try:
    import IPython
except:
    %pip install IPython
    import IPython 
from IPython.display import display, IFrame, HTML, Javascript
HTML("""<link rel="stylesheet" type="text/css" href="src/css/notebook.css"/>""")

try:
    import pandas as pd
except:
    %pip install pandas
    import pandas as pd
    
fileNGA = 'data/nga/input/nga_ruskin.csv'

dataFrameNGA = pd.read_csv(fileNGA)
dataFrameNGA.head()

fileNGAimages = "https://raw.githubusercontent.com/NationalGalleryOfArt/opendata/main/data/published_images.csv"
dataFrameNGAImages = pd.read_csv(fileNGAimages)
dataFrameNGAImages.head()

# remove BOM

removeBOM = open(fileNGA, mode='r', encoding='utf-8-sig').read()
open(fileNGA, mode='w', encoding='utf-8').write(removeBOM)


# data mapping between Linked Art data model and NGA data entities
mapp =  {
    "id":"objectid",
  "accession_number":"accessionnum",
    "accession_date": "",
    "classification" : "classification",
    "title": "title",
    "alt_title": "",
    "notes": "",
    "date_created":"displaydate",
    "date_created_earliest": "beginyear",
    "date_created_latest": "endyear",
    "created_period":"",
    "created_dynasty":"",
    "created_inscriptions":"",
    "created_notes": "",
    "created_provenance" : "",
    "creator":"attribution",
    "physical_medium": "medium",
    "physical_style": "",
    "physical_technique": "",
    "physical_description": "",
    "physical_dimensions": "dimensions",
    "created_provenance": "" ,
    "credit_line": "creditline",
    "collection" : "departmentabbr",
    "current_status" : "",
    "current_owner" : "",
    "homepage": ""
}

# display transposed dataframe of data mapping
display(pd.DataFrame(mapp, index=[0]).T)

try:
    import cromulent 
except:
    %pip install cromulent
    import cromulent
    
from cromulent.model import factory
from lib import linkedart as la

import csv

#  baseURI for JSON-LD document
baseURI = "https://www.nga.gov/collection/"
fileNGA = 'data/nga/input/nga_ruskin.csv'

# list to hold file names for use with jsonld visualisation dropdown
selectOptions = []
selectOptions = [('Please select an artwork', '')]

def createObjProp(obj,mapp):
    objProp = {}
    csv_keys = list(obj.keys())
    for key in csv_keys:
        for prop in mapp:
            if key == mapp[prop]:
                
                # custom code to populate the creator property
                if prop == "creator":
                    objProp[prop] = [{"id": baseURI +"creatorid/" + obj[mapp["id"]] ,"name": obj[key],"role":"Artist"}]
                else:
                    objProp[prop] = obj[key]
    
    # custom code to populate the image_url property
    matchImages = dataFrameNGAImages.query('depictstmsobjectid == ' + objProp["id"] )
    objProp["image_url"] = matchImages["iiifurl"].iloc[0]  + "/full/!500,500/0/default.jpg"
   
    # custom code to define the homepage properity
    objProp["homepage"] = "https://www.nga.gov/collection/art-object-page." + objProp["id"] + ".html"
        
    return objProp   

    
allObjects = csv.DictReader(open(fileNGA, mode='r',encoding='utf-8'))

for obj in allObjects:
    # create object property dictionary
    objProp = createObjProp(obj,mapp)
    
    id = objProp["id"]
    filename = id + ".json"

    # create obj description
    objLA = la.createObjDesc(objProp,la.objTypes,baseURI + id)

    # write to file 
   
    text_file = open(outputdir + filename, "wt")
    n = text_file.write(factory.toString(objLA, compact=False))
    text_file.close()
    
    selectOptions.append( ( objProp["title"] + " (" + filename + ")" , filename))
    
    # display title and table for illustration
    display(objProp["title"])
    display(pd.DataFrame(objProp, index=[0]))
    

try:
    import ipywidgets
except:
    %pip install ipywidgets
    import ipywidgets

from ipywidgets import Layout, FileUpload 
from IPython.display import display, IFrame, HTML, Image

import os

try:
    import json
except:
    %pip install json
    import json 
    
from IPython.core.display import Javascript        
    
def dropdown_eventhandler(change):
    with open('./src/js/visld.js', 'r') as _jscript:
        code = _jscript.read() + "var file = '" + outputdir + change.new + "';var selector = '#visnga';visjsonld(file, selector); "
        display(Javascript(code))
    
        with open( outputdir + "/" + change.new) as json_file:
    
                artwork = json.load(json_file)
                if ("representation" in artwork):
                    image = artwork["representation"][0]["id"]
                    display(Javascript("document.getElementById('artworknga').src = '" + image   +   "';"))
                else:
                    display(Javascript("document.getElementById('artworknga').src = '';"))
                    

selectObject = ipywidgets.Dropdown(options=selectOptions)
selectObject.observe(dropdown_eventhandler, names='value')

display(selectObject)

from IPython.display import YouTubeVideo, HTML

display(HTML("<h4>Video - Using  OpenRefine to extract Ruskin record from CMA data</h4>"))

YouTubeVideo('npVMbybZ3p8', width=1024, height=576)

#  baseURI for JSON-LD document
baseURI = "https://clevelandart.org/art/"
file = './data/cma/input/ruskin.csv'
#mpg = pd.read_csv(file)
#mpg.head()

display(pd.DataFrame(pd.read_csv(file), index=[0]).T)

try:
    from PIL import Image
except:
    %pip install Pillow
    from PIL import Image
    
import requests

url = "https://openaccess-cdn.clevelandart.org/1989.14/1989.14_web.jpg"
image = Image.open(requests.get(url, stream=True).raw)

print("Budding Sycamore")
image