vabarbosa

// tensorflow.js 0.x.x
const MODEL_URL = '/model/tensorflowjs_model.pb'
const WEIGHTS_URL = '/model/weights_manifest.json'

// https://js.tensorflow.org/api/0.15.1/#loadFrozenModel
const model = await tf.loadFrozenModel(MODEL_URL, WEIGHTS_URL)

vabarbosa

tensorflowjs_converter \
    --input_format=tf_frozen_model \
    --output_node_names='SemanticPredictions' \
    /path/to/frozen_inference_graph.pb \
    /path/to/output_dir

vabarbosa

pip install tensorflowjs

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@PixiedustDisplay()
class SimpleWordCloudMeta(DisplayHandlerMeta):
    @addId
    def getMenuInfo(self,entity,dataHandler):
        if entity.__class__.__name__ == "DataFrame":
            return [
                {
                    "categoryId": "Chart",
                    "title": "Simple Word Cloud",
                    "icon": "fa-cloud",

vabarbosa

from pixiedust.display.display import *
from wordcloud import WordCloud
import cStringIO
import base64

class SimpleWordCloudDisplay(Display):
    def doRender(self, handlerId):
        # convert from dataframe to dict
        dfdict = {}
        df = self.entity.toPandas()

vabarbosa

@PixiedustDisplay()
class TestPluginMeta(DisplayHandlerMeta):
    @addId
    def getMenuInfo(self,entity,dataHandler):
        if entity.__class__.__name__ == "DataFrame":
            return [
                {
                    "categoryId": "Table",
                    "title": "My Simple Table",
                    "icon": "fa-table",

vabarbosa

from pixiedust.display.display import *

class TestDisplay(Display):
    def doRender(self, handlerId):
        self._addHTMLTemplateString(
"""
<div>My Simple Table</div>
<table class="table table-striped">
  <thead>                
    {%for field in entity.schema.fields%}