yptheangel

import shap
import streamlit as st
import streamlit.components.v1 as components
import xgboost
import matplotlib.pyplot as plt

@st.cache
def load_data():
    return shap.datasets.boston()

yptheangel

let x;
let y;
let xspeed;
let yspeed;

let r,g,b ;

function preload() {
  logo  = loadImage("logo.png")
}

yptheangel

custom_data_path = 'path_to_your_ner_dataset.jsonl'
import json
 
with open(custom_data_path, 'r', encoding="utf8") as json_file:
    json_list = list(json_file)

dataset = [json.loads(jline) for jline in json_list]
print(f"number of examples : {len(dataset)}")

yptheangel

import numpy as np
import pandas as pd
import shap

explainer = shap.TreeExplainer("your_tree_model")
shap_values = explainer.shap_values(X_train)
scores= np.abs(shap_values).mean(0)
feature_importance = pd.DataFrame(list(zip(X_train.columns, sum(scores))), columns=['feature','feature_importance_score'])
feature_importance.sort_values(by=['feature_importance_score'], ascending=False,inplace=True)

yptheangel

import cv2
import numpy as np


def augment_brightness(img, scale):
    img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    h, s, v = cv2.split(img)
    scale = 0.5
    v = np.clip(v * scale, 0, 255, out=v)
    img = cv2.merge((h, s, v))

yptheangel

import streamlit as st
import numpy as np
from PIL import Image
import cv2
from io import BytesIO
import base64
import uuid
import re

@st.cache

yptheangel

import mediapipe as mp
import cv2

mp_objectron = mp.solutions.objectron
mp_drawing = mp.solutions.drawing_utils

if __name__ == '__main__':
    objectron = mp_objectron.Objectron(static_image_mode=True,
                                       max_num_objects=5,
                                       min_detection_confidence=0.5,

yptheangel

from google.cloud import bigquery
import os
import pandas as pd

os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "YOURGCPSERVICEACCOUNTKEY.json"
GCP_PROJECT_ID = "YOURGCPPROJECT"

client = bigquery.Client(project=GCP_PROJECT_ID)
datasets = list(client.list_datasets())
project = client.project

yptheangel

import cv2

filename = "yourfilename.jpg"
frame = cv2.imread(filename)
overlay = frame.copy()

text = "I love opencv"
font, font_scale, font_thickness = cv2.FONT_ITALIC, 3.5, 5

textsize = cv2.getTextSize(text, font, font_scale, font_thickness)[0] # get text size

yptheangel

trainingInput:
  scaleTier: CUSTOM
  masterType: n1-standard-4
  hyperparameters:
    goal: MAXIMIZE
    maxTrials: 90
    maxParallelTrials: 8
    algorithm: GRID_SEARCH
    hyperparameterMetricTag: val_accuracy
    params: