From 022452bcff7fd606a2be6fa0254392dc003070ab Mon Sep 17 00:00:00 2001
From: Caroline DE POURTALES <cdepourt@montana.irit.fr>
Date: Fri, 25 Mar 2022 15:20:03 +0100
Subject: [PATCH] added dataset upload and now works with pkl file, however
wrong form on inftance
---
callbacks.py | 17 ++-
.../DecisionTree/DecisionTreeComponent.py | 22 ++-
pages/application/DecisionTree/utils/data.py | 102 +++++++++++++
pages/application/DecisionTree/utils/dtree.py | 28 ++--
pages/application/DecisionTree/utils/dtviz.py | 11 +-
.../DecisionTree/utils/upload_tree.py | 141 ++++++++++++++++--
pages/application/application.py | 21 ++-
utils.py | 13 ++
8 files changed, 308 insertions(+), 47 deletions(-)
create mode 100644 pages/application/DecisionTree/utils/data.py
diff --git a/callbacks.py b/callbacks.py
index 95101df..0427370 100644
--- a/callbacks.py
+++ b/callbacks.py
@@ -4,7 +4,7 @@ from dash import Input, Output, State
from dash.dependencies import Input, Output, State
from dash.exceptions import PreventUpdate
-from utils import parse_contents_graph, parse_contents_instance
+from utils import parse_contents_graph, parse_contents_instance, parse_contents_data
def register_callbacks(page_home, page_course, page_application, app):
@@ -37,6 +37,8 @@ def register_callbacks(page_home, page_course, page_application, app):
Input('ml_model_choice', 'value'),
Input('ml_pretrained_model_choice', 'contents'),
State('ml_pretrained_model_choice', 'filename'),
+ Input('model_dataset_choice', 'contents'),
+ State('model_dataset_choice', 'filename'),
Input('ml_instance_choice', 'contents'),
State('ml_instance_choice', 'filename'),
Input('number_explanations', 'value'),
@@ -45,7 +47,7 @@ def register_callbacks(page_home, page_course, page_application, app):
Input('expl_choice', 'value'),
prevent_initial_call=True
)
- def update_ml_type(value_ml_model, pretrained_model_contents, pretrained_model_filename, instance_contents, instance_filename, enum, xtype, solver, expl_choice):
+ def update_ml_type(value_ml_model, pretrained_model_contents, pretrained_model_filename, model_dataset, model_dataset_filename, instance_contents, instance_filename, enum, xtype, solver, expl_choice):
ctx = dash.callback_context
if ctx.triggered:
ihm_id = ctx.triggered[0]['prop_id'].split('.')[0]
@@ -57,8 +59,15 @@ def register_callbacks(page_home, page_course, page_application, app):
elif ihm_id == 'ml_pretrained_model_choice':
if model_application.ml_model is None :
raise PreventUpdate
- tree = parse_contents_graph(pretrained_model_contents, pretrained_model_filename)
- model_application.update_pretrained_model(tree, pretrained_model_filename)
+ graph = parse_contents_graph(pretrained_model_contents, pretrained_model_filename)
+ model_application.update_pretrained_model(graph)
+ return pretrained_model_filename, None, None, None
+
+ elif ihm_id == 'model_dataset_choice':
+ if model_application.ml_model is None :
+ raise PreventUpdate
+ model_dataset = parse_contents_data(model_dataset, model_dataset_filename)
+ model_application.update_pretrained_model_dataset(model_dataset)
return pretrained_model_filename, None, model_application.component.network, None
elif ihm_id == 'ml_instance_choice' :
diff --git a/pages/application/DecisionTree/DecisionTreeComponent.py b/pages/application/DecisionTree/DecisionTreeComponent.py
index 8c795d7..020a956 100644
--- a/pages/application/DecisionTree/DecisionTreeComponent.py
+++ b/pages/application/DecisionTree/DecisionTreeComponent.py
@@ -6,6 +6,7 @@ import dash_interactive_graphviz
import numpy as np
from dash import dcc, html
from pages.application.DecisionTree.utils.upload_tree import UploadedDecisionTree
+from pages.application.DecisionTree.utils.data import Data
from pages.application.DecisionTree.utils.dtree import DecisionTree
from pages.application.DecisionTree.utils.dtviz import (visualize,
@@ -14,18 +15,20 @@ from pages.application.DecisionTree.utils.dtviz import (visualize,
class DecisionTreeComponent():
- def __init__(self, tree, filename_tree):
+ def __init__(self, tree, dataset):
+ data = Data(dataset)
+ fvmap = data.mapping_features()
+
try:
feature_names = tree.feature_names_in_
except:
print("You did not dump the model with the features names")
feature_names = [f'f{i}' for i in range(tree.n_features_in_)]
- self.uploaded_dt = UploadedDecisionTree(tree, 'SKL', filename_tree, maxdepth=tree.get_depth(), feature_names=feature_names, nb_classes=tree.n_classes_)
- #need a function that takes as input UploadedDecisionTree and gives DecisionTree
- self.dt_format, self.map, features_names_mapping = self.uploaded_dt.convert_dt(feat_names=feature_names)
- self.dt = DecisionTree(from_dt=self.dt_format, mapfile = self.map, mapping_features=features_names_mapping)
+ self.uploaded_dt = UploadedDecisionTree(tree, 'SKL', maxdepth=tree.get_depth(), feature_names=feature_names, nb_classes=tree.n_classes_)
+ self.dt_format, self.map, features_names_mapping = self.uploaded_dt.dump(fvmap, feat_names=feature_names)
+ self.dt = DecisionTree(from_dt=self.dt_format, mapfile = self.map)
dot_source = visualize(self.dt)
self.network = [dbc.Row(dash_interactive_graphviz.DashInteractiveGraphviz(dot_source=dot_source, style = {"width": "60%",
@@ -34,6 +37,11 @@ class DecisionTreeComponent():
self.explanation = []
def update_with_explicability(self, instance, enum, xtype, solver) :
+
+ self.explanation = []
+ list_explanations_path=[]
+ explanation = self.dt.explain(instance, enum=enum, xtype = xtype, solver=solver)
+
dot_source = visualize_instance(self.dt, instance)
self.network = [dbc.Row(dash_interactive_graphviz.DashInteractiveGraphviz(
dot_source=dot_source, style = {"width": "50%",
@@ -41,9 +49,6 @@ class DecisionTreeComponent():
"background-color": "transparent"}
))]
- self.explanation = []
- list_explanations_path=[]
- explanation = self.dt.explain(instance, enum=enum, xtype = xtype, solver=solver)
#Creating a clean and nice text component
for k in explanation.keys() :
@@ -62,6 +67,7 @@ class DecisionTreeComponent():
return list_explanations_path
def draw_explanation(self, instance, expl) :
+ print(expl)
dot_source = visualize_expl(self.dt, instance, expl)
self.network = [dbc.Row(dash_interactive_graphviz.DashInteractiveGraphviz(
dot_source=dot_source,
diff --git a/pages/application/DecisionTree/utils/data.py b/pages/application/DecisionTree/utils/data.py
new file mode 100644
index 0000000..e719b59
--- /dev/null
+++ b/pages/application/DecisionTree/utils/data.py
@@ -0,0 +1,102 @@
+#!/usr/bin/env python
+#-*- coding:utf-8 -*-
+##
+## data.py
+##
+## Created on: Sep 20, 2017
+## Author: Alexey Ignatiev, Nina Narodytska
+## E-mail: aignatiev@ciencias.ulisboa.pt, narodytska@vmware.com
+##
+
+#
+#==============================================================================
+from __future__ import print_function
+import collections
+import itertools
+import os, pickle
+import six
+import gzip
+from six.moves import range
+import numpy as np
+import pandas as pd
+
+#from sklearn.preprocessing import OneHotEncoder
+from sklearn.model_selection import train_test_split
+
+
+#
+#==============================================================================
+class Data(object):
+ """
+ Class for representing data (transactions).
+ """
+
+ def __init__(self, data, separator=','):
+ """
+ Constructor and parser.
+ """
+ self.names = None
+ self.nm2id = None
+ self.feats = None
+ self.targets = None
+ self.samples = None
+
+ self.parse(data, separator)
+
+ def parse(self, data, separator):
+ """
+ Parse input file.
+ """
+
+ # reading data set from file
+ lines = data.split('\n')
+
+ # reading preamble
+ self.names = [name.replace('"','').strip() for name in lines[0].strip().split(separator)]
+ self.feats = [set([]) for n in self.names[:-1]]
+ self.targets = set([])
+
+ lines = lines[1:]
+
+ # filling name to id mapping
+ self.nm2id = {name: i for i, name in enumerate(self.names)}
+
+ self.nonbin2bin = {}
+ for name in self.nm2id:
+ spl = name.rsplit(':',1)
+ if (spl[0] not in self.nonbin2bin):
+ self.nonbin2bin[spl[0]] = [name]
+ else:
+ self.nonbin2bin[spl[0]].append(name)
+
+ # reading training samples
+ self.samples = []
+
+ for line, w in six.iteritems(collections.Counter(lines)):
+ inst = [v.strip() for v in line.strip().split(separator)]
+ self.samples.append(inst)
+ for i, v in enumerate(inst[:-1]):
+ if v:
+ self.feats[i].add(str(v))
+ assert(inst[-1])
+ self.targets.add(str(inst[-1]))
+
+ self.nof_feats = len(self.names[:-1])
+
+ def mapping_features(self):
+ """
+ feature-value mapping
+ """
+ fvmap = {}
+
+ for i in range(self.nof_feats):
+ fvmap[f'f{i}'] = dict()
+ for j, v in enumerate(sorted(self.feats[i])):
+ fvmap[f'f{i}'][j] = (self.names[i], True, v)
+
+ if len(self.feats[i]) > 2:
+ m = len(self.feats[i])
+ for j, v in enumerate(sorted(self.feats[i])):
+ fvmap[f'f{i}'][j+m] = (self.names[i], False, v)
+
+ return fvmap
\ No newline at end of file
diff --git a/pages/application/DecisionTree/utils/dtree.py b/pages/application/DecisionTree/utils/dtree.py
index 837decf..bbd6046 100644
--- a/pages/application/DecisionTree/utils/dtree.py
+++ b/pages/application/DecisionTree/utils/dtree.py
@@ -45,7 +45,7 @@ class DecisionTree():
Simple decision tree class.
"""
- def __init__(self, from_dt=None, mapfile=None, mapping_features=None, verbose=0):
+ def __init__(self, from_dt=None, mapfile=None, verbose=0):
"""
Constructor.
"""
@@ -63,8 +63,6 @@ class DecisionTree():
self.fdoms = {}
self.fvmap = {}
- self.features_names = mapping_features
-
# OHE mapping
OHEMap = collections.namedtuple('OHEMap', ['dir', 'opp'])
self.ohmap = OHEMap(dir={}, opp={})
@@ -79,7 +77,6 @@ class DecisionTree():
for v in self.fdoms[f]:
self.fvmap[tuple([f, v])] = '{0}={1}'.format(f, v)
-
def from_dt(self, data):
"""
Get the tree from a file pointer.
@@ -134,7 +131,7 @@ class DecisionTree():
# simplifying the features and their domains
self.feats = sorted(self.feats)
- self.feids = {f: i for i, f in enumerate(self.feats)}
+ #self.feids = {f: i for i, f in enumerate(self.feats)}
self.fdoms = {f: sorted(self.fdoms[f]) for f in self.fdoms}
# here we assume all features are present in the tree
@@ -171,9 +168,13 @@ class DecisionTree():
# skipping the first comment line if necessary
lines = lines[1:]
+ # number of features
+ self.nof_feats = int(lines[0].strip())
+ self.feids = {}
+
for line in lines[1:]:
feat, val, real = line.split()
- self.fvmap[tuple([feat, int(val)])] = '{0}{1}'.format(self.features_names[feat], real)
+ self.fvmap[tuple([feat, int(val)])] = '{0}{1}'.format(feat, real)
#if feat not in self.feids:
# self.feids[feat] = len(self.feids)
@@ -331,19 +332,18 @@ class DecisionTree():
"""
Compute a given number of explanations.
"""
+
+ self.feids = {f[0]: i for i, f in enumerate(inst)}
- inst_dic = {}
- for i in range(len(inst)):
- inst_dic[inst[i][0]] = np.float32(inst[i][1])
path, term, depth = self.execute(inst, pathlits)
#contaiins all the elements for explanation
explanation_dic = {}
#instance plotting
- explanation_dic["Instance : "] = str(inst_dic)
+ explanation_dic["Instance : "] = str([self.fvmap[inst[i]] for i in range (len(inst))])
#decision path
- decision_path_str = 'IF {0} THEN class={1}'.format(' AND '.join([str(inst[self.feids[self.nodes[n].feat]]) for n in path]), term)
+ decision_path_str = 'IF {0} THEN class={1}'.format(' AND '.join([self.fvmap[inst[self.feids[self.nodes[n].feat]]] for n in path]), term)
explanation_dic["Decision path of instance : "] = decision_path_str
explanation_dic["Decision path length : "] = 'Path length is :'+ str(depth)
@@ -375,8 +375,8 @@ class DecisionTree():
with Hitman(bootstrap_with=to_hit, solver='m22', htype=htype) as hitman:
expls = []
for i, expl in enumerate(hitman.enumerate(), 1):
- list_expls.append([ str(p[0]) + "=" + str(p[1]) for p in expl])
- list_expls_str.append('Explanation: IF {0} THEN class={1}'.format(' AND '.join([str(p) for p in sorted(expl, key=lambda p: p[0])]), term))
+ list_expls.append([self.fvmap[p] for p in sorted(expl, key=lambda p: p[0])])
+ list_expls_str.append('Explanation: IF {0} THEN class={1}'.format(' AND '.join([self.fvmap[p] for p in sorted(expl, key=lambda p: p[0])]), term))
expls.append(expl)
if i == enum:
@@ -409,7 +409,7 @@ class DecisionTree():
list_expls_str = []
explanation = {}
for expl in expls:
- list_expls_str.append('Contrastive: IF {0} THEN class!={1}'.format(' OR '.join(['!{0}'.format(str(p)) for p in sorted(expl, key=lambda p: p[0])]), term))
+ list_expls_str.append('Contrastive: IF {0} THEN class!={1}'.format(' OR '.join(['!{0}'.format(self.fvmap[p]) for p in sorted(expl, key=lambda p: p[0])]), term))
explanation["List of contrastive explanation(s)"] = list_expls_str
explanation["Number of contrastive explanation(s) : "]=str(len(expls))
diff --git a/pages/application/DecisionTree/utils/dtviz.py b/pages/application/DecisionTree/utils/dtviz.py
index 681ccca..0c940c6 100755
--- a/pages/application/DecisionTree/utils/dtviz.py
+++ b/pages/application/DecisionTree/utils/dtviz.py
@@ -50,7 +50,7 @@ def visualize(dt):
# non-terminal nodes
for n in dt.nodes:
- g.add_node(n, label=dt.features_names[dt.nodes[n].feat])
+ g.add_node(n, label=dt.nodes[n].feat)
node = g.get_node(n)
node.attr['shape'] = 'circle'
node.attr['fontsize'] = 13
@@ -98,7 +98,7 @@ def visualize_instance(dt, instance):
# non-terminal nodes
for n in dt.nodes:
- g.add_node(n, label=dt.features_names[dt.nodes[n].feat])
+ g.add_node(n, label=dt.nodes[n].feat)
node = g.get_node(n)
node.attr['shape'] = 'circle'
node.attr['fontsize'] = 13
@@ -156,7 +156,7 @@ def visualize_expl(dt, instance, expl):
# non-terminal nodes
for n in dt.nodes:
- g.add_node(n, label=dt.features_names[dt.nodes[n].feat])
+ g.add_node(n, label=dt.nodes[n].feat)
node = g.get_node(n)
node.attr['shape'] = 'circle'
node.attr['fontsize'] = 13
@@ -183,8 +183,9 @@ def visualize_expl(dt, instance, expl):
#instance path in dashed
if ((n1,n2) in edges_instance) or (n2_type=='square' and (n1, "term:"+ dt.terms[n2]) in edges_instance):
edge.attr['style'] = 'dashed'
- if edge.attr['label'] in expl:
- edge.attr['color'] = 'blue'
+ for label in edge.attr['label'].split('\n'):
+ if label in expl:
+ edge.attr['color'] = 'blue'
edge.attr['fontsize'] = 10
edge.attr['arrowsize'] = 0.8
diff --git a/pages/application/DecisionTree/utils/upload_tree.py b/pages/application/DecisionTree/utils/upload_tree.py
index e2babad..ea82f0a 100644
--- a/pages/application/DecisionTree/utils/upload_tree.py
+++ b/pages/application/DecisionTree/utils/upload_tree.py
@@ -82,7 +82,7 @@ def scores_tree(node, sample):
#
#==============================================================================
-def get_json_tree(model, tool, maxdepth=None, fname=None):
+def get_json_tree(model, tool, maxdepth=None):
"""
returns the dtree in JSON format
"""
@@ -116,19 +116,136 @@ class UploadedDecisionTree:
""" A decision tree.
This object provides a common interface to many different types of models.
"""
- def __init__(self, model, tool, fname, maxdepth, feature_names=None, nb_classes = 0):
+ def __init__(self, model, tool, maxdepth, feature_names=None, nb_classes = 0):
self.tool = tool
self.model = model
self.tree = None
self.depth = None
self.n_nodes = None
- json_tree = get_json_tree(self.model, self.tool, maxdepth, fname)
+ json_tree = get_json_tree(self.model, self.tool, maxdepth)
self.tree, self.n_nodes, self.depth = self.build_tree(json_tree, feature_names)
def print_tree(self):
print("DT model:")
walk_tree(self.tree)
+
+
+ def dump(self, fvmap, filename=None, maxdepth=None, feat_names=None):
+ """
+ save the dtree and data map in .dt/.map file
+ """
+
+ def walk_tree(node, domains, internal, terminal):
+ """
+ extract internal (non-term) & terminal nodes
+ """
+ if (len(node.children) == 0): # leaf node
+ terminal.append((node.id, node.values))
+ else:
+ assert (node.children[0].id == node.left_node_id)
+ assert (node.children[1].id == node.right_node_id)
+
+ f = f"f{node.feature}"
+
+ if self.tool == "DL85":
+ l,r = (1,0)
+ internal.append((node.id, f, l, node.children[0].id))
+ internal.append((node.id, f, r, node.children[1].id))
+
+ elif self.tool == "ITI":
+ #l,r = (0,1)
+ if len(fvmap[f]) > 2:
+ n = 0
+ for v in fvmap[f]:
+ if (fvmap[f][v][2] == node.threshold) and \
+ (fvmap[f][v][1] == True):
+ l = v
+ n = n + 1
+ if (fvmap[f][v][2] == node.threshold) and \
+ (fvmap[f][v][1] == False):
+ r = v
+ n = n + 1
+
+ assert (n == 2)
+
+ elif (fvmap[f][0][2] == node.threshold):
+ l,r = (0,1)
+ else:
+ assert (fvmap[f][1][2] == node.threshold)
+ l,r = (1,0)
+
+ internal.append((node.id, f, l, node.children[0].id))
+ internal.append((node.id, f, r, node.children[1].id))
+
+ elif self.tool == "IAI":
+ left, right = [], []
+ for p in fvmap[f]:
+ if fvmap[f][p][1] == True:
+ assert (fvmap[f][p][2] in node.split)
+ if node.split[fvmap[f][p][2]]:
+ left.append(p)
+ else:
+ right.append(p)
+
+ internal.extend([(node.id, f, l, node.children[0].id) for l in left])
+ internal.extend([(node.id, f, r, node.children[1].id) for r in right])
+
+ elif self.tool == 'SKL':
+ left, right = [], []
+ for j in domains[f]:
+ if np.float32(fvmap[f][j][2]) <= np.float32(node.threshold):
+ left.append(j)
+ else:
+ right.append(j)
+
+ internal.extend([(node.id, f, l, node.children[0].id) for l in left])
+ internal.extend([(node.id, f, r, node.children[1].id) for r in right])
+
+ dom0, dom1 = dict(), dict()
+ dom0.update(domains)
+ dom1.update(domains)
+ dom0[f] = left
+ dom1[f] = right
+
+ else:
+ assert False, 'Unhandled model type: {0}'.format(self.tool)
+
+
+ internal, terminal = walk_tree(node.children[0], dom0, internal, terminal)
+ internal, terminal = walk_tree(node.children[1], dom1, internal, terminal)
+
+ return internal, terminal
+
+ domains = {f:[j for j in fvmap[f] if((fvmap[f][j][1]))] for f in fvmap}
+ internal, terminal = walk_tree(self.tree, domains, [], [])
+
+ dt = f"{self.n_nodes}\n{self.tree.id}\n"
+ dt += f"I {' '.join(dict.fromkeys([str(i) for i,_,_,_ in internal]))}\n"
+ dt +=f"T {' '.join([str(i) for i,_ in terminal ])}\n"
+ for i,c in terminal:
+ dt +=f"{i} T {c}\n"
+ for i,f, j, n in internal:
+ dt +=f"{i} {f} {j} {n}\n"
+
+ map = "Categorical\n"
+ map += f"{len(fvmap)}"
+ for f in fvmap:
+ for v in fvmap[f]:
+ if (fvmap[f][v][1] == True):
+ map += f"\n{f} {v} ={fvmap[f][v][2]}"
+ if (fvmap[f][v][1] == False) and self.tool == "ITI":
+ map += f"\n{f} {v} !={fvmap[f][v][2]}"
+
+
+ if feat_names is not None:
+ features_names_mapping = ''
+ for i,fid in enumerate(feat_names):
+ f=f'f{i}'
+ features_names_mapping += f'T:C,{fid}:{f},'+",".join([f'{fvmap[f][v][2]}:{v}' for v in fvmap[f] if(fvmap[f][v][1])])+'\n'
+
+ return dt, map, features_names_mapping
+
def convert_dt(self, feat_names):
"""
save dtree in .dt format & generate dtree map from the tree
@@ -182,18 +299,14 @@ class UploadedDecisionTree:
map += f"\n{f} {j} <={t}"
map += f"\n{f} {j+1} >{t}"
- features_names_mapping = {}
- for i,fid in enumerate(feat_names):
- f=f'f{i}'
- if f in self.intvs:
- features_names_mapping[f] = fid
- #features_names_mapping += ",".join([f'{t}:{j}' for j,t in enumerate(self.intvs[f])])+'\n'
- #thresholds = self.intvs[f][:-1]+[self.intvs[f][-2]]
- #fp.write(",".join([f'{t}:{j}' for j,t in enumerate(thresholds)])+'\n')
+ if feat_names is not None:
+ features_names_mapping = ''
+ for i,fid in enumerate(feat_names):
+ f=f'f{i}'
+ if f in self.intvs:
+ features_names_mapping += f'\n Categorical,{fid}:{f},'
+ features_names_mapping += ",".join([f'{t}:{j}' for j,t in enumerate(self.intvs[f])])
- print(dt)
- print(map)
- print(features_names_mapping)
return dt, map, features_names_mapping
diff --git a/pages/application/application.py b/pages/application/application.py
index a27efac..f5c7032 100644
--- a/pages/application/application.py
+++ b/pages/application/application.py
@@ -19,6 +19,7 @@ class Model():
self.ml_model = ''
self.pretrained_model = ''
+ self.model_dataset = ''
self.instance = ''
@@ -33,9 +34,12 @@ class Model():
self.component_class = self.dict_components[self.ml_model]
self.component_class = globals()[self.component_class]
- def update_pretrained_model(self, pretrained_model_update, filename_model):
+ def update_pretrained_model(self, pretrained_model_update):
self.pretrained_model = pretrained_model_update
- self.component = self.component_class(self.pretrained_model, filename_model)
+
+ def update_pretrained_model_dataset(self, model_dataset):
+ self.model_dataset = model_dataset
+ self.component = self.component_class(self.pretrained_model, self.model_dataset)
def update_instance(self, instance, enum, xtype, solver="g3"):
self.instance = instance
@@ -65,6 +69,16 @@ class View():
),
html.Div(id='pretrained_model_filename')])
+ self.model_dataset = html.Div([
+ dcc.Upload(
+ id='model_dataset_choice',
+ children=html.Div([
+ 'Drag and Drop or ',
+ html.A('Select File')
+ ]),
+ className="upload"
+ )])
+
self.instance_upload = html.Div([
dcc.Upload(
id='ml_instance_choice',
@@ -87,6 +101,9 @@ class View():
html.Br(),
self.pretrained_model_upload,
html.Hr(),
+ html.Label("Choose the pretrained model dataset : "),
+ self.model_dataset,
+ html.Hr(),
html.Label("Choose the instance to explain : "),
html.Br(),
self.instance_upload,
diff --git a/utils.py b/utils.py
index 4562909..4b38288 100644
--- a/utils.py
+++ b/utils.py
@@ -21,6 +21,19 @@ def parse_contents_graph(contents, filename):
return data
+def parse_contents_data(contents, filename):
+ content_type, content_string = contents.split(',')
+ decoded = base64.b64decode(content_string)
+ try:
+ if '.csv' in filename:
+ data = decoded.decode('utf-8')
+ except Exception as e:
+ print(e)
+ return html.Div([
+ 'There was an error processing this file.'
+ ])
+
+ return data
def parse_contents_instance(contents, filename):
content_type, content_string = contents.split(',')
--
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