'r') as f: # comments = json.load(f) # print(comment_res.parse_youtube_list_response(comments)) # replies = youtube.get_comment_replies('UgxwXLTWugMg7IEoKgR4AaABAg') # import json
for shape in sorted(data["shapes"], key=lambda x: x["label"]): label_name = shape["label"] if label_name in label_name_to_value: label_value = label_name_to_value[label_name] else: label_value = len(label_name_to_value) label_name_to_value[label_name] = label_value lbl, _ = utils.shapes_to_label(img.shape, data["shapes"], label_name_to_value) label_names = [None] * (max(label_name_to_value.values()) + 1) for name, value in label_name_to_value.items(): label_names[value] = name lbl_viz = imgviz.label2rgb( lbl, imgviz.asgray(img), label_names=label_names, loc="rb" ) PIL.Image.fromarray(img).save(osp.join(out_dir, "img.png")) utils.lblsave(osp.join(out_dir, "label.png"), lbl) PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, "label_viz.png")) with open(osp.join(out_dir, "label_names.txt"), "w") as f: for lbl_name in label_names: f.write(lbl_name + "\n") logger.info("Saved to: {}".format(out_dir)) if __name__ == "__main__": main()
from dotenv import load_dotenv load_dotenv() from flask.cli import FlaskGroup from api import create_app app = create_app() cli = FlaskGroup(create_app=create_app) if __name__ == "__main__": cli()
import os.path as osp from math import sqrt import numpy as np from qtpy import QtCore from qtpy import QtGui from qtpy import QtWidgets here = osp.dirname(osp.abspath(__file__)) def newIcon(icon): icons_dir = osp.join(here, "../icons") return QtGui.QIcon(osp.join(":/", icons_dir, "%s.png" % icon)) def newButton(text, icon=None, slot=None): b = QtWidgets.QPushButton(text) if icon is not None: b.setIcon(newIcon(icon)) if slot is not None: b.clicked.connect(slot) return b def newAction( parent, text, slot=None, shortcut=None, icon=None, tip=None, checkable=False, enabled=True, checked=False, ): """Create a new action and assign callbacks, shortcuts, etc.""" a = QtWidgets.QAction(text, parent) if icon is not None: a.setIconText(text.replace(" ", "\n"))
import os from .config import Config from flask import Flask def set_configuration(app: Flask): """Set the application configuration. The application configuration will depend on the environment i.e Test, Development, Staging or Production. Parameters ---------- app: flask.Flask A flask app instance Returns ------- bool: Whether the config was set up successfully. """ config_name = os.environ.get("FLASK_ENV") app.config.from_object(Config[config_name]) return True
# sort_key=lambda x: len(x.src), # device=device, # ) # encoder_net = Encoder( # input_size_encoder, encoder_embedding_size, hidden_size, num_layers, enc_dropout # ).to(device) # decoder_net = Decoder( # input_size_decoder, # decoder_embedding_size, # hidden_size, # output_size, # num_layers, # dec_dropout, # ).to(device) # model = Seq2Seq(encoder_net, decoder_net).to(device) # optimizer = optim.Adam(model.parameters(), lr=learning_rate) # pad_idx = english.vocab.stoi["<pad>"] # criterion = nn.CrossEntropyLoss(ignore_index=pad_idx) # if load_model: # load_checkpoint(torch.load("my_checkpoint.pth.tar"), model, optimizer) # sentence = "ein boot mit mehreren männern darauf wird von einem großen pferdegespann ans ufer gezogen." # for epoch in range(num_epochs): # print(f"[Epoch {epoch} / {num_epochs}]") # checkpoint = {"state_dict": model.state_dict(), "optimizer": optimizer.state_dict()} # save_checkpoint(checkpoint) # model.eval() # translated_sentence = translate_sentence( # model, sentence, german, english, device, max_length=50 # )
mask = labelme.utils.shape_to_mask(img.shape[:2], points, shape_type) if group_id is None: group_id = uuid.uuid1() instance = (label, group_id) if instance in masks: masks[instance] = masks[instance] | mask else: masks[instance] = mask if shape_type == "rectangle": (x1, y1), (x2, y2) = points x1, x2 = sorted([x1, x2]) y1, y2 = sorted([y1, y2]) points = [x1, y1, x2, y1, x2, y2, x1, y2] if shape_type == "circle": (x1, y1), (x2, y2) = points r = np.linalg.norm([x2 - x1, y2 - y1]) # r(1-cos(a/2))<x, a=2*pi/N => N>pi/arccos(1-x/r) # x: tolerance of the gap between the arc and the line segment n_points_circle = max(int(np.pi / np.arccos(1 - 1 / r)), 12) i = np.arange(n_points_circle) x = x1 + r * np.sin(2 * np.pi / n_points_circle * i) y = y1 + r * np.cos(2 * np.pi / n_points_circle * i) points = np.stack((x, y), axis=1).flatten().tolist() else: points = np.asarray(points).flatten().tolist() segmentations[instance].append(points) segmentations = dict(segmentations) for instance, mask in masks.items(): cls_name, group_id = instance if cls_name not in class_name_to_id: continue cls_id = class_name_to_id[cls_name] mask = np.asfortranarray(mask.astype(np.uint8))
self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None ) -> str: """Use the tool asynchronously.""" raise NotImplementedError("Calculator does not support async") class YouTubeChannelVideoSearchTool(BaseTool): name = "youtube_channel_video_search" description = "useful for when you need to answer questions about videos for a youtube channel" args_schema: Type[BaseModel] = YouTubeChannelSearch def _run( self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None ) -> str: """Use the tool.""" return '' async def _arun( self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None ) -> str: """Use the tool asynchronously.""" raise NotImplementedError("Calculator does not support async") llm = ChatOpenAI( temperature=0, openai_api_key=config.open_ai_token, ) tools = [ YouTubeChannelTitleSearchTool(), YouTubeChannelVideoSearchTool(), YouTubeChannelSearchTool() ] agent = initialize_agent( tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True, handle_parsing_errors=True )
from .set_config import set_configuration
import ast import os import subprocess from argparse import ArgumentParser, Namespace from ast import AsyncFunctionDef, ClassDef, Constant, Expr, FunctionDef from collections import deque from os import listdir, path from queue import Queue from typing import Iterator from langchain.prompts import PromptTemplate from .config import Config from .extensions import llm from .templates import get_function_prompt_template, get_class_prompt_template def generate_function_docstring(function_code: str, config: Config) -> str: prompt_formatted_str: str = get_function_prompt_template( function_code=function_code, config=config ) function_and_docstring = llm.invoke(prompt_formatted_str) return function_and_docstring def generate_class_docstring(class_code: str, config: Config) -> str: prompt_formatted_str: str = get_class_prompt_template( class_code=class_code, config=config ) class_and_docstring = llm.invoke(prompt_formatted_str) return class_and_docstring def get_class_docstring(class_and_docstring: str) -> str: """Get the class docstring.""" class_tree = ast.parse(class_and_docstring) for node in class_tree.body: if isinstance(node, ClassDef): cls_docstring: str = ast.get_docstring(node) return cls_docstring
def activate_user_account(session: Session, activation_data: ActivateUser): with session() as db: user: User = db.query(User).filter(User.id == activation_data.user_id).first() if user.id == User.decode_auth_token(activation_data.activation_token): user.activated = True db.commit() return True raise InvalidTokenError('Invalid or Expired token.') def loggin_user(session: Session, login_data: LoginUser): with session() as db: user: User = db.query(User).filter(User.email_address == login_data.email_address).first() if user and user.check_password(login_data.password): return True raise ValueError('Invalid email address and or password.')
self.addLabel(shape) self.labelList.clearSelection() self._noSelectionSlot = False self.canvas.loadShapes(shapes, replace=replace) def loadLabels(self, shapes): s = [] for shape in shapes: label = shape["label"] points = shape["points"] shape_type = shape["shape_type"] flags = shape["flags"] description = shape.get("description", "") group_id = shape["group_id"] other_data = shape["other_data"] if not points: # skip point-empty shape continue shape = Shape( label=label, shape_type=shape_type, group_id=group_id, description=description, mask=shape["mask"], ) for x, y in points: shape.addPoint(QtCore.QPointF(x, y)) shape.close() default_flags = {} if self._config["label_flags"]: for pattern, keys in self._config["label_flags"].items(): if re.match(pattern, label): for key in keys: default_flags[key] = False shape.flags = default_flags shape.flags.update(flags) shape.other_data = other_data
dest="config", help="config file or yaml-format string (default: {})".format( default_config_file ), default=default_config_file, ) # config for the gui parser.add_argument( "--nodata", dest="store_data", action="store_false", help="stop storing image data to JSON file", default=argparse.SUPPRESS, ) parser.add_argument( "--autosave", dest="auto_save", action="store_true", help="auto save", default=argparse.SUPPRESS, ) parser.add_argument( "--nosortlabels", dest="sort_labels", action="store_false", help="stop sorting labels", default=argparse.SUPPRESS, ) parser.add_argument( "--flags", help="comma separated list of flags OR file containing flags", default=argparse.SUPPRESS, ) parser.add_argument( "--labelflags", dest="label_flags", help=r"yaml string of label specific flags OR file containing json " r"string of label specific flags (ex. {person-\d+: [male, tall], " r"dog-\d+: [black, brown, white], .*: [occluded]})", # NOQA default=argparse.SUPPRESS,
from sqlalchemy.orm import Session from ..models.post import Post from ..schemas.post import ( CreatePost, GetPosts, GetPost, UpdatePost ) from werkzeug.datastructures import FileStorage from flask import current_app from uuid import uuid4 from werkzeug.utils import secure_filename import os import secrets from typing import Callable
def create_like(session: Session, activity: CreateActivity) -> Like: with session() as db: like: Like = Like( author_id=activity.user_id, post_id=activity.post_id ) db.add(like) db.commit() db.refresh(like) return like
session.add(slide) session.commit() except: session.rollback() raise finally: session.close() return item class DuplicatesPipeline(object): def __init__(self): """ Initializes database connection and sessionmaker. Creates tables. """ engine = db_connect() create_table(engine) self.Session = sessionmaker(bind=engine) logging.info("****DuplicatesPipeline: database connected****") def process_item(self, item: Item, spider: Spider): session = self.Session() exist_slide = session.query(Slide).filter_by(title=item["title"]).first() session.close() if exist_slide is not None: # the current slide exists raise DropItem("Duplicate item found: %s" % item["title"]) else: return item
@post.route("/views", methods=["GET"]) def get_post_views(): """Get a posts comments.""" try: post_data = GetPost(post_id=request.args.get('post_id')) except ValidationError: return {'error': 'Invalid input: you probably did not include the post id.'}, HTTPStatus.BAD_REQUEST try: post: Post = get_post(session=get_db, post_data=post_data) if not post: return {'Error': f'post with id {post_data.post_id} does not exists'}, HTTPStatus.NOT_FOUND views: list[View] = list_post_views(session=get_db, post_data=post_data) except (OperationalError, IntegrityError) as e: print(e) # Send email to return {'Error': 'The application is experiencing a tempoary error. Please try again in a few minutes.'}, HTTPStatus.INTERNAL_SERVER_ERROR resp = [ RepeatableActivityCreated( user_id=view.author_id, post_id=view.post_id, date_created=view.view_date, id=view.id ).model_dump() for view in views ] return resp, HTTPStatus.OK
def has_viewed(session: Session, activity: CreateActivity) -> View: with session() as db: view: View = db.query(View).filter(View.author_id==activity.user_id, View.post_id==activity.post_id).first() if view: return True return False def list_user_views(session: Session, user_data: GetUser) -> list[View]: with session() as db: user: User = db.query(User).filter(User.id == user_data.user_id).first() views: list[View] = user.views return views def list_post_views(session: Session, post_data: GetPost): with session() as db: post: Post = db.query(Post).filter(Post.id == post_data.post_id).first() views: list[View] = post.views return views
function_name=function_name, function_code=function_code, config=config, ) new_tree = transformer.visit(module_tree) ast.fix_missing_locations(new_tree) new_module_code = ast.unparse(new_tree) except Empty: continue except Exception as e: print(e) functions_source_queue.task_done() continue else: save_processed_file( file_path=module_path, processed_module_code=new_module_code ) format_file(module_path) functions_source_queue.task_done() def generate_class_docstrings(class_source_queue: Queue, config: Config) -> None: """Generate docstrings for this file.""" while True: try: module_path, class_name, class_code = class_source_queue.get() module_tree = ast.parse(get_module_source_code(module_path)) transformer = ClassDocStringWriter( module_path=module_path, class_name=class_name, class_code=class_code, config=config, ) new_tree = transformer.visit(module_tree) ast.fix_missing_locations(new_tree) new_module_code = ast.unparse(new_tree) except Empty: continue except Exception as e: print(e) class_source_queue.task_done()
from .register_blueprints import register_blueprints
def get_exception(exc): """Log exceptions""" if exc: app_logger.warning(f"{exc.__class__.__name__ }: {str(exc)}") def register_app_hooks(app: Flask): @app.before_first_request def application_startup(): """Log the beginning of the application.""" app_logger.info('Web app is up!') @app.before_request def log_request(): """Log the data held in the request""" if request.method in ['POST', 'PUT']: log_post_request() elif request.method in ['GET', 'DELETE']: log_get_request() @app.after_request def log_response(response): try: get_response(response) except Exception: pass finally: return response @app.teardown_request def log_exception(exc): get_exception(exc)
from dotenv import load_dotenv load_dotenv() from assistant.agents import default_agent import chainlit as cl @cl.on_chat_start async def start(): cl.user_session.set('agent', default_agent) @cl.on_message async def main(message: cl.Message): agent = cl.user_session.get('agent') msg = cl.Message(content='') await msg.send() await cl.sleep(1) msg.content = agent.invoke({'input': message.content})['output'] await msg.update()
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