【发布时间】：2020-03-05 12:36:11
【问题描述】：
输入 heroku logs --tail --app "app_name" 后 我发现上面的错误 没有这样的表 我的数据库工作正常，并且没有错误，然后显示此错误，即没有这样的表
import pandas as pd
import base64
import datetime
import io
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 sqlalchemy as sa
from datetime import date, timedelta
import sqlite3
import flask
external_stylesheets = ["https://codepen.io/chriddyp/pen/bWLwgP.css"]
con = sqlite3.connect('C:\\Users\\Admin\\Downloads\\Python Scripts\\Next_Gresing_Date\\Grease.db', check_same_thread=False)
df = pd.read_sql_query('SELECT * FROM ABCC1;', con)
dataaa = df
### SQL Engine
disk_engine = sa.create_engine("sqlite:///C:\\Users\\Admin\\Downloads\\Python Scripts\\Next_Gresing_Date\\Grease.db")
connection = disk_engine.connect()
metadata = sa.MetaData()
SQL_table = sa.Table(
"ABCC1",
metadata,
sa.Column("Site", sa.VARCHAR),
sa.Column("Last_Greased_Date:YYYY-MM-DD", sa.TEXT),
sa.Column("Department", sa.VARCHAR),
sa.Column("Equipment_ID", sa.VARCHAR),
sa.Column("Equipment_Name", sa.VARCHAR),
sa.Column("HAC_Code", sa.VARCHAR),
sa.Column("Frequency_Schedule_Days", sa.INTEGER),
sa.Column("NEXT_Date:YYYY-MM-DD", sa.TEXT),
sa.Column("Grease_Grade", sa.VARCHAR),
sa.Column("Point", sa.INTEGER),
sa.Column("Stroke", sa.INTEGER),
sa.Column("Grease_Gun_No(gm_per_stroke)", sa.VARCHAR),
sa.Column("Quantity_Grease_In_Grams(in_one_stroke)", sa.FLOAT),
sa.Column("Total_Quantity_Grease_Used(in_gms)", sa.FLOAT),
sa.Column("Name_Of_Technicians", sa.TEXT),
sa.Column("Remarks", sa.VARCHAR),
)
disk_engine = sa.create_engine(
"sqlite:///C:\\Users\\Admin\\Downloads\\Python Scripts\\Next_Gresing_Date\\Grease.db",
connect_args={"check_same_thread": False})
connection = disk_engine.connect()
metadata = sa.MetaData()
SQL_TABLE = sa.Table(
"Scheduler",
metadata,
sa.Column("Department", sa.VARCHAR),
sa.Column("EqName", sa.VARCHAR),
sa.Column("EqId", sa.VARCHAR),
sa.Column("GreaseGrade", sa.VARCHAR),
sa.Column("Point", sa.INTEGER),
sa.Column("Stroke", sa.INTEGER),
sa.Column("gmperstroke", sa.FLOAT),
sa.Column("TotalGreaseused(ingms.)", sa.FLOAT),
sa.Column('GreaseNippleStatus', sa.TEXT),
sa.Column("Schedule.freqDays", sa.INTEGER),
sa.Column("AttendeBy", sa.VARCHAR),
sa.Column("Remark/anyabnormalitiesfound", sa.VARCHAR),
)
dss = pd.read_sql_query('SELECT * FROM Scheduler;', con)
app = dash.Dash(__name__, external_stylesheets=external_stylesheets, )
server = flask.Flask("C:\\Users\\Admin\\Downloads\\Python Scripts\\Next_Gresing_Date\\Database")
def serve_layout():
layout = html.Div([
html.H4("Next Greasing Date"),
html.H4("Upload"),
dcc.Upload(
id="upload-data",
children=html.Div(
["Drag and drop or click to select a file to upload."]
),
multiple=True,
style={
"width": "100%",
"height": "60px",
"lineHeight": "60px",
"borderWidth": "1px",
"borderStyle": "dashed",
"borderRadius": "5px",
"textAlign": "center",
"margin": "10px",
}
),
html.Div(id='output-of-upload'),
html.P([
html.Label('Choose a Department:', style={'fontSize': 18}),
dcc.Dropdown(
id='dept_input',
options=[{'label': i, 'value': i} for i in dataa['Department'].unique()],
style={'height': '30px', 'width': '300px'}
)], className="three columns"),
html.P([
html.Label('Choose Date:Day-Month-Year', style={'fontSize': 18}),
dcc.DatePickerSingle(
id="single",
month_format='MMMM Y',
placeholder='DD-MM-YYYY',
with_portal=True,
clearable=True,
display_format="DD-MM-YYYY",
)], ),
html.P([
html.Label('Next Greasing Dates', style={'fontSize': 18}),
html.Div([
dash_table.DataTable(
id='next_greasing_dates', sort_action="native", sort_mode="multi",
columns=[{"name": i, "id": i} for i in ['Site', 'Last_Greased_Date:YYYY-MM-DD', 'Department',
'Equipment_ID', 'Equipment_Name',
'HAC_Code', 'Frequency_Schedule_Days',
'NEXT_Date:YYYY-MM-DD', 'Grease_Grade', 'Point', 'Stroke',
'Grease_Gun(gm_per_stroke)',
'Quantity_Grease_In_Grams(in_one_stroke)',
'Total_Quantity_Grease_Used(in_gms)',
'Name_Of_Technicians', 'Remarks']],
export_format='csv',
export_columns="all",
export_headers='display',
merge_duplicate_headers=True,
style_cell={'textAlign': 'left'},
style_cell_conditional=[
{'if': {'column_id': 'Equipment_ID'},
'textAlign': 'center'},
{'if': {'column_id': 'HAC_CODE'},
'textAlign': 'center'},
{'if': {'column_id': 'Last_Greased_Date:YYYY-MM-DD'},
'textAlign': 'center'},
{'if': {'column_id': 'Frequency_Schedule_Days'},
'textAlign': 'center'},
{'if': {'column_id': 'NEXT_Date:YYYY-MM-DD'},
'textAlign': 'center'},
{'if': {'column_id': 'Point'},
'textAlign': 'center'},
{'if': {'column_id': 'Quantity_Grease_In_Grams(in_one_stroke)'},
'textAlign': 'center'},
{'if': {'column_id': 'Stroke'},
'textAlign': 'center'},
{'if': {'column_id': 'Date:Year-Month-Date'},
'width': '40%'},
{'if': {'column_id': 'NEXT_Date:YYYY-MM-DD'},
'width': '40%'}],
style_header={
'backgroundColor': 'rgb(230, 230, 230)',
'fontWeight': 'bold'},
style_table={'overflowX': 'scroll'}, )
])
]),
]
)
return layout
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
dg = pd.read_csv(io.StringIO(decoded.decode('utf-8'))).to_sql('ABCC1', con, if_exists='append', index=False)
elif 'xls' in filename:
# Assume that the user uploaded an excel file
dg = pd.read_excel(io.BytesIO(decoded)).to_sql('ABCC1', con, if_exists='append', index=False)
except Exception as e:
print(e)
return html.Div([
'There was an error processing this file.'
])
return html.Div([
html.H5(filename),
html.H6(datetime.datetime.fromtimestamp(date))
])
# adding one column in dataa by setting its value to 0
dataaa['freq'] = 0
dataaa['grade'] = 0
dataaa['qty'] = 0
dataaa['stroke'] = 0
dataaa['point'] = 0
dataaa['man'] = 0
dataaa['depty'] = 0
# converting date object to date time format
dataaa['Last_Greased_Date:YYYY-MM-DD'] = pd.to_datetime(dataaa['Last_Greased_Date:YYYY-MM-DD'])
dataaa['Last_Greased_Date:YYYY-MM-DD'].dt.strftime("%Y-%m-%d")
dataa = dataaa
dataa['date'] = pd.to_datetime(dataa['Last_Greased_Date:YYYY-MM-DD'])
# checking eq id and eq name in scheduler dataset and then filling the respective new columns
for i in range(0, len(dss)):
m = dss['EqName'][i]
n = dss['EqId'][i]
if (dataa['Equipment_ID'] == n).any():
dataa['freq'][(dataa['Equipment_Name'] == m) & (dataa['Equipment_ID'] == n)] = dss['Schedule.freqDays'][i]
dataa['depty'][(dataa['Equipment_Name'] == m) & (dataa['Equipment_ID'] == n)] = dss['Department'][i]
dataa['grade'][(dataa['Equipment_Name'] == m) & (dataa['Equipment_ID'] == n)] = dss['GreaseGrade'][i]
dataa['point'][(dataa['Equipment_Name'] == m) & (dataa['Equipment_ID'] == n)] = dss['Point'][i]
dataa['qty'][(dataa['Equipment_Name'] == m) & (dataa['Equipment_ID'] == n)] = dss['TotalGreaseused(ingms.)'][i]
dataa['stroke'][(dataa['Equipment_Name'] == m) & (dataa['Equipment_ID'] == n)] = dss['Stroke'][i]
dataa['man'][(dataa['Equipment_Name'] == m) & (dataa['Equipment_ID'] == n)] = dss['AttendeBy'][i]
else:
dataa['freq'][(dataa['Equipment_Name'] == m) & (dataa['HAC_Code'] == n)] = dss['Schedule.freqDays'][i]
dataa['depty'][(dataa['Equipment_Name'] == m) & (dataa['HAC_Code'] == n)] = dss['Department'][i]
dataa['grade'][(dataa['Equipment_Name'] == m) & (dataa['HAC_Code'] == n)] = dss['GreaseGrade'][i]
dataa['point'][(dataa['Equipment_Name'] == m) & (dataa['HAC_Code'] == n)] = dss['Point'][i]
dataa['qty'][(dataa['Equipment_Name'] == m) & (dataa['HAC_Code'] == n)] = dss['TotalGreaseused(ingms.)'][i]
dataa['stroke'][(dataa['Equipment_Name'] == m) & (dataa['HAC_Code'] == n)] = dss['Stroke'][i]
dataa['man'][(dataa['Equipment_Name'] == m) & (dataa['HAC_Code'] == n)] = dss['AttendeBy'][i]
# Sorting date in decending order inorder to get last greasing date of every equipment
dataa['Last_Greased_Date:YYYY-MM-DD'] = dataa['Last_Greased_Date:YYYY-MM-DD'].astype(str)
d = dataa.sort_values("Last_Greased_Date:YYYY-MM-DD", ascending=False)
d.reset_index(inplace=True)
# droping the duplicates of eqid and eqname and keeping them first inorder to get the last graesing dates of equipments
di = d.drop_duplicates(["Equipment_Name", "HAC_Code"], keep='first')
di = d.drop_duplicates(["Equipment_ID", "Equipment_Name"], keep='first')
di.drop('index', axis=1, inplace=True)
di.reset_index(inplace=True)
app.layout = serve_layout
@app.callback(Output('next_greasing_dates', 'data'),
[Input('single', 'date'),
Input('dept_input', 'value')])
def ngrease_table(datee, dept):
data = di[di['Department'] == dept]
data1 = data[['Site', 'Last_Greased_Date:YYYY-MM-DD', 'Department', 'Equipment_ID', 'Equipment_Name',
'HAC_Code', 'Frequency_Schedule_Days', 'NEXT_Date:YYYY-MM-DD',
'Grease_Grade', 'Point', 'Stroke', 'Grease_Gun_No(gm_per_stroke)',
'Quantity_Grease_In_Grams(in_one_stroke)', 'Total_Quantity_Grease_Used(in_gms)',
'Name_Of_Technicians', 'Remarks', 'freq', 'date', "grade", 'point', "qty", "stroke", "man"]]
data1.reset_index(inplace=True)
data1.drop('index', axis=1, inplace=True)
yl = int(
datee.split('-')[0]) # splitting the end date in year month and date and putting it in separate index 0,1,2
ml = int(datee.split('-')[1])
dl = int(datee.split('-')[2])
df2 = pd.DataFrame()
for i in range(0, len(data1)):
# splitting last date in year month and date and putting it in separate index 0,1,2
y = int(data1['Last_Greased_Date:YYYY-MM-DD'][i].split('-')[0])
m = int(data1['Last_Greased_Date:YYYY-MM-DD'][i].split('-')[1])
d = int(data1['Last_Greased_Date:YYYY-MM-DD'][i].split('-')[2])
if data1['freq'][i] != 0: # giving freq not = 0 because it gives us error
# subtracting end date with the last date to get number of days in between and then divding it by freq
# to get the quotient so we can come to know that how many times it should get greased
# for example if the diff betwen dates are 30 and the and the freq we get is 10 when diveded we get 3days
# that means 3 times we can apply greasing between to selected dates
p = (date(year=yl, month=ml, day=dl) - date(year=y, month=m, day=d)) // data1['freq'][i]
# making new data frame changing column names adding new column NEXT_Date
# which is initially 0 and then multiplying p.days to it ie. 3days will get
# multiplied to it and number of rows will be 3
df1 = pd.DataFrame({'Site': [data1['Site'][i]] * p.days,
'Last_Greased_Date:YYYY-MM-DD': [data1['Last_Greased_Date:YYYY-MM-DD'][i]] * p.days,
'Department': [data1['Department'][i]] * p.days,
'Equipment_ID': [data1['Equipment_ID'][i]] * p.days,
'Equipment_Name': [data1['Equipment_Name'][i]] * p.days,
'HAC_Code': [data1['HAC_Code'][i]] * p.days,
'Frequency_Schedule_Days': [data1['freq'][i]] * p.days,
'NEXT_Date:YYYY-MM-DD': [0] * p.days,
"Grease_Grade": [data1["grade"][i]] * p.days,
"Point": [data1["point"][i]] * p.days,
"Stroke": [data1["stroke"][i]] * p.days,
"Quantity_Grease_In_Grams(in_one_stroke)": [data1["qty"][i]] * p.days, })
for j in range(0, len(df1)): # it will take lenght according to df1
# now adding date with freq and saving it in NEXT_Date
df1['NEXT_Date:YYYY-MM-DD'][j] = data1.date[i] + (timedelta(days=int(data1['freq'][i] * (j + 1))))
df2 = pd.concat([df1, df2])
# above it wil give all the dates like last date to end date
# therefore, here we have given start date so that it can only display from selected start date to end date
df3 = df2[df2['NEXT_Date:YYYY-MM-DD'] >= datetime.datetime.strptime(datee, '%Y-%m-%d')]
df3.sort_values(by='NEXT_Date:YYYY-MM-DD', inplace=True)
# here the seconds with the date will get cut off
df3['NEXT_Date:YYYY-MM-DD'] = df3['NEXT_Date:YYYY-MM-DD'].apply(lambda x: str(x.date()))
df3.reset_index(inplace=True)
df3.drop('index', axis=1, inplace=True)
con = sa.create_engine('sqlite:///C:\\Users\\Admin\\Downloads\\Python Scripts\\Next_Gresing_Date\\Grease.db')
df3.to_sql('Next_Datee', con, if_exists='append', index=False)
return df3.to_dict('records')
@app.callback(Output('output-of-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(contents, filename, date) for contents, filename, date in
zip(list_of_contents, list_of_names, list_of_dates)]
return children
if __name__ == "__main__":
app.run_server()
上面是我的代码
需求文本
Click==7.0
dash==1.9.0
dash-core-components==1.8.0
dash-html-components==1.0.2
dash-renderer==1.2.4
dash-table==4.6.0
Flask==1.1.1
Flask-Compress==1.4.0
Flask-SeaSurf==0.2.2
future==0.18.2
gunicorn==20.0.4
itsdangerous==1.1.0
Jinja2==2.11.1
MarkupSafe==1.1.1
numpy==1.18.1
pandas==1.0.1
plotly==4.5.0
python-dateutil==2.8.1
pytz==2019.3
retrying==1.3.3
six==1.14.0
SQLAlchemy==1.3.13
ua-parser==0.9.0
Werkzeug==1.0.0
这是我的 gitignore 文件
venv *.pyc .DS_Store .env
这是我的档案 网络：gunicorn 应用程序：服务器
【问题讨论】：
标签： python heroku plotly-dash