Skip to content

Instantly share code, notes, and snippets.

@CaioEuzebio

CaioEuzebio/Dashboard.py

Created January 27, 2020 12:15
Show Gist options
  • Save CaioEuzebio/b3e45d86d8cde291ae655ecdd58423f1 to your computer and use it in GitHub Desktop.
Save CaioEuzebio/b3e45d86d8cde291ae655ecdd58423f1 to your computer and use it in GitHub Desktop.
Download ZIP
Raw
Dashboard.py
import base64
import datetime
import io
from dash.exceptions import PreventUpdate
import dash
from dash.dependencies import Input, Output, State
import dash_core_components as dcc
import dash_html_components as html
import dash_table
import dash_bootstrap_components as dbc
import pandas as pd
import random
import dash_table_experiments
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import plotly.graph_objects as go
import numpy as np
import dash_table_experiments as dash_table
import dash
from dash.dependencies import Input, Output
import dash_table
import pandas as pd
import plotly_express as px
import plotly.offline as pyo
get_colors = lambda n: list(map(lambda i: "#" + "%06x" % random.randint(0, 0xFFFFFF),range(n)))
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets, )
app.config['suppress_callback_exceptions'] = True
app.layout = html.Div([ html.H1('Dashboard Para Gestão de Operações', style = {'textAlign' : 'center', 'align-items': 'center', 'display': 'block'}),
dcc.Store(id='session', storage_type='session'),
dcc.Upload(
id='upload-data',
children=html.Div([
'Arrate e Solte Aqui O Orderlines Descompactado ou ',
html.A('Clique para selecionar')
], style= {'align-items': 'center',
'display': 'block'},
),
style={
'width': '96%',
'height': '10px',
'lineHeight': '10px',
'borderWidth': '2px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'margin': '10px',
'boxShadow': '0 0 14px 0 rgba(0, 0, 0, 0.2)',
'padding': '30px 20px',
'align-items': 'center',
'display': 'block'
},
# Allow multiple files to be uploaded
multiple=True
),
html.Div(id='output-data-upload'),
], style={
'boxShadow': '0 0 14px 0 rgba(0, 0, 0, 0.2)',
'align-items': 'center',
'display': 'block',
'textAlign': 'center'
,
})
def parse_contents(contents, filename, date):
content_type, content_string = contents.split(',')
decoded = base64.b64decode(content_string)
try:
if 'csv' in filename:
# Assume that the user uploaded a CSV file
df = pd.read_csv(
io.StringIO(decoded.decode('latin-1')))
"""fdr4= df.groupby(['Machine', 'Time']).sum()
fdr4.reset_index(inplace=True)
fig = px.scatter(fdr4, x="Time", y="Units Pruduced", color="Machine", template="plotly_dark")
fig.update_traces(mode='lines+marker')"""
df1 = df
#df2 = df
df1['Product Category'] = df1['Product Category'].str.upper()
df1.rename(columns={"Order Type": "OrderType"}, inplace=True)
df1.rename(columns={df1.columns[40]:'ProcessFinishTime'}, inplace=True)
df1.rename(columns={df1.columns[30]:'ProcessStartTime'}, inplace=True)
df1.rename(columns={df1.columns[12]:'HoraDrop'}, inplace=True)
dforder = df1.groupby('Order No').sum().round(2)
dforder = df1.pivot_table(index='Order No', aggfunc='sum').round(2)
dfmedir = df1[['Order No', 'OrderType','ProcessStartTime','ProcessFinishTime']]
dfmedir['UnidadesProcessadas'] = 0
#dfmedir.drop(['index'], axis=1, inplace=True)
dforder.reset_index(inplace=True)
# Convertendo tempos
dfmedir = pd.merge(dfmedir, dforder[['Order No','Qty']], on='Order No', how='left')
df1.loc[(df1.ProcessFinishTime == '::'), 'ProcessFinishTime'] = 0
df1.loc[(df1.ProcessStartTime == '::'), 'ProcessStartTime'] = 0
dfmedir.loc[(dfmedir.ProcessFinishTime == '::'), 'ProcessFinishTime'] = 0
dfmedir.loc[(dfmedir.ProcessStartTime == '::'), 'ProcessStartTime'] = 0
dfmedir.loc[(dfmedir.ProcessFinishTime != 0), 'UnidadesProcessadas'] = dfmedir['Qty']
df1['ProcessFinishTime'] = pd.to_datetime(df1['ProcessFinishTime'])
df1['ProcessStartTime'] = pd.to_datetime(df1['ProcessStartTime'])
df1['HoraDrop'] = pd.to_datetime(df1['HoraDrop'])
df1['TimeOfProcess'] = (df1['ProcessFinishTime'] - df1['ProcessStartTime'])
df1['TimeOfProcess'] = pd.to_datetime(df1['TimeOfProcess'])
df1['ProcessFinishTime'] = pd.to_datetime(df1['ProcessFinishTime'],errors='ignore')
df1['ProcessStartTime'] = pd.to_datetime(df1['ProcessStartTime'],errors='ignore')
df1['TimeOfProcess'] = df1['ProcessFinishTime'] - df1['ProcessStartTime']
df1['TimeOfProcess'] = pd.to_datetime(df1['TimeOfProcess'],errors='ignore')
dfmedir['ProcessFinishTime'] = pd.to_datetime(dfmedir['ProcessFinishTime'],errors='ignore')
dfmedir['ProcessStartTime'] = pd.to_datetime(dfmedir['ProcessStartTime'],errors='ignore')
dfmedir.drop_duplicates('Order No', inplace=True)
dfmedir['ProcessFinishTime'] = pd.to_datetime(dfmedir['ProcessFinishTime'],errors='ignore')
dfmedir['ProcessStartTime'] = pd.to_datetime(dfmedir['ProcessStartTime'],errors='ignore')
dfmedir['TimeOfProcess'] = dfmedir['ProcessFinishTime'] - dfmedir['ProcessStartTime']
dfmedir['Time Proc Seg'] = (dfmedir['TimeOfProcess'].dt.total_seconds().astype(int))
dfmedir['Horas Trabalhadas'] = ((dfmedir['Time Proc Seg'] / 3600).round(2))
#Teste De Processamento
df1.loc[(df1.Processed != '::'), 'UnidadesProcessadas'] = df1['Qty']
df1.loc[(df1.Processed == '::'), 'UnidadesProcessadas'] = 0
#df1.loc[(df1.Processed == '::'), 'UnidadesProcessadas'] = 0
dfmedir['Unidades Pendentes'] = dfmedir['Qty'] - dfmedir['UnidadesProcessadas']
dfmedir.loc[(dfmedir.UnidadesProcessadas == 0), 'Time Proc Seg'] = 0
dfmedir.loc[(dfmedir.UnidadesProcessadas == 0), 'Horas Trabalhadas'] = 0
dfmedirgrouped = dfmedir
dfmedirgrouped['Hora'] = dfmedirgrouped['ProcessFinishTime'].apply(lambda x: x.hour)
dfmedirgrouped = dfmedir.groupby('Order No').sum()
dfmedirgrouped.reset_index(inplace=True)
dfmedirgrouped.drop_duplicates('Order No', inplace=True)
#dfmedirgrouped['UPH'] = dfmedirgrouped['UnidadesProcessadas'] / dfmedirgrouped['Horas Trabalhadas']
dfmedirgrouped = pd.merge(dfmedirgrouped, dfmedir[['Order No','OrderType']], on='Order No', how='left')
dfmedirgrouped = pd.merge(dfmedirgrouped, df1[['Order No',' Packout station Number']], on='Order No', how='left')
dfmedirgrouped = pd.merge(dfmedirgrouped, df1[['Order No','Packout station Operator']], on='Order No', how='left')
dfmedirgrouped.drop_duplicates('Order No', inplace=True)
dfoperador = dfmedirgrouped.groupby('Packout station Operator').sum()
dfoperador.reset_index(inplace=True)
dfoperador['UPH'] = (dfoperador['UnidadesProcessadas'] / dfoperador['Horas Trabalhadas']).round(2)
dfordertype = dfmedirgrouped.groupby('OrderType').sum().round(2)
dfordertype.reset_index(inplace=True)
dfordertype['UPH'] = (dfordertype['UnidadesProcessadas'] / dfordertype['Horas Trabalhadas']).round(2)
dfstation = dfmedirgrouped.groupby(' Packout station Number').sum()
dfstation.reset_index(inplace=True)
dfstation['UPH'] = (dfstation['UnidadesProcessadas'] / dfstation['Horas Trabalhadas']).round(2)
dfmedirgrouped['MM_10'] = dfmedirgrouped['UnidadesProcessadas'].rolling(window=10).mean()
dfmedirgrouped['MM_20'] = dfmedirgrouped['UnidadesProcessadas'].rolling(window=20).mean()
dfmedirgrouped['MM_30'] = dfmedirgrouped['UnidadesProcessadas'].rolling(window=30).mean()
# Agrupamentos
df2 = df1.groupby('OrderType').sum()
df3 = df1.groupby(' Packout station Number').sum()
df4 = df1.groupby('Packout station Operator').sum()
df5 = df1.groupby('Product Category').sum()
df1.rename(columns={df1.columns[12]:'Received Time'}, inplace=True)
df6 = df1.groupby('Received Time').sum()
df7 = df1.groupby('Cut Off Time').sum()
df1['HoraDrop'] = df1['Received Time'].apply(lambda x: x.hour)
dfdrop = df1.groupby(['OrderType', 'HoraDrop']).sum()
dfdrop.reset_index(inplace=True)
# Convertendo tempos
dfmedir['HoraDrop'] = df1['Received Time'].apply(lambda x: x.hour)
dfdrop2 = dfmedir.groupby(['HoraDrop', 'OrderType']).sum()
dfdrop2.reset_index(inplace=True)
dfordertype['ETA'] = (dfordertype['Unidades Pendentes'] / dfordertype['UPH']).round(2)
# Calculando UnidadesProcessadas por hora
df8 = df1.groupby('OrderType').sum()
#DFYPPH
dfupph = dfmedirgrouped.groupby(['Packout station Operator','Hora','OrderType']).sum()
dfupph['UPPH'] = dfupph['UnidadesProcessadas'] / dfupph['Horas Trabalhadas']
dfupph.reset_index(inplace=True)
npessoas = dfupph['Packout station Operator'].unique()
npessoas = len(npessoas)+1
dfupphcanal = dfupph.groupby(['OrderType', 'Hora']).sum()
dfupphcanal.reset_index(inplace=True)
dfupphcanal['UPH'] = (dfupphcanal['UnidadesProcessadas'] / dfupphcanal['Horas Trabalhadas']).round(2)
dfupphcanal['UPPH'] = (dfupphcanal['UPH'] / npessoas).round(2)
# Resetando indices
df2.reset_index(inplace=True)
df3.reset_index(inplace=True)
df4.reset_index(inplace=True)
df5.reset_index(inplace=True)
df6.reset_index(inplace=True)
df7.reset_index(inplace=True)
tabelaordertype = dfordertype
tabelaordertype.drop(['Order No', 'Time Proc Seg', 'Hora'],axis=1, inplace=True)
tabelaordertype = tabelaordertype['Horas Trabalhadas'].round(2)
#TablePlan
dfplano = dfmedirgrouped.groupby('OrderType').sum().round(2)
dfplano.reset_index(inplace=True)
dfplano.drop(['Order No', 'Time Proc Seg', 'Hora','MM_10','MM_20','MM_30'],axis=1, inplace=True)
#dfplano.rename(columns={'Unidades Pendentes':'UnidadesPendentes'}, inplace=True)
#dfplano.rename(columns={'Horas Trabalhadas':'HorasTrabalhadas'}, inplace=True)
dfplano['UPH_BPI_vs_Perfil'] = 0
dfplano['Head_Disponível'] = 0
dfplano['UPH_BPI_vs_Head'] = 0
dfplano['ETA_Geral'] = 0
dfplano['Horas_Disp'] = 0
dfplano['Delta_Hora'] = 0
dfplano['Risco_Aging'] = 0
# Tabela de Gerenciamento de backlog
dgbydates = df1.groupby(['Cut Off Date', 'Cut Off Time','Order No', 'OrderType']).sum().round(2)
dgbydates.reset_index(inplace=True)
dgbydates.drop(['Line', 'PickNO','Etch Line','HoraDrop', 'UnidadesProcessadas' ],axis=1, inplace=True)
dgbydates = pd.merge(dgbydates, dfmedir[['Order No','Unidades Pendentes']], on='Order No', how='left')
dgbydates = pd.merge(dgbydates, dfmedir[['Order No','Time Proc Seg']], on='Order No', how='left')
tabelaback = dgbydates.groupby(['Cut Off Date','Cut Off Time','OrderType']).sum()
tabelaback['Unidades Processadas'] = (tabelaback['Qty'] - tabelaback['Unidades Pendentes']).round(2)
tabelaback['Horas Treabalhadas'] = (tabelaback['Time Proc Seg'] / 3600).round(2)
tabelaback['UPH'] = (tabelaback['Unidades Processadas'] / tabelaback['Horas Treabalhadas']).round(2)
tabelaback.drop(['Time Proc Seg', 'Order No'],axis=1, inplace=True)
tabelaback['ETA'] = (tabelaback['Unidades Pendentes'] / tabelaback['UPH']).round(2)
tabelaback.reset_index(inplace=True)
tabelaback['Horas Disp'] = ''
tabelaback['Delta Hora'] = 0
tabelaback['Risco Aging'] = ''
#Prod by cut off
dfprod = df1.groupby(['Product Category', 'Cut Off Date', 'Cut Off Time']).sum()
dfprod.reset_index(inplace=True)
dfprod['Unidades Pendentes'] = dfprod['Qty'] - dfprod['UnidadesProcessadas']
#tabelaordertype.drop(['UPH_BPI_vs_Perfil', 'Head_Disponível', 'ETA_BPI_vs_Head'], axis=1, inplace=True)
df9 = dfmedirgrouped.groupby(['OrderType', 'Hora']).sum()
df9.reset_index(inplace=True)
groups2 = df9.groupby(by='OrderType')
data2 = []
colors2=get_colors(50)
for group2, dataframe2 in groups2:
dataframe2 = dataframe2.sort_values(by=['Hora'])
trace2 = go.Scatter(x=dataframe2.Hora.tolist(),
y=dataframe2.UnidadesProcessadas.tolist(),
marker=dict(color=colors2[len(data2)]),
name=group2,
mode="lines+markers+text",
textposition='top center',
text=dataframe2.UnidadesProcessadas)
data2.append(trace2)
layout2 = go.Layout(xaxis={'title': 'Time'},
yaxis={'title': 'Produced Units'},
margin={'l': 40, 'b': 40, 't': 50, 'r': 50},
hovermode='closest',
template='plotly_white',
title={
'text': "Pruducão /Hora (Canal)",
'y':.9,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'})
figure2 = go.Figure(data=data2, layout=layout2)
df10 = dfmedirgrouped.groupby([' Packout station Number', 'Hora']).sum()
df10.reset_index(inplace=True)
groups3 = df10.groupby(by=' Packout station Number')
data3 = []
colors3=get_colors(50)
for group3, dataframe3 in groups3:
dataframe3 = dataframe3.sort_values(by=['Hora'])
trace3 = go.Scatter(x=dataframe3.Hora.tolist(),
y=dataframe3.UnidadesProcessadas.tolist(),
marker=dict(color=colors3[len(data3)]),
name=group3,
)
data3.append(trace3)
layout3 = go.Layout(xaxis={'title': 'Hora'},
yaxis={'title': 'Unidades Produzidas'},
margin={'l': 40, 'b': 40, 't': 50, 'r': 50},
hovermode='closest',
template='plotly_white',
title={
'text': "Pruducão /Hora (Estação)",
'y':.9,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'})
figure3 = go.Figure(data=data3, layout=layout3)
df11 = dfmedirgrouped.groupby(['Packout station Operator', 'Hora']).sum()
df11.reset_index(inplace=True)
groups4 = df11.groupby(by='Packout station Operator')
data4 = []
colors4=get_colors(50)
for group4, dataframe4 in groups4:
dataframe4 = dataframe4.sort_values(by=['Hora'])
trace4 = go.Scatter(x=dataframe4.Hora.tolist(),
y=dataframe4.UnidadesProcessadas.tolist(),
marker=dict(color=colors4[len(data4)]),
name=group4,
mode='lines+markers'
)
data4.append(trace4)
layout4 = go.Layout(xaxis={'title': 'Hora'},
yaxis={'title': 'Unidades Produzidas'},
margin={'l': 40, 'b': 40, 't': 50, 'r': 50},
hovermode='closest',
template='plotly_white',
title={
'text': "Pruducão /Hora (Operador)",
'y':.9,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'})
figure4 = go.Figure(data=data4, layout=layout4)
dfmm = dfmedirgrouped.groupby('Hora').sum()
dfmm.reset_index(inplace=True)
dfmm = dfmm.drop(dfmm[dfmm.Hora == 0].index)
tracemm10 = go.Scatter(x=dfmm.Hora.tolist(),
y=dfmm.MM_10.tolist(),
name='MM_10')
tracemm20 = go.Scatter(x=dfmm.Hora.tolist(),
y=dfmm.MM_20.tolist(),
name='MM_20',
)
tracemm30 = go.Scatter(x=dfmm.Hora.tolist(),
y=dfmm.MM_30.tolist(),
name='MM_30',
)
traceunidades = go.Bar(x=dfmm.Hora.tolist(),
y=dfmm.UnidadesProcessadas.tolist(),
name='Qty Produzida',
marker_color='blue'
)
datamm = (tracemm10, tracemm20,tracemm30, traceunidades)
layoutmm = go.Layout(xaxis={'title': 'Hora'},
yaxis={'title': 'Unidades Produzidas'},
margin={'l': 40, 'b': 40, 't': 50, 'r': 50},
hovermode='closest',
template='plotly_white',
title={'text': "Medias Moveis - 10, 20 e 30 Periodos",
'y':.9,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'}
)
figuremm = go.Figure(data=datamm, layout=layoutmm)
dfmmcanal = dfmedirgrouped.groupby(['Hora', 'OrderType']).sum()
dfmmcanal.reset_index(inplace=True)
dfmmcanal = dfmmcanal.drop(dfmmcanal[dfmmcanal.Hora == 0].index)
groups7 = dfmmcanal.groupby(by='OrderType')
data7 = []
colors7=get_colors(50)
for group7, dataframe7 in groups7:
dataframe7 = dataframe7.sort_values(by=['Hora'])
trace7 = go.Scatter(x=dataframe7.Hora.tolist(),
y=dataframe7.MM_10.tolist(),
marker=dict(color=colors7[len(data7)]),
name=group7,
)
data7.append(trace7)
layout7 = go.Layout(xaxis={'title': 'Hora'},
yaxis={'title': 'Unidades Produzidas'},
margin={'l': 40, 'b': 40, 't': 50, 'r': 50},
hovermode='closest',
template='plotly_white',
title={
'text': "Média Móvel 10 Intervalos Por Canal",
'y':.9,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'})
figure7 = go.Figure(data=data7, layout=layout7)
dfgraphdrop = df1.groupby(['HoraDrop', 'OrderType']).sum()
dfgraphdrop.reset_index(inplace=True)
groupsdn = dfgraphdrop.groupby(by='OrderType')
tracefd = go.Bar(x=dfdrop2.HoraDrop.tolist(),
y=dfdrop2.Qty.tolist(),
name='Dropado',
)
tracefd2 = go.Bar(x=dfdrop2.HoraDrop.tolist(),
y=dfdrop2.UnidadesProcessadas.tolist(),
name='Processado',
)
traceff = go.Bar(x=dfdrop2.HoraDrop.tolist(),
y=dfdrop2['Unidades Pendentes'].tolist(),
name='Pendente',
)
datafd = (tracefd, tracefd2, traceff)
layoutfd= go.Layout(xaxis={'title': 'Hora'},
yaxis={'title': 'Unidades Produzidas'},
margin={'l': 40, 'b': 40, 't': 50, 'r': 50},
hovermode='closest',
template='plotly_white',
barmode='stack',
title={
'text': "Unidades Recebidas vs Processadas - Por Horario",
'y':.9,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'})
figurefd = go.Figure(data=datafd, layout=layoutfd)
#UPPH
dfupphcanal = dfupphcanal.drop(dfupphcanal[dfupphcanal.Hora == 0].index)
groups52 = dfupphcanal.groupby(by='OrderType')
data52 = []
colors52=get_colors(50)
for group52, dataframe52 in groups52:
dataframe52 = dataframe52.sort_values(by=['Hora'])
trace52 = go.Scatter(x=dataframe52.Hora.tolist(),
y=dataframe52.UPPH.tolist(),
marker=dict(color=colors52[len(data52)]),
name=group52,
)
data52.append(trace52)
layout52 = go.Layout(xaxis={'title': 'Hora'},
yaxis={'title': 'Unidades Produzidas'},
margin={'l': 40, 'b': 40, 't': 50, 'r': 50},
hovermode='closest',
template='plotly_white',
title={
'text': "Unidades Produzidas Por Pessoas / Hora",
'y':.9,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'})
figure52 = go.Figure(data=data52, layout=layout52)
tabelaupph = dfupphcanal.drop(['Unidades Pendentes','Qty','Order No', 'Time Proc Seg', 'MM_10', 'MM_20', 'MM_30', 'UPH', 'Hora' ], axis=1)
tabelaupph2 = tabelaupph.groupby('OrderType').sum().round(2)
tabelaupph2.reset_index(inplace=True)
elif 'xls' in filename:
# Assume that the user uploaded an excel file
df = pd.read_excel(io.BytesIO(decoded))
except Exception as e:
print(e)
return html.Div([
'There was an error processing this file.'
])
return html.Div([
html.H5('Nome Do Arquivo:'+filename),
html.H6(datetime.datetime.fromtimestamp(date)),
html.Hr(), # horizontal line
# For debugging, display the raw contents provided by the web browser
html.Div('Conteudo Bruto: '),
html.Pre(contents[0:200] + '...',
style={
'whiteSpace': 'pre-wrap',
'wordBreak': 'break-all',
'align-items': 'center',
'display': 'block'
}),
#Tabela De Desempenho Por Canal<
html.Div([
html.Div([
html.H2(children = "Tabela De Desempenho Por Canal",
style = {'textAlign' : 'center',}),
html.Br(""),
dash_table.DataTable(
id='table1',
columns=[{"name": i, "id": i} for i in dfordertype.columns],
data=dfordertype.to_dict('records'),
style_table={'textAlign': 'center'},
style_as_list_view=True,
style_cell={'padding': '5px','fontSize': 12, 'textAlign': 'center'},
style_header={
'backgroundColor': 'Gainsboro',
'fontWeight': 'bold',
'fontSize': 12},
),
],style={'textAlign': 'center',
'align-items': 'center',
'fontSize': 12,
'width': '100%',
'display': 'block',
'align-items': 'center',
'justify-content': 'center',
'boxShadow': '0 0 14px 0 rgba(0, 0, 0, 0.2)',
'padding': '30px 20px'}),
],style={'textAlign': 'center'}),
#Tabela De Desempenho Por Canal/>
#Planejamento Estilizado<
html.Div([
html.Div([
html.H2(children = "Tabela De Suporte Ao Planejamento",
style = {'textAlign' : 'center',}),
html.Br(""),
dash_table.DataTable(
id='computed-table',
columns=[{"name": i, "id": i} for i in dfplano.columns ],
data=dfordertype.to_dict('records'),
editable=True,
style_table={'textAlign': 'center'},
style_as_list_view=True,
style_cell={'padding': '5px','fontSize': 12, 'textAlign': 'center'},
style_header={
'backgroundColor': 'Gainsboro',
'fontWeight': 'bold',
'fontSize': 12},
),
],style={'textAlign': 'center',
'align-items': 'center',
'fontSize': 12,
'width': '100%',
'display': 'block',
'align-items': 'center',
'justify-content': 'center',
'boxShadow': '0 0 14px 0 rgba(0, 0, 0, 0.2)',
'padding': '30px 20px'}),
],style={'textAlign': 'center',
'marginTop': '15px',
'display': 'block'}),
#Planejamento Estilizado/>
# Tabela Gerenciamento de Backlog<
html.Div([
html.Div([
html.H2(children = "Status Por Cut Off",
style = {'textAlign' : 'center',}),
html.Br(""),
dash_table.DataTable(
id='table-backlog',
columns=[{"name": i, "id": i} for i in tabelaback.columns ],
data=tabelaback.to_dict('records'),
editable=True,
style_table={'textAlign': 'center'},
style_as_list_view=True,
style_cell={'padding': '5px','fontSize': 12, 'textAlign': 'center'},
style_header={
'backgroundColor': 'Gainsboro',
'fontWeight': 'bold',
'fontSize': 12},
),
],style={'textAlign': 'center',
'align-items': 'center',
'fontSize': 12,
'width': '100%',
'display': 'block',
'align-items': 'center',
'justify-content': 'center',
'boxShadow': '0 0 14px 0 rgba(0, 0, 0, 0.2)',
'padding': '30px 20px'}),
],style={'textAlign': 'center',
'marginTop': '15px',
'display': 'block'}),
# Tabela Gerenciamento de Backlog/>
#Mos Prod hora estacao & prod hora operador<
html.Div([
html.Div([
html.H3('Produção Por Estação / Hora'),
dcc.Graph(id='chart1', figure= figure3)
], className="six columns", style= {'width': '100%','textAlign' : 'center',
'marginBottom': 50, 'marginTop': 25,
'boxShadow': '0 0 14px 0 rgba(0, 0, 0, 0.2)',
'padding': '30px 20px', 'display': 'flex',
'display': 'inline-block',
'flexDirection': 'row'}),
html.Div([
html.H3('Produção Por Operador / Hora'),
dcc.Graph(id='chart2', figure=figure4)
], className="six columns", style= {'width': '100%', 'textAlign' : 'center',
'marginBottom': 50, 'marginTop': 25,
'boxShadow': '0 0 14px 0 rgba(0, 0, 0, 0.2)',
'padding': '30px 20px', 'display': 'flex',
'display': 'inline-block',
'flexDirection': 'row'}),
], className="row", style={'display': 'flex', 'flexWrap': 'nowrap', 'flexDirection': 'row'}),
#Mos Prod hora estacao & prod hora operador/>
#Tabela tipo produto por cut off<
html.Div([
html.Div([
html.H2(children = "Tipo de Produto Por Cut Off",
style = {'textAlign' : 'center',}),
html.Br(""),
dash_table.DataTable(
id='table-prod',
columns=[{"name": i, "id": i} for i in dfprod.columns ],
data=dfprod.to_dict('records'),
editable=True,
style_table={'textAlign': 'center'},
style_as_list_view=True,
style_cell={'padding': '5px','fontSize': 12, 'textAlign': 'center'},
style_header={
'backgroundColor': 'Gainsboro',
'fontWeight': 'bold',
'fontSize': 12},
),
],style={'textAlign': 'center',
'align-items': 'center',
'fontSize': 12,
'width': '100%',
'display': 'block',
'align-items': 'center',
'justify-content': 'center',
'boxShadow': '0 0 14px 0 rgba(0, 0, 0, 0.2)',
'padding': '30px 20px'}),
],style={'textAlign': 'center',
'marginTop': '15px',
'display': 'block'}),
#Tabela tipo de produto por cur off/>
#Prod Hora Canal<
html.Div([
html.Div([
html.Div([
html.H3(children = "Produção Por Hora (Canal)",
style = {'textAlign' : 'center',}),
html.Br(""),
dcc.Graph( id = 'GrapGo', figure = figure2),
],style={'textAlign': 'center',
'align-items': 'center',
'fontSize': 12,
'width': '100%',
'display': 'block',
'align-items': 'center',
'justify-content': 'center',
'boxShadow': '0 0 14px 0 rgba(0, 0, 0, 0.2)',
'padding': '30px 20px'}),
],style={'textAlign': 'center',
'marginTop': '15px',
'display': 'block'}),
#Prod Hora Canal/>
#Prod Hora Estaao<
html.Div([
html.Div([
html.H3(children = "Produção Por Hora (Estação)",
style = {'textAlign' : 'center',}),
html.Br(""),
dcc.Graph(id = 'GrapGo2', figure = figure3),
],style={'textAlign': 'center',
'align-items': 'center',
'fontSize': 12,
'width': '100%',
'display': 'block',
'align-items': 'center',
'justify-content': 'center',
'boxShadow': '0 0 14px 0 rgba(0, 0, 0, 0.2)',
'padding': '30px 20px'}),
],style={'textAlign': 'center',
'marginTop': '15px',
'display': 'block'}),
#Prod Hora Estacao/>
#Prod Hora Operador<
html.Div([
html.Div([
html.H3(children = "Produção Por Hora (Operador)",
style = {'textAlign' : 'center',}),
html.Br(""),
dcc.Graph( id = 'GrapGo3', figure = figure4),
],style={'textAlign': 'center',
'align-items': 'center',
'fontSize': 12,
'width': '100%',
'display': 'block',
'align-items': 'center',
'justify-content': 'center',
'boxShadow': '0 0 14px 0 rgba(0, 0, 0, 0.2)',
'padding': '30px 20px'}),
],style={'textAlign': 'center',
'marginTop': '15px',
'display': 'block'}),
#Prod Hora Operador/>
#Medias moveis<
html.Div([
html.Div([
html.H3(children = "Médias Mveis (10, 20 e 30 periodos)",
style = {'textAlign' : 'center',}),
html.Br(""),
dcc.Graph(id = 'GrapGo4',figure = figuremm),
],style={'textAlign': 'center',
'align-items': 'center',
'fontSize': 12,
'width': '100%',
'display': 'block',
'align-items': 'center',
'justify-content': 'center',
'boxShadow': '0 0 14px 0 rgba(0, 0, 0, 0.2)',
'padding': '30px 20px'}),
],style={'textAlign': 'center',
'marginTop': '15px',
'display': 'block'}),
#Medias moveis/>
#Media 10 intervalos<
html.Div([
html.Div([
html.H3(children = "Médias Móveis - 10 Intervalos por canal",
style = {'textAlign' : 'center',}),
html.Br(""),
dcc.Graph(id = 'GrapGo4',figure = figure7),
],style={'textAlign': 'center',
'align-items': 'center',
'fontSize': 12,
'width': '100%',
'display': 'block',
'align-items': 'center',
'justify-content': 'center',
'boxShadow': '0 0 14px 0 rgba(0, 0, 0, 0.2)',
'padding': '30px 20px'}),
],style={'textAlign': 'center',
'marginTop': '15px',
'display': 'block'}),
#Media 10 intervalos/>
#Unidades Recebidas Vs Processadas/hora<
html.Div([
html.Div([
html.H3(children = "Unidades Recebidas Vs Processadas por Hora",
style = {'textAlign' : 'center',}),
html.Br(""),
dcc.Graph(id = 'GrapGo4',figure = figurefd),
],style={'textAlign': 'center',
'align-items': 'center',
'fontSize': 12,
'width': '100%',
'display': 'block',
'align-items': 'center',
'justify-content': 'center',
'boxShadow': '0 0 14px 0 rgba(0, 0, 0, 0.2)',
'padding': '30px 20px'}),
],style={'textAlign': 'center',
'marginTop': '15px',
'display': 'block'}),
#Unidades Recebidas Vs Processadas/hora/>
#Tabela UPPH<
html.Div([
html.Div([
html.H2(children = "Tabela De Desempenho Por Pessoa / Hora",
style = {'textAlign' : 'center',}),
html.Br(""),
dash_table.DataTable(
id='tableUPPH',
columns=[{"name": i, "id": i} for i in tabelaupph2.columns],
data=tabelaupph2.to_dict('records'),
style_table={'textAlign': 'center'},
style_as_list_view=True,
style_cell={'padding': '5px','fontSize': 12, 'textAlign': 'center'},
style_header={
'backgroundColor': 'Gainsboro',
'fontWeight': 'bold',
'fontSize': 12},
),
],style={'textAlign': 'center',
'align-items': 'center',
'fontSize': 12,
'width': '100%',
'display': 'block',
'align-items': 'center',
'justify-content': 'center',
'boxShadow': '0 0 14px 0 rgba(0, 0, 0, 0.2)',
'padding': '30px 20px'}),
],style={'textAlign': 'center',
'marginTop': '15px',
'display': 'block'}),
#Tabela UPPH/>
#UPPH Chart<
html.Div([
html.Div([
html.H3(children = "Unidades Recebidas Vs Processadas por Hora",
style = {'textAlign' : 'center',}),
html.Br(""),
dcc.Graph(id = 'GrapGo4',figure = figure52)
],style={'textAlign': 'center',
'align-items': 'center',
'fontSize': 12,
'width': '100%',
'display': 'block',
'align-items': 'center',
'justify-content': 'center',
'boxShadow': '0 0 14px 0 rgba(0, 0, 0, 0.2)',
'padding': '30px 20px'}),
],style={'textAlign': 'center',
'marginTop': '15px',
'display': 'block'}),
#UPPH chrta/>
], style={'marginBottom': 50,
'marginTop': 50,
'textAlign': 'center',
'padding': '30px 20px',
'align-items': 'center' }),
], style={'marginLeft': 50,
'marginRight': 50,
'display':'block',
'textAlign': 'center',
'align-items': 'center',
'padding': '30px 20px'})
#Update Normal Tables
@app.callback(Output('output-data-upload', 'children'),
[Input('upload-data', 'contents')],
[State('upload-data', 'filename'),
State('upload-data', 'last_modified')])
def update_output(list_of_contents, list_of_names, list_of_dates):
if list_of_contents is not None:
children = [
parse_contents(c, n, d) for c, n, d in
zip(list_of_contents, list_of_names, list_of_dates)]
return children
else:
PreventUpdate
@app.callback(
Output('computed-table', 'data'),
[Input('computed-table', 'data_timestamp')],
[State('computed-table', 'data')])
def update_columns(timestamp, rows):
for row in rows:
try:
if row['Unidades Pendentes'] != 0:
row['UPH_BPI_vs_Head'] = float(row['UPH_BPI_vs_Perfil']) * float(row['Head_Disponível'])
row['ETA_Geral'] = float(row['Unidades Pendentes']) / float(row['UPH_BPI_vs_Head'])
row['Delta_Hora'] = float(row['Horas_Disp']) - float(row['ETA_Geral'])
row['Risco_Aging'] = float(row['Delta_Hora']) * float(row['UPH_BPI_vs_Head'])
else:
row['UPH_BPI_vs_Head'] = "Completed"
row['ETA_Geral'] = "Completed"
row['Delta_Hora'] = "Completed"
row['Risco_Aging'] = "Completed"
row['UPH_BPI_vs_Perfil'] = "Completed"
row['Head_Disponível'] = "Completed"
row['Horas_Disp'] = "Completed"
except:
row['ETA_Geral'] = 'NA'
return rows
@app.callback(
Output('table-backlog', 'data'),
[Input('table-backlog', 'data_timestamp')],
[State('table-backlog', 'data')])
def update_columns(timestamp, rows):
for row in rows:
try:
if row['Unidades Pendentes'] != 0:
row['Delta Hora'] = float(row['Horas Disp']) - float(row['ETA'])
row['Risco Aging'] = float(row['Delta Hora']) * float(row['UPH'])
else:
row['ETA'] = "Completed"
except:
row['ETA'] = row['ETA']
return rows
if __name__ == "__main__":
app.run_server()
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment