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import 'package:flutter/material.dart';
import 'package:widgets/model/article.dart';

class ArticlePage extends StatelessWidget {
  final Article? article;

  const ArticlePage({Key? key, this.article}) : super(key: key);

  @override
  Widget build(BuildContext context) {

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import tensorflow as tf
import tensorflow_addons as tfa
import tensorflow_hub as hub
import tensorflow_text as text
import pandas as pd

## Dataset is obtained from https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset
df=pd.read_csv('spam.csv', encoding_errors='ignore')
df.drop(df.columns[2:5], axis=1, inplace=True)
df.rename(columns = {'v1':'Category', 'v2':'Message'}, inplace = True)

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from transformers import BertForSequenceClassification, BertTokenizer

## Step 1 - Pre-processing
MODEL = 'YOUR MODEL'
TEXT = ("YOUR INPUTS")
tokenizer = BertTokenizer.from_pretrained(MODEL)
tokens = tokenizer.encode_plus(TEXT, max_length=512, # max number of tokens in each sample
                               truncation=True, 	 # what to do with extra token over max_length
                               padding='max_length', # for shorter sequences, pad with 0's
                               add_special_tokens=True, # add special tokens by default

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const express = require('express')
const bodyParser = require('body-parser')
const zlib = require('zlib')
const jose = require('node-jose');
const fs = require('fs');
const qrcode = require('qrcode')
const nodemailer = require('nodemailer');

let signingKey;
let keystore = jose.JWK.asKeyStore(JSON.parse(fs.readFileSync('keys.json'))).then(function (result) {

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package ca.onfhir.interceptor;

import ca.uhn.fhir.context.FhirContext;
import ca.uhn.fhir.interceptor.api.Hook;
import ca.uhn.fhir.interceptor.api.Interceptor;
import ca.uhn.fhir.interceptor.api.Pointcut;
import ca.uhn.fhir.jpa.api.dao.DaoRegistry;
import ca.uhn.fhir.jpa.api.dao.IFhirResourceDao;
import ca.uhn.fhir.jpa.searchparam.SearchParameterMap;
import ca.uhn.fhir.rest.api.server.IBundleProvider;

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{
  "resourceType": "ImagingStudy",
  "text": {
    "status": "generated",
    "div": "<div xmlns='http://www.w3.org/1999/xhtml'>Chest Frontal View X-Ray. John Smith (MRN: 109236). Accession: W12342398. Performed: 2017-01-01. 1 series, 1 image.</div>" },
  "identifier": [ {
      "use": "official",
      "system": "urn:dicom:uid",
      "value": "urn:oid:2.16.124.113543.6003.1154777499.30246.19789.3503430046" } ],
    

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{
  "predictions": [
    { "label": "Atelectasis", "probability": 0.065 },
    { "label": "Cardiomegaly", "probability": 0.009 },
    { "label": "Effusion", "probability": 0.43 },
    { "label": "Infiltration", "probability": 0.397 },
    { "label": "Mass", "probability": 0.0652 },
    { "label": "Nodule", "probability": 0.052 },
    { "label": "Pneumonia", "probability": 0.049 },
    { "label": "Pneumothorax", "probability": 0.037 },

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import io

import flask
import keras.applications
import numpy as np
from PIL import Image
from keras.layers import Input
from keras.layers.core import Dense
from keras.models import Model
from skimage.transform import resize

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{
  "resourceType": "ImagingStudy",
  "text": {
    "status": "generated",
    "div": "<div xmlns='http://www.w3.org/1999/xhtml'>Chest Frontal View X-Ray. John Smith (MRN: 109236). Accession: W12342398. Performed: 2017-01-01. 1 series, 1 image.</div>" },
  "identifier": [ {
      "use": "official",
      "system": "urn:dicom:uid",
      "value": "urn:oid:2.16.124.113543.6003.1154777499.30246.19789.3503430046" } ],
  "status": "available",

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package ca.onfhir.interceptor;

import ca.uhn.fhir.context.FhirContext;
import ca.uhn.fhir.fhirpath.IFhirPath;
import ca.uhn.fhir.interceptor.api.Hook;
import ca.uhn.fhir.interceptor.api.Interceptor;
import ca.uhn.fhir.interceptor.api.Pointcut;
import ca.uhn.fhir.jpa.api.dao.DaoRegistry;
import ca.uhn.fhir.jpa.api.dao.IFhirResourceDao;
import ca.uhn.fhir.rest.api.server.RequestDetails;