Skip to content

Commit b6bab17

Browse files
nooobcodernooobcoder
committed
Add lovely docker support
1 parent 1cc6abe commit b6bab17

File tree

13 files changed

+19857
-0
lines changed

13 files changed

+19857
-0
lines changed

‎Track 4_ReactJS_Web Development/Project/Docker/Dockerfile-flask.dockerfile

+6
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,6 @@
1+
FROM python:3.9.12
2+
COPY ./FlaskService/ /opt/app
3+
WORKDIR /opt/app
4+
RUN python -m pip install --upgrade pip
5+
RUN pip3 install -r requirements.txt
6+
CMD ["python3","integration.py"]

‎Track 4_ReactJS_Web Development/Project/Docker/Dockerfile-servlet.dockerfile

+3
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,3 @@
1+
FROM tomcat
2+
COPY ./Servlet/ROOT.war /usr/local/tomcat/webapps
3+
CMD ["catalina.sh", "run"]

‎Track 4_ReactJS_Web Development/Project/Docker/FlaskService/Final.csv

+9,682
Large diffs are not rendered by default.

‎Track 4_ReactJS_Web Development/Project/Docker/FlaskService/New_Bucket.py

+299
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,299 @@
1+
import pickle
2+
3+
import numpy as np
4+
import pandas as pd
5+
from sklearn.preprocessing import LabelEncoder
6+
7+
8+
class EncoderExt(object):
9+
def __init__(self):
10+
self.label_encoder = LabelEncoder()
11+
12+
def fit(self, data_list):
13+
self.label_encoder = self.label_encoder.fit(list(data_list) + ['Unknown'])
14+
self.classes_ = self.label_encoder.classes_
15+
return self
16+
17+
def transform(self, data_list):
18+
new_data_list = list(data_list)
19+
for unique_item in np.unique(data_list):
20+
if unique_item not in self.label_encoder.classes_:
21+
new_data_list = ['Unknown' if x == unique_item else x for x in new_data_list]
22+
return self.label_encoder.transform(new_data_list)
23+
24+
25+
def predict(nulldata):
26+
print(" Predict")
27+
# nulldata = pd.read_csv("data.csv")
28+
29+
nulldata["clear_date"] = pd.to_datetime(nulldata.clear_date)
30+
nulldata["posting_date"] = pd.to_datetime(nulldata.posting_date)
31+
nulldata["due_in_date"] = pd.to_datetime(nulldata.due_in_date)
32+
nulldata["baseline_create_date"] = pd.to_datetime(nulldata.baseline_create_date)
33+
34+
nulldata1 = nulldata.copy()
35+
36+
from sklearn.preprocessing import LabelEncoder
37+
business_codern = LabelEncoder()
38+
business_codern.fit(nulldata['business_code'])
39+
nulldata['business_code_enc'] = business_codern.transform(nulldata['business_code'])
40+
41+
# if : nulldata['cust_number'] = nulldata['cust_number'].str.replace('CCCA',"1").str.replace('CCU',
42+
# "2").str.replace('CC',"3").astype(int)
43+
nulldata['cust_number'] = nulldata['cust_number'].replace('CCCA02', "1").replace(['CCU001', 'CCU013', 'CCU002'],
44+
"2").replace(['CC3411', 'CC6000'],
45+
"3").astype(int)
46+
# nulldata['cust_number'] = nulldata['cust_number'].str.replace('CCCA',"1").astype(int)
47+
# nulldata['cust_number'] = nulldata['cust_number'].str.replace('CCU',"2").astype(int)
48+
# nulldata['cust_number'] = nulldata['cust_number'].str.replace('CC',"3").astype(int)
49+
50+
nulldata['day_of_cleardate'] = nulldata['clear_date'].dt.day
51+
nulldata['month_of_cleardate'] = nulldata['clear_date'].dt.month
52+
nulldata['year_of_cleardate'] = nulldata['clear_date'].dt.year
53+
54+
nulldata['day_of_postingdate'] = nulldata['posting_date'].dt.day
55+
nulldata['month_of_postingdate'] = nulldata['posting_date'].dt.month
56+
nulldata['year_of_postingdate'] = nulldata['posting_date'].dt.year
57+
58+
nulldata['day_of_due'] = nulldata['due_in_date'].dt.day
59+
nulldata['month_of_due'] = nulldata['due_in_date'].dt.month
60+
nulldata['year_of_due'] = nulldata['due_in_date'].dt.year
61+
62+
nulldata['day_of_createdate'] = nulldata['baseline_create_date'].dt.day
63+
nulldata['month_of_createdate'] = nulldata['baseline_create_date'].dt.month
64+
nulldata['year_of_createdate'] = nulldata['baseline_create_date'].dt.year
65+
66+
label_encoder = EncoderExt()
67+
label_encoder1 = EncoderExt()
68+
label_encoder1.fit(nulldata['cust_payment_terms'])
69+
label_encoder1.fit(nulldata['business_code'])
70+
label_encoder.fit(nulldata['name_customer'])
71+
nulldata['cust_payment_terms_enc'] = label_encoder1.transform(nulldata['cust_payment_terms'])
72+
nulldata['business_code_enc'] = label_encoder1.transform(nulldata['business_code'])
73+
nulldata['name_customer_enc'] = label_encoder.transform(nulldata['name_customer'])
74+
75+
nulldata.drop(
76+
['business_code', "baseline_create_date", "due_in_date", "posting_date", "name_customer", "clear_date",
77+
"cust_payment_terms"], axis=1, inplace=True)
78+
nulldata.drop(['day_of_cleardate', "month_of_cleardate", "year_of_cleardate"], axis=1, inplace=True)
79+
80+
nulldata2 = nulldata[['cust_number', 'buisness_year', 'doc_id', 'converted_usd',
81+
'business_code_enc', 'name_customer_enc', 'cust_payment_terms_enc',
82+
'day_of_postingdate', 'month_of_postingdate', 'year_of_postingdate',
83+
'day_of_createdate', 'month_of_createdate', 'year_of_createdate',
84+
'day_of_due', 'month_of_due', 'year_of_due']]
85+
86+
model = pickle.load(open("model.sav", 'rb'))
87+
88+
final_result = model.predict(nulldata2)
89+
90+
final_result = pd.Series(final_result, name='avg_delay')
91+
92+
nulldata1.reset_index(drop=True, inplace=True)
93+
Final = nulldata1.merge(final_result, on=nulldata.index)
94+
95+
Final['clear_date'] = pd.to_datetime(Final['due_in_date']) + pd.to_timedelta(Final['avg_delay'], unit='s')
96+
97+
Final['avg_delay'] = Final.apply(lambda row: row.avg_delay // (24 * 3600), axis=1)
98+
99+
bins = [0, 15, 30, 45, 60, 100]
100+
labels = ['0-15', '16-30', '31-45', '46-60', 'Greatar than 60']
101+
Final['Aging Bucket'] = pd.cut(Final['avg_delay'], bins=bins, labels=labels, right=False)
102+
103+
Final.drop(['key_0', "avg_delay"], axis=1, inplace=True)
104+
Final.to_csv("Final.csv")
105+
Final1 = Final[['doc_id', 'Aging Bucket']].copy()
106+
doc = Final1["doc_id"].tolist()
107+
aging = Final1["Aging Bucket"].tolist()
108+
a = []
109+
for i in range(len(doc)):
110+
a.append({"doc_id": doc[i], "aging_bucket": str(aging[i])})
111+
112+
return a
113+
114+
115+
def doc_id_bucket(doc_list):
116+
print(" Predict")
117+
118+
nulldata = pd.read_csv("nulldata.csv", index_col=0)
119+
# nulldata = pd.read_csv("nulldata.csv")
120+
121+
nulldata["clear_date"] = pd.to_datetime(nulldata.clear_date)
122+
nulldata["posting_date"] = pd.to_datetime(nulldata.posting_date)
123+
nulldata["due_in_date"] = pd.to_datetime(nulldata.due_in_date)
124+
nulldata["baseline_create_date"] = pd.to_datetime(nulldata.baseline_create_date)
125+
126+
nulldata1 = nulldata.copy()
127+
128+
from sklearn.preprocessing import LabelEncoder
129+
business_codern = LabelEncoder()
130+
business_codern.fit(nulldata['business_code'])
131+
nulldata['business_code_enc'] = business_codern.transform(nulldata['business_code'])
132+
133+
# if : nulldata['cust_number'] = nulldata['cust_number'].str.replace('CCCA',"1").str.replace('CCU',
134+
# "2").str.replace('CC',"3").astype(int)
135+
nulldata['cust_number'] = nulldata['cust_number'].replace('CCCA02', "1").replace(['CCU001', 'CCU013', 'CCU002'],
136+
"2").replace(['CC3411', 'CC6000'],
137+
"3").astype(int)
138+
# nulldata['cust_number'] = nulldata['cust_number'].str.replace('CCCA',"1").astype(int)
139+
# nulldata['cust_number'] = nulldata['cust_number'].str.replace('CCU',"2").astype(int)
140+
# nulldata['cust_number'] = nulldata['cust_number'].str.replace('CC',"3").astype(int)
141+
142+
nulldata['day_of_cleardate'] = nulldata['clear_date'].dt.day
143+
nulldata['month_of_cleardate'] = nulldata['clear_date'].dt.month
144+
nulldata['year_of_cleardate'] = nulldata['clear_date'].dt.year
145+
146+
nulldata['day_of_postingdate'] = nulldata['posting_date'].dt.day
147+
nulldata['month_of_postingdate'] = nulldata['posting_date'].dt.month
148+
nulldata['year_of_postingdate'] = nulldata['posting_date'].dt.year
149+
150+
nulldata['day_of_due'] = nulldata['due_in_date'].dt.day
151+
nulldata['month_of_due'] = nulldata['due_in_date'].dt.month
152+
nulldata['year_of_due'] = nulldata['due_in_date'].dt.year
153+
154+
nulldata['day_of_createdate'] = nulldata['baseline_create_date'].dt.day
155+
nulldata['month_of_createdate'] = nulldata['baseline_create_date'].dt.month
156+
nulldata['year_of_createdate'] = nulldata['baseline_create_date'].dt.year
157+
158+
label_encoder = EncoderExt()
159+
label_encoder1 = EncoderExt()
160+
label_encoder1.fit(nulldata['cust_payment_terms'])
161+
label_encoder1.fit(nulldata['business_code'])
162+
label_encoder.fit(nulldata['name_customer'])
163+
nulldata['cust_payment_terms_enc'] = label_encoder1.transform(nulldata['cust_payment_terms'])
164+
nulldata['business_code_enc'] = label_encoder1.transform(nulldata['business_code'])
165+
nulldata['name_customer_enc'] = label_encoder.transform(nulldata['name_customer'])
166+
167+
nulldata.drop(
168+
['business_code', "baseline_create_date", "due_in_date", "posting_date", "name_customer", "clear_date",
169+
"cust_payment_terms"], axis=1, inplace=True)
170+
nulldata.drop(['day_of_cleardate', "month_of_cleardate", "year_of_cleardate"], axis=1, inplace=True)
171+
172+
nulldata2 = nulldata[['cust_number', 'buisness_year', 'doc_id', 'converted_usd',
173+
'business_code_enc', 'name_customer_enc', 'cust_payment_terms_enc',
174+
'day_of_postingdate', 'month_of_postingdate', 'year_of_postingdate',
175+
'day_of_createdate', 'month_of_createdate', 'year_of_createdate',
176+
'day_of_due', 'month_of_due', 'year_of_due']]
177+
178+
model = pickle.load(open("model.sav", 'rb'))
179+
180+
final_result = model.predict(nulldata2)
181+
182+
final_result = pd.Series(final_result, name='avg_delay')
183+
184+
nulldata1.reset_index(drop=True, inplace=True)
185+
Final = nulldata1.merge(final_result, on=nulldata.index)
186+
187+
Final['clear_date'] = pd.to_datetime(Final['due_in_date']) + pd.to_timedelta(Final['avg_delay'], unit='s')
188+
189+
Final['avg_delay'] = Final.apply(lambda row: row.avg_delay // (24 * 3600), axis=1)
190+
191+
bins = [0, 15, 30, 45, 60, 100]
192+
labels = ['0-15', '16-30', '31-45', '46-60', 'Greatar than 60']
193+
Final['Aging Bucket'] = pd.cut(Final['avg_delay'], bins=bins, labels=labels, right=False)
194+
195+
Final.drop(['key_0', "avg_delay"], axis=1, inplace=True)
196+
Final.to_csv("Final.csv")
197+
Final1 = Final[['doc_id', 'Aging Bucket']].copy()
198+
doc = Final1["doc_id"].tolist()
199+
aging = Final1["Aging Bucket"].tolist()
200+
a = []
201+
for i in range(len(doc)):
202+
if doc[i] in doc_list:
203+
a.append({"doc_id": str(doc[i]), "aging_bucket": str(aging[i])})
204+
205+
return a
206+
207+
208+
def alll():
209+
print(" Predict")
210+
211+
nulldata = pd.read_csv("nulldata.csv", index_col=0)
212+
# nulldata = pd.read_csv("nulldata.csv")
213+
214+
nulldata["clear_date"] = pd.to_datetime(nulldata.clear_date)
215+
nulldata["posting_date"] = pd.to_datetime(nulldata.posting_date)
216+
nulldata["due_in_date"] = pd.to_datetime(nulldata.due_in_date)
217+
nulldata["baseline_create_date"] = pd.to_datetime(nulldata.baseline_create_date)
218+
219+
nulldata1 = nulldata.copy()
220+
221+
from sklearn.preprocessing import LabelEncoder
222+
business_codern = LabelEncoder()
223+
business_codern.fit(nulldata['business_code'])
224+
nulldata['business_code_enc'] = business_codern.transform(nulldata['business_code'])
225+
226+
# if : nulldata['cust_number'] = nulldata['cust_number'].str.replace('CCCA',"1").str.replace('CCU',
227+
# "2").str.replace('CC',"3").astype(int)
228+
nulldata['cust_number'] = nulldata['cust_number'].replace('CCCA02', "1").replace(['CCU001', 'CCU013', 'CCU002'],
229+
"2").replace(['CC3411', 'CC6000'],
230+
"3").astype(int)
231+
# nulldata['cust_number'] = nulldata['cust_number'].str.replace('CCCA',"1").astype(int)
232+
# nulldata['cust_number'] = nulldata['cust_number'].str.replace('CCU',"2").astype(int)
233+
# nulldata['cust_number'] = nulldata['cust_number'].str.replace('CC',"3").astype(int)
234+
235+
nulldata['day_of_cleardate'] = nulldata['clear_date'].dt.day
236+
nulldata['month_of_cleardate'] = nulldata['clear_date'].dt.month
237+
nulldata['year_of_cleardate'] = nulldata['clear_date'].dt.year
238+
239+
nulldata['day_of_postingdate'] = nulldata['posting_date'].dt.day
240+
nulldata['month_of_postingdate'] = nulldata['posting_date'].dt.month
241+
nulldata['year_of_postingdate'] = nulldata['posting_date'].dt.year
242+
243+
nulldata['day_of_due'] = nulldata['due_in_date'].dt.day
244+
nulldata['month_of_due'] = nulldata['due_in_date'].dt.month
245+
nulldata['year_of_due'] = nulldata['due_in_date'].dt.year
246+
247+
nulldata['day_of_createdate'] = nulldata['baseline_create_date'].dt.day
248+
nulldata['month_of_createdate'] = nulldata['baseline_create_date'].dt.month
249+
nulldata['year_of_createdate'] = nulldata['baseline_create_date'].dt.year
250+
251+
label_encoder = EncoderExt()
252+
label_encoder1 = EncoderExt()
253+
label_encoder1.fit(nulldata['cust_payment_terms'])
254+
label_encoder1.fit(nulldata['business_code'])
255+
label_encoder.fit(nulldata['name_customer'])
256+
nulldata['cust_payment_terms_enc'] = label_encoder1.transform(nulldata['cust_payment_terms'])
257+
nulldata['business_code_enc'] = label_encoder1.transform(nulldata['business_code'])
258+
nulldata['name_customer_enc'] = label_encoder.transform(nulldata['name_customer'])
259+
260+
nulldata.drop(
261+
["business_code", "baseline_create_date", "due_in_date", "posting_date", "name_customer", "clear_date",
262+
"cust_payment_terms", 'day_of_cleardate', "month_of_cleardate", "year_of_cleardate"], axis=1,
263+
inplace=True)
264+
265+
nulldata2 = nulldata[['cust_number', 'buisness_year', 'doc_id', 'converted_usd',
266+
'business_code_enc', 'name_customer_enc', 'cust_payment_terms_enc',
267+
'day_of_postingdate', 'month_of_postingdate', 'year_of_postingdate',
268+
'day_of_createdate', 'month_of_createdate', 'year_of_createdate',
269+
'day_of_due', 'month_of_due', 'year_of_due']]
270+
271+
model = pickle.load(open("model.sav", 'rb'))
272+
273+
final_result = model.predict(nulldata2)
274+
275+
final_result = pd.Series(final_result, name='avg_delay')
276+
277+
nulldata1.reset_index(drop=True, inplace=True)
278+
279+
Final = nulldata1.merge(final_result, on=nulldata.index)
280+
281+
Final['clear_date'] = pd.to_datetime(Final['due_in_date']) + pd.to_timedelta(Final['avg_delay'], unit='s')
282+
283+
Final['avg_delay'] = Final.apply(lambda row: row.avg_delay // (24 * 3600), axis=1)
284+
285+
bins = [0, 15, 30, 45, 60, 100]
286+
labels = ['0-15', '16-30', '31-45', '46-60', 'Greatar than 60']
287+
Final['Aging Bucket'] = pd.cut(Final['avg_delay'], bins=bins, labels=labels, right=False)
288+
289+
Final.drop(['key_0', "avg_delay"], axis=1, inplace=True)
290+
Final.to_csv("Final.csv")
291+
Final1 = Final[['doc_id', 'Aging Bucket']].copy()
292+
doc = Final1["doc_id"].tolist()
293+
aging = Final1["Aging Bucket"].tolist()
294+
a = []
295+
for i in range(len(doc)):
296+
# if doc[i] in doc_list:
297+
a.append({"doc_id": doc[i], "aging_bucket": str(aging[i])})
298+
299+
return a

‎Track 4_ReactJS_Web Development/Project/Docker/FlaskService/integration.py

+86
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,86 @@
1+
import json
2+
3+
from flask import Flask, redirect, url_for, render_template, request, jsonify, make_response
4+
import New_Bucket
5+
import pandas as pd
6+
from flask_cors import CORS
7+
8+
data = pd.DataFrame()
9+
app = Flask(__name__)
10+
CORS(app)
11+
12+
13+
@app.route("/", methods=["POST", "GET"])
14+
def home():
15+
if request.method == "POST":
16+
business_code = request.form["business_code"]
17+
cust_number = request.form['cust_number']
18+
name_customer = request.form['name_customer']
19+
clear_date = request.form['clear_date']
20+
buisness_year = int(request.form['buisness_year'])
21+
doc_id = int(request.form['doc_id'])
22+
posting_date = request.form['posting_date']
23+
due_in_date = request.form['due_in_date']
24+
baseline_create_date = request.form['baseline_create_date']
25+
cust_payment_terms = request.form['cust_payment_terms']
26+
converted_usd = float(request.form['converted_usd'])
27+
28+
data['business_code'] = [business_code]
29+
data['cust_number'] = [cust_number]
30+
data['name_customer'] = [name_customer]
31+
data['clear_date'] = [clear_date]
32+
data['buisness_year'] = [buisness_year]
33+
data['doc_id'] = [doc_id]
34+
data['posting_date'] = [posting_date]
35+
data['due_in_date'] = [due_in_date]
36+
data['baseline_create_date'] = [baseline_create_date]
37+
data['cust_payment_terms'] = [cust_payment_terms]
38+
data['converted_usd'] = [converted_usd]
39+
40+
41+
response = make_response(jsonify(New_Bucket.predict(data)), 200)
42+
response.headers["Content-Type"] = "application/json"
43+
return response
44+
else:
45+
return render_template("index1.html")
46+
47+
48+
@app.route("/get_prediction", methods=["GET", 'POST'])
49+
def get_prediction():
50+
if request.method == "POST":
51+
doc_id_list = list(request.get_json(force=True)["data"])
52+
print(doc_id_list)
53+
response = make_response(jsonify(New_Bucket.doc_id_bucket(doc_id_list)), 200)
54+
response.headers["Access-Control-Allow-Origin"] = "*"
55+
response.headers["Content-Type"] = "application/json"
56+
return response
57+
else:
58+
return render_template("index2.html")
59+
60+
61+
@app.route("/all", methods=["GET", 'POST'])
62+
def alll():
63+
if request.method == "POST":
64+
response = make_response(jsonify(New_Bucket.alll()), 200)
65+
response.headers["Content-Type"] = "application/json"
66+
return response
67+
else:
68+
return render_template("index3.html")
69+
70+
71+
@app.route("/login", methods=["POST", "GET"])
72+
def login():
73+
if request.method == "POST":
74+
user = request.form["nm"]
75+
return redirect(url_for("user", usr=user))
76+
else:
77+
return render_template("login.html")
78+
79+
80+
@app.route("/<usr>")
81+
def user(usr):
82+
return f"<h1>{usr}</h1>"
83+
84+
85+
if __name__ == "__main__":
86+
app.run(host='0.0.0.0')

‎Track 4_ReactJS_Web Development/Project/Docker/FlaskService/model.sav

499 KB
Binary file not shown.

0 commit comments

Comments
 (0)