devqueue

Assaydashboard

A Dashboard application built using django and chart.js

Data requirements

1. The uploaded CSV files must contain be in the following format.

Assay	January	November	December	Year	AssayID	MachineID
Sick panel (MS/MS)	279	219	220	2020	SICKPANEL_20	FI-MSMS

devqueue

# Force http to https
server {
    server_name genelookup.research.cgm.genetics.kfshrc.edu.sa www.genelookup.research.cgm.genetics.kfshrc.edu.sa; 
    return 301 https://genelookup.research.cgm.genetics.kfshrc.edu.sa$request_uri;
}

# NGINX server block
server {
    server_name genelookup.research.cgm.genetics.kfshrc.edu.sa www.genelookup.research.cgm.genetics.kfshrc.edu.sa; 
    

devqueue

<div class="gmail_quote">
    <div dir="auto">
        <div>
            <div class="gmail_quote">
                <div class="adM"><br></div><u></u>
                <div style="background-color:transparent;margin:0;padding:0">
                    <table border="0" cellpadding="0" cellspacing="0" role="presentation"
                        style="background-color:transparent" width="100%">
                        <tbody>
                            <tr>

devqueue

import requests
import os
import pandas as pd
import traceback
import time
import ntpath
import warnings


warnings.simplefilter(action='ignore', category=pd.errors.PerformanceWarning)

devqueue

def recognize_plate(img, coords):
    plate_num = ""
    # separate plate from image
    xmin, ymin, xmax, ymax = coords
    box = img[int(ymin)-5:int(ymax)+5, int(xmin)-5:int(xmax)+5]

    # grayscale and rezise
    grayimg = cv2.cvtColor(box, cv2.COLOR_RGB2GRAY)
    grayimg = cv2.resize(grayimg, None, fx = 3, fy = 3, interpolation = cv2.INTER_CUBIC)
    # threshold the image using Otsus method to preprocess for tesseract

devqueue

import cv2
import mediapipe as mp
import numpy as np 
import math


mphands = mp.solutions.hands
hands = mphands.Hands()
mpDraw = mp.solutions.drawing_utils
lmlist = []

devqueue

{
    "image": "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

devqueue

void getRecognisedText() async {
    // final inputImage = InputImage.fromFilePath(image.path);
    String getPrettyJSONString(jsonObject) {
      var encoder = new JsonEncoder.withIndent("     ");
      return encoder.convert(jsonObject);
    }

    textScanning = true;
    setState(() {});
    const url = 'https://ocrapi-5l4bm6okaa-uc.a.run.app/api';

devqueue

import cv2
import mediapipe as mp
import numpy as np 
import math

mphands = mp.solutions.hands
hands = mphands.Hands()
mpDraw = mp.solutions.drawing_utils
lmlist = []

devqueue

import cv2
import time

def Facedetect():
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_alt2.xml')

    cap = cv2.VideoCapture(0)

    while(True):
        # Capture frame-by-frame