enable tfdv, remove hardcode as a sample for following PRs

backend/src/apiserver/visualization/requirements.txt

@@ -4,11 +4,14 @@ gcsfs==0.2.3
 google-api-python-client==1.7.9
 itables==0.1.0
 ipykernel==5.1.1
+ipython==7.12.0
 jupyter_client==5.2.4
 nbconvert==5.5.0
 nbformat==4.4.0
 pandas==0.24.2
+pyarrow==0.15.1
 scikit_learn==0.21.2
+tensorflow-metadata==0.21.1
 tensorflow-model-analysis==0.21.1
 tensorflow-data-validation==0.21.1
 tornado==6.0.2

backend/src/apiserver/visualization/types/tfdv.py

@@ -12,14 +12,81 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import base64
 import tensorflow_data_validation as tfdv
+from IPython.display import display
+from IPython.display import HTML
+from tensorflow_metadata.proto.v0 import statistics_pb2
+from typing import Text
 
 # The following variables are provided through dependency injection. These
 # variables come from the specified input path and arguments provided by the
 # API post request.
 #
 # source
 
-train_stats = tfdv.generate_statistics_from_csv(data_location=source)
+# train_stats = tfdv.generate_statistics_from_csv(data_location=source)
+# tfdv.visualize_statistics(train_stats) 
 
-tfdv.visualize_statistics(train_stats)
+def get_statistics_html(
+    lhs_statistics: statistics_pb2.DatasetFeatureStatisticsList
+) -> Text:
+  """Build the HTML for visualizing the input statistics using Facets.
+  Args:
+    lhs_statistics: A DatasetFeatureStatisticsList protocol buffer.
+  Returns:
+    HTML to be embedded for visualization.
+  Raises:
+    TypeError: If the input argument is not of the expected type.
+    ValueError: If the input statistics protos does not have only one dataset.
+  """
+
+  rhs_statistics = None
+  lhs_name = 'lhs_statistics'
+  rhs_name = 'rhs_statistics'
+
+  if not isinstance(lhs_statistics,
+                    statistics_pb2.DatasetFeatureStatisticsList):
+    raise TypeError(
+        'lhs_statistics is of type %s, should be '
+        'a DatasetFeatureStatisticsList proto.' % type(lhs_statistics).__name__)
+
+  if len(lhs_statistics.datasets) != 1:
+    raise ValueError('lhs_statistics proto contains multiple datasets. Only '
+                     'one dataset is currently supported.')
+
+  if lhs_statistics.datasets[0].name:
+    lhs_name = lhs_statistics.datasets[0].name
+
+  # Add lhs stats.
+  combined_statistics = statistics_pb2.DatasetFeatureStatisticsList()
+  lhs_stats_copy = combined_statistics.datasets.add()
+  lhs_stats_copy.MergeFrom(lhs_statistics.datasets[0])
+  lhs_stats_copy.name = lhs_name
+
+  protostr = base64.b64encode(
+      combined_statistics.SerializeToString()).decode('utf-8')
+
+  # pylint: disable=line-too-long
+  # Note that in the html template we currently assign a temporary id to the
+  # facets element and then remove it once we have appended the serialized proto
+  # string to the element. We do this to avoid any collision of ids when
+  # displaying multiple facets output in the notebook.
+  html_template = """<iframe id='facets-iframe' width="100%" height="500px"></iframe>
+        <script>
+        facets_iframe = document.getElementById('facets-iframe');
+        facets_html = '<script src="https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js"><\/script><link rel="import" href="https://raw.githubusercontent.com/PAIR-code/facets/master/facets-dist/facets-jupyter.html"><facets-overview proto-input="protostr"></facets-overview>';
+        facets_iframe.srcdoc = facets_html;
+         facets_iframe.id = "";
+         setTimeout(() => {
+           facets_iframe.setAttribute('height', facets_iframe.contentWindow.document.body.offsetHeight + 'px')
+         }, 1500)
+         </script>"""
+  # pylint: enable=line-too-long
+  html = html_template.replace('protostr', protostr)
+
+  return html
+
+stats = tfdv.load_statistics(source)
+html = get_statistics_html(stats)
+display(HTML(html))

frontend/src/lib/OutputArtifactLoader.ts

@@ -14,36 +14,36 @@
  * limitations under the License.
  */
 
-import WorkflowParser, { StoragePath } from './WorkflowParser';
-import { Apis } from '../lib/Apis';
-import { ConfusionMatrixConfig } from '../components/viewers/ConfusionMatrix';
-import { HTMLViewerConfig } from '../components/viewers/HTMLViewer';
-import { MarkdownViewerConfig } from '../components/viewers/MarkdownViewer';
-import { PagedTableConfig } from '../components/viewers/PagedTable';
-import { PlotType, ViewerConfig } from '../components/viewers/Viewer';
-import { ROCCurveConfig } from '../components/viewers/ROCCurve';
-import { TensorboardViewerConfig } from '../components/viewers/Tensorboard';
-import { csvParseRows } from 'd3-dsv';
-import { logger, errorToMessage } from './Utils';
-import { ApiVisualization, ApiVisualizationType } from '../apis/visualization';
 import {
   Api,
   Artifact,
   ArtifactType,
   Context,
   Event,
   Execution,
-  GetArtifactTypesRequest,
-  GetArtifactTypesResponse,
   GetArtifactsByIDRequest,
   GetArtifactsByIDResponse,
+  GetArtifactTypesRequest,
+  GetArtifactTypesResponse,
   GetContextByTypeAndNameRequest,
   GetContextByTypeAndNameResponse,
-  GetExecutionsByContextRequest,
-  GetExecutionsByContextResponse,
   GetEventsByExecutionIDsRequest,
   GetEventsByExecutionIDsResponse,
+  GetExecutionsByContextRequest,
+  GetExecutionsByContextResponse,
 } from '@kubeflow/frontend';
+import { csvParseRows } from 'd3-dsv';
+import { ApiVisualization, ApiVisualizationType } from '../apis/visualization';
+import { ConfusionMatrixConfig } from '../components/viewers/ConfusionMatrix';
+import { HTMLViewerConfig } from '../components/viewers/HTMLViewer';
+import { MarkdownViewerConfig } from '../components/viewers/MarkdownViewer';
+import { PagedTableConfig } from '../components/viewers/PagedTable';
+import { ROCCurveConfig } from '../components/viewers/ROCCurve';
+import { TensorboardViewerConfig } from '../components/viewers/Tensorboard';
+import { PlotType, ViewerConfig } from '../components/viewers/Viewer';
+import { Apis } from '../lib/Apis';
+import { errorToMessage, logger } from './Utils';
+import WorkflowParser, { StoragePath } from './WorkflowParser';
 
 export interface PlotMetadata {
   format?: 'csv';
@@ -279,12 +279,7 @@ export class OutputArtifactLoader {
       const trainUri = uri + '/train/stats_tfrecord';
       viewers = viewers.concat(
         [evalUri, trainUri].map(async specificUri => {
-          const script = [
-            'import tensorflow_data_validation as tfdv',
-            `stats = tfdv.load_statistics('${specificUri}')`,
-            'tfdv.visualize_statistics(stats)',
-          ];
-          return buildArtifactViewer(script);
+          return buildArtifactViewerTfdvStatistics(specificUri);
         }),
       );
     });
@@ -537,6 +532,21 @@ async function buildArtifactViewer(script: string[]): Promise<HTMLViewerConfig>
   };
 }
 
+async function buildArtifactViewerTfdvStatistics(url: string): Promise<HTMLViewerConfig> {
+  const visualizationData: ApiVisualization = {
+    source: url,
+    type: ApiVisualizationType.TFDV,
+  };
+  const visualization = await Apis.buildPythonVisualizationConfig(visualizationData);
+  if (!visualization.htmlContent) {
+    throw new Error('Failed to build artifact viewer, no value in visualization.htmlContent');
+  }
+  return {
+    htmlContent: visualization.htmlContent,
+    type: PlotType.WEB_APP,
+  };
+}
+
 // TODO: add tfma back
 // function filterTfmaArtifactsPaths(
 //   artifactTypes: ArtifactType[],