def ModelLoad(self):
"""
模型载入
Raises:
Exception: [description]
"""
if not os.path.exists(self.save_file_path):
raise Exception("参数 save_file_path 指向的文件路径不存在，请检查.")
self.model = Model.fromFile(self.save_file_path)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
config = Config().app_config
if "pmml" in config:
pmml_config = config["pmml"]
if "path" in pmml_config:
pmml_file_path = pmml_config["path"]
if os.path.exists(pmml_file_path):
try:
self.model = Model.fromFile(pmml_file_path)
except Exception as exp:
raise ServerError(
f"Config file was loaded however PMML config couldn't be parsed correctly with error: {exp}"
)
else:
raise NotFoundError(
f"file: {pmml_file_path} could not be loaded, as it doesn't exist"
)
def is_support(self):
try:
from pypmml import Model
model_content = self.model
if isinstance(model_content, (bytes, bytearray)):
model_content = model_content.decode('utf-8')
if isinstance(model_content, str):
# Check if a file path
if os.path.exists(model_content):
self.pmml_model = Model.fromFile(model_content)
else:
self.pmml_model = Model.fromString(model_content)
return True
else:
Model.close()
return False
except Exception as e:
return False
def run(self):
exec_env = ExecutionEnvironment.get_execution_environment()
exec_env.set_parallelism(1)
t_config = TableConfig()
t_env = StreamTableEnvironment.create(exec_env, t_config)
t_env.connect(FileSystem().path('/tmp/input')) \
.with_format(OldCsv()
.field('word', DataTypes.STRING())) \
.with_schema(Schema()
.field('word', DataTypes.STRING())) \
.create_temporary_table('mySource')
t_env.connect(FileSystem().path('/tmp/output')) \
.with_format(OldCsv()
.field_delimiter('\t')
.field('word', DataTypes.STRING())
.field('count', DataTypes.BIGINT())) \
.with_schema(Schema()
.field('word', DataTypes.STRING())
.field('count', DataTypes.BIGINT())) \
.create_temporary_table('mySink')
model = Model.fromFile('./../batch_ml/model.pmml')
t_env.from_path('mySource') \
.group_by('word') \
.select('word, count(1)') \
.insert_into('mySink')
t_env.execute("tutorial_job")
self.read_data()
result = model.predict({
"Sepal_Length": 5.1,
"Sepal_Width": 3.5,
"Petal_Length": 1.4,
"Petal_Width": 0.2
})
# https://pypi.org/project/pypmml/
from pypmml import Model
# The model is from http://dmg.org/pmml/pmml_examples/KNIME_PMML_4.1_Examples/single_iris_dectree.xml
model = Model.fromFile('pmmlFiles\single_iris_dectree.xml')
result = model.predict({
"Sepal_Length": 5.1,
"Sepal_Width": 3.5,
"Petal_Length": 1.4,
"Petal_Width": 0.2
})
print("r1", result)
result = model.predict({
'sepal_length': 5.1,
'sepal_width': 3.5,
'petal_length': 1.4,
'petal_width': 0.2
})
{
'probability_Iris-setosa': 1.0,
'probability_Iris-versicolor': 0.0,
'probability': 1.0,
'predicted_class': 'Iris-setosa',
'probability_Iris-virginica': 0.0,
'node_id': '1'
}
print("r2", result)
def test_from_file(self):
# The model is from here: http://dmg.org/pmml/pmml_examples/KNIME_PMML_4.1_Examples/single_iris_dectree.xml
model = Model.fromFile('./resources/models/single_iris_dectree.xml')
self.assertEqual(model.version, '4.1')
app = model.header.application
self.assertEqual(app.name, 'KNIME')
self.assertEqual(app.version, '2.8.0')
self.assertEqual(model.modelElement, 'TreeModel')
self.assertEqual(model.functionName, 'classification')
self.assertEqual(model.modelName, 'DecisionTree')
self.assertEqual(model.algorithmName, None)
self.assertEqual(
model.inputNames,
['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])
inputs = model.inputFields
self.assertEqual(len(inputs), 4)
self.assertEqual(inputs[0].name, 'sepal_length')
self.assertEqual(inputs[0].dataType, 'double')
self.assertEqual(inputs[0].opType, 'continuous')
self.assertEqual(model.targetNames, ['class'])
targets = model.targetFields
self.assertEqual(len(targets), 1)
self.assertEqual(targets[0].name, 'class')
self.assertEqual(targets[0].dataType, 'string')
self.assertEqual(targets[0].opType, 'nominal')
self.assertEqual(model.classes,
['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'])
self.assertEqual(model.outputNames, [
'predicted_class', 'probability', 'probability_Iris-setosa',
'probability_Iris-versicolor', 'probability_Iris-virginica',
'node_id'
])
outputs = model.outputFields
self.assertEqual(outputs[0].feature, 'predictedValue')
self.assertEqual(outputs[1].feature, 'probability')
self.assertEqual(outputs[1].value, None)
self.assertEqual(outputs[2].feature, 'probability')
self.assertEqual(outputs[2].value, 'Iris-setosa')
self.assertEqual(outputs[3].feature, 'probability')
self.assertEqual(outputs[3].value, 'Iris-versicolor')
self.assertEqual(outputs[4].feature, 'probability')
self.assertEqual(outputs[4].value, 'Iris-virginica')
self.assertEqual(outputs[5].feature, 'entityId')
# Data in dict
result = model.predict({
'sepal_length': 5.1,
'sepal_width': 3.5,
'petal_length': 1.4,
'petal_width': 0.2
})
self.assertEqual(result['predicted_class'], 'Iris-setosa')
self.assertEqual(result['probability'], 1.0)
self.assertEqual(result['node_id'], '1')
result = model.predict({
'sepal_length': 7,
'sepal_width': 3.2,
'petal_length': 4.7,
'petal_width': 1.4
})
self.assertEqual(result['predicted_class'], 'Iris-versicolor')
self.assertEqual(result['probability'], 0.9074074074074074)
self.assertEqual(result['node_id'], '3')
# Data in json
result = model.predict(
'[{"sepal_length": 5.1, "sepal_width": 3.5, "petal_length": 1.4, "petal_width": 0.2}]'
)
self.assertEqual(
result,
'[{"node_id":"1","probability_Iris-setosa":1.0,"predicted_class":"Iris-setosa","probability_Iris-virginica":0.0,"probability_Iris-versicolor":0.0,"probability":1.0}]'
)
result = model.predict(
'{"columns": ["sepal_length", "sepal_width", "petal_length", "petal_width"], "data": [[7, 3.2, 4.7, 1.4]]}'
)
self.assertEqual(
result,
'{"columns":["predicted_class","probability","probability_Iris-setosa","probability_Iris-versicolor","probability_Iris-virginica","node_id"],"data":[["Iris-versicolor",0.9074074074074074,0.0,0.9074074074074074,0.09259259259259259,"3"]]}'
)
# Data in list
result = model.predict([5.1, 3.5, 1.4, 0.2])
self.assertEqual(result[0], 'Iris-setosa')
self.assertEqual(result[1], 1.0)
self.assertEqual(result[2], 1.0)
self.assertEqual(result[3], 0.0)
self.assertEqual(result[4], 0.0)
self.assertEqual(result[5], '1')
# Data in list of list
result = model.predict([[5.1, 3.5, 1.4, 0.2], [7, 3.2, 4.7, 1.4]])
self.assertEqual(len(result), 2)
self.assertEqual(result[0][0], 'Iris-setosa')
self.assertEqual(result[0][1], 1.0)
self.assertEqual(result[0][2], 1.0)
self.assertEqual(result[0][3], 0.0)
self.assertEqual(result[0][4], 0.0)
self.assertEqual(result[0][5], '1')
self.assertEqual(result[1][0], 'Iris-versicolor')
self.assertEqual(result[1][1], 0.9074074074074074)
self.assertEqual(result[1][2], 0.0)
self.assertEqual(result[1][3], 0.9074074074074074)
self.assertEqual(result[1][4], 0.09259259259259259)
self.assertEqual(result[1][5], '3')
# Data in numpy
# Shutdown the gateway
Model.close()
random_state=seed)
pipeline = Pipeline([('scaling', StandardScaler()),
('xgb', XGBClassifier(n_estimators=5, seed=seed))])
pipeline.fit(X_train, y_train)
y_pred = pipeline.predict(X_test)
y_pred_proba = pipeline.predict_proba(X_test)
import pickle
import numpy as np
d = pickle.dumps(pipeline)
saved_pipeline = pickle.loads(d)
y_pred_saved = saved_pipeline.predict(X_test)
y_pred_proba_saved = saved_pipeline.predict_proba(X_test)
assert np.array_equal(y_pred, y_pred_saved), "Not equal after saved"
assert np.array_equal(y_pred_proba_saved,
y_pred_proba), "Not equal after saved"
from nyoka import xgboost_to_pmml
xgboost_to_pmml(saved_pipeline, features, target, "xgb-iris.pmml")
from pypmml import Model
model = Model.fromFile("xgb-iris.pmml")
y_pred_pmml = model.predict(X_test)
assert np.array_equal(
y_pred, y_pred_pmml["predicted_Species"]), "Not equal after saved"
def init():
global model_tag
global pmmlFields
# 下载模型
download_model.download_model(download_model_zip_path, unzip_path)
try:
#如果模型路径下存在pmml文件，那么直接加载pmml模型
#pmml文件压缩包的结构是model/xxx.pmml文件
#因为pmml文件结构的特殊性，所以解压函数要修改代码
model_path_childs = os.listdir(local_model_path)
logging.info(f'模型文件夹下的文件有:{model_path_childs}')
for child in model_path_childs:
if child.endswith(".pmml"):
full_path = os.path.join(local_model_path, child)
break
#或者是保存在model/model/part-00000中的pmml模型
elif child == "model":
for file in os.listdir(os.path.join(local_model_path,"model")):
if file.startswith("part"):
full_path = local_model_path + "/model/" + file
break
logging.info(f'获取到的模型路径是:{full_path}')
print("模型大小是:",os.path.getsize(full_path))
global pmmlModel
pmmlModel = loadPmml.fromFile(full_path)
pmmlFields = parse_xml(full_path)
logging.info(f'成功加载pmml模型')
model_tag = 1
except:
logging.info("从pmml模型的加载处理中跳出")
# 获取模型路径
get_model_path(local_model_path)
# 加载模型
try:
global model
logging.info("尝试加载PipelineModel")
model = PipelineModel.load(local_model_path)#加载模型
model_tag = 2
except:
try:
# H2O模型必须走这里
from pysparkling.ml import H2OMOJOSettings, H2OMOJOModel
logging.info("从加载PipelineModel的try中跳出")
print("在except的try中尝试加载H2OMOJOModel")
settings = H2OMOJOSettings(withDetailedPredictionCol=True)
model = H2OMOJOModel.createFromMojo(local_model_path + '/mojo_model', settings)
model_tag = 3
except:
global pipeline_model
print("从加载H2OMOJOModel的try中跳出")
print("尝试加载XGBModel")
# model = XGBoostClassificationModel.load(local_model_path)
model = load_xgb_model(local_model_path,m_type='XGBoostClassificationModel')
if not model:
logging.error('XGBoostClassificationModel没有加载成功')
pipeline_model = load_xgb_model(local_model_path, "PipelineModel")
if not pipeline_model:
logging.error('XGB需要的pipelinemodel没有加载成功')
logging.error(pipeline_model)
model_tag = 4
global final_transform_json_path
final_transform_json_path = get_jsonfile_fullname()
# 读取json，model_json: 模型中存储的json
with open(final_transform_json_path, encoding='utf-8') as f:
global model_json
model_json = json.load(f)
@author: pedro
"""
import pandas as pd
#from rest_framework import status
#from rest_framework.response import Response
from Propythia_ML_PMML import MachineLearning_PMML
#from sklearn2pmml import sklearn2pmml
from pypmml import Model
seq = "STGQKSIVAYTMSLGAENSIAYANNSIAIPTNFSISVTTEVMPVSMAKTAVDCTMYICGDSLECSNLLLQYGSFCTQLNRALTGIAIEQDKNTQEVFAQVKQMYKTPAIKDFGGFNFSQILPDPSKPTKRSFIEDLLFNKVTLADAGFMKQYGDCLGDVSARDLICAQKFNGLTVLPPLLTDEMVAAYTAALVSGTATAGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQESLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPSQEKNFTTAPAICHEGKAYFPREGVFVSNGTSWFITQRNFYSPQLITTDNTFVSGNCDVVIGIINNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYVWLGFIAGLIAIVMVTILLCCMTSCCSCLKGACSCGSCCKFDEDDSEPVLKGVKLHYT"
gap = 1
window_size = 15
model = Model.fromFile('Model_pmml_svm_28052020.pmml')
print(model)
dataset = pd.read_csv(r'dataset3_all_svc.csv', delimiter=',')
x_original = dataset.loc[:, dataset.columns != 'labels']
labels = dataset['labels']
# create Machine learning object
# ml = MachineLearning(x_original, labels, classes=['non_vfp', 'vfp'])
ml = MachineLearning_PMML(x_original, labels, classes=['non_vfp', 'vfp'])
result = ml.predict_window(model,
seq=seq,
x=None,
def get_app():
BackEndApp = Flask(__name__)
cors = CORS(BackEndApp, resources={r"/*": {"origins": "*"}})
mydb = mysql.connector.connect(host="127.0.0.1",
user="******",
passwd="",
database="cactus",
raise_on_warnings=True)
mycursor = mydb.cursor(dictionary=True)
mycursor1 = mydb.cursor()
mycursor.execute("SELECT * FROM Model_Data WHERE Status = '1'")
Model2 = mycursor.fetchone()
model = Model.fromFile("ModelGBT/" + Model2['Name'])
Accuracy = float(Model2['Accuracy'])
print("LoadModel...({0})".format(Model2['Name']))
with open('dataFT.json', encoding="utf8") as json_file:
jsonFTData = json.load(json_file)
print("====Server Start====")
@BackEndApp.route('/')
def hello():
return '{"result" : "Hello","No" : 1}'
@BackEndApp.route('/Model/Accuracy/')
def ModelAccuracy():
data = {"Accuracy": Accuracy}
return json.dumps(data)
@BackEndApp.route('/countCactusName/')
def countCactusName():
sql = "SELECT Result, COUNT(Result) as count_result FROM Predict_Data GROUP BY Result ORDER BY COUNT(Result) DESC"
mycursor.execute(sql)
data = mycursor.fetchall()
result = {"countCactusName": data}
return json.dumps(result)
@BackEndApp.route('/countDataStatus/')
def countDataStatus():
sql = "SELECT DataStatus, COUNT(DataStatus) as count_datastatus FROM Predict_Data GROUP BY DataStatus "
mycursor.execute(sql)
data = mycursor.fetchall()
result = {"countDataStatus": data}
return json.dumps(result)
@BackEndApp.route('/countUserData/')
def countUserData():
sql = "SELECT EmV, COUNT(EmV) as count_countUserData FROM User_data GROUP BY EmV "
mycursor.execute(sql)
data = mycursor.fetchall()
result = {"countUserData": data}
return json.dumps(result)
@BackEndApp.route('/checkServer/')
def checkStatus():
sql = "SELECT * FROM Sys_Status"
mycursor.execute(sql)
data = mycursor.fetchall()
status = {"status_server": data}
return json.dumps(status)
@BackEndApp.route("/userAll/", methods=['GET'])
def userAll():
sql = "SELECT * FROM User_Data WHERE role = 'M'"
mycursor.execute(sql)
data = mycursor.fetchall()
user = {"user": data}
return json.dumps(user)
@BackEndApp.route("/userAll/search/Email", methods=['GET'])
def userAll_Email():
sql = "SELECT * FROM User_Data WHERE role = 'M'AND Email LIKE '%{0}%'".format(
request.args.get("email"))
mycursor.execute(sql)
data = mycursor.fetchall()
user = {"user": data}
return json.dumps(user)
@BackEndApp.route("/userAll/search/Name", methods=['GET'])
def userAll_Name():
sql = "SELECT * FROM User_Data WHERE role = 'M'AND Name LIKE '%{0}%'".format(
request.args.get("name"))
mycursor.execute(sql)
data = mycursor.fetchall()
user = {"user": data}
return json.dumps(user)
@BackEndApp.route("/userAll/search/Surname", methods=['GET'])
def userAll_Surname():
sql = "SELECT * FROM User_Data WHERE role = 'M'AND Surname LIKE '%{0}%'".format(
request.args.get("surname"))
mycursor.execute(sql)
data = mycursor.fetchall()
user = {"user": data}
return json.dumps(user)
@BackEndApp.route("/history/all/", methods=['POST'])
def historyAll():
resp = request.get_json()
sort = resp['sort']
sortBy = resp['sortBy']
memberId = int(resp['id'])
cactusName = str(resp['CactusName'])
if memberId > 0:
if cactusName == 'null':
if sortBy == 'id':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} ORDER BY Transaction_id".format(
memberId)
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} ORDER BY Transaction_id DESC".format(
memberId)
elif sortBy == 'result':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} ORDER BY Result".format(
memberId)
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} ORDER BY Result DESC".format(
memberId)
elif sortBy == 'time':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} ORDER BY Timestamp".format(
memberId)
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} ORDER BY Timestamp DESC".format(
memberId)
else:
if sortBy == 'id':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} AND Result LIKE '%{1}%' ORDER BY Transaction_id".format(
memberId, cactusName)
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} AND Result LIKE '%{1}%' ORDER BY Transaction_id DESC".format(
memberId, cactusName)
elif sortBy == 'result':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} AND Result LIKE '%{1}%' ORDER BY Result".format(
memberId, cactusName)
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} AND Result LIKE '%{1}%' ORDER BY Result DESC".format(
memberId, cactusName)
elif sortBy == 'time':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} AND Result LIKE '%{1}%' ORDER BY Timestamp".format(
memberId, cactusName)
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} AND Result LIKE '%{1}%' ORDER BY Timestamp DESC".format(
memberId, cactusName)
else:
if cactusName == 'null':
if sortBy == 'id':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data ORDER BY Transaction_id"
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data ORDER BY Transaction_id DESC"
elif sortBy == 'result':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data ORDER BY Result"
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data ORDER BY Result DESC"
elif sortBy == 'time':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data ORDER BY Timestamp"
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data ORDER BY Timestamp DESC"
else:
if sortBy == 'id':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data WHERE Result LIKE '%{0}%' ORDER BY Transaction_id".format(
cactusName)
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data WHERE Result LIKE '%{0}%' ORDER BY Transaction_id DESC".format(
cactusName)
elif sortBy == 'result':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data WHERE Result LIKE '%{0}%' ORDER BY Result".format(
cactusName)
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data WHERE Result LIKE '%{0}%' ORDER BY Result DESC".format(
cactusName)
elif sortBy == 'time':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data WHERE Result LIKE '%{0}%' ORDER BY Timestamp".format(
cactusName)
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data WHERE Result LIKE '%{0}%' ORDER BY Timestamp DESC".format(
cactusName)
mycursor.execute(sql)
data = mycursor.fetchall()
for i in range(len(data)):
FT = []
for j in range(13):
FT.append(jsonFTData['DataFT'][j]['FtList'][
int(data[i]['Feature'][j]) - 1]['Name'])
data[i]['Feature'] = FT
predict = {"predict": data}
return json.dumps(predict)
@BackEndApp.route("/history/", methods=['POST'])
def history():
resp = request.get_json()
sort = resp['sort']
sortBy = resp['sortBy']
memberId = int(resp['id'])
cactusName = str(resp['CactusName'])
if cactusName == 'null':
if sortBy == 'id':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} ORDER BY Transaction_id".format(
memberId)
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} ORDER BY Transaction_id DESC".format(
memberId)
elif sortBy == 'result':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} ORDER BY Result".format(
memberId)
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} ORDER BY Result DESC".format(
memberId)
elif sortBy == 'time':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} ORDER BY Timestamp".format(
memberId)
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} ORDER BY Timestamp DESC".format(
memberId)
else:
if sortBy == 'id':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} AND Result LIKE '%{1}%' ORDER BY Transaction_id".format(
memberId, cactusName)
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} AND Result LIKE '%{1}%' ORDER BY Transaction_id DESC".format(
memberId, cactusName)
elif sortBy == 'result':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} AND Result LIKE '%{1}%' ORDER BY Result".format(
memberId, cactusName)
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} AND Result LIKE '%{1}%' ORDER BY Result DESC".format(
memberId, cactusName)
elif sortBy == 'time':
if sort == 'asc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} AND Result LIKE '%{1}%' ORDER BY Timestamp".format(
memberId, cactusName)
elif sort == 'desc':
sql = "SELECT * FROM Predict_Data WHERE Member_code = {0} AND Result LIKE '%{1}%' ORDER BY Timestamp DESC".format(
memberId, cactusName)
mycursor.execute(sql)
data = mycursor.fetchall()
for i in range(len(data)):
FT = []
for j in range(13):
FT.append(jsonFTData['DataFT'][j]['FtList'][
int(data[i]['Feature'][j]) - 1]['Name'])
data[i]['Feature'] = FT
predict = {"predict": data}
return json.dumps(predict)
@BackEndApp.route('/predict-save/note/', methods=['POST'])
def predictSavewithNote():
resp = request.get_json()
code = resp['memberCode']
ft = resp['ft']
result = resp['result']
note = resp['note']
timestamp = str(int(time.time()))
mycursor1.execute(
"INSERT INTO Predict_Data (Member_code,Feature,Result,Timestamp,DataStatus,note) VALUES (%s,%s,%s,%s,%s,%s)",
(code, ft, result, timestamp, '1', note))
mydb.commit()
return ('{"Status" : 1}')
@BackEndApp.route('/predict-save/', methods=['POST'])
def predictSave():
resp = request.get_json()
code = resp['memberCode']
ft = resp['ft']
result = resp['result']
timestamp = str(int(time.time()))
mycursor1.execute(
"INSERT INTO Predict_Data (Member_code,Feature,Result,Timestamp,DataStatus) VALUES (%s,%s,%s,%s,%s)",
(code, ft, result, timestamp, '1'))
mydb.commit()
minPreProcess = 500
minTraining = 500
mycursor.execute(
"SELECT COUNT(*) FROM Predict_Data WHERE DataStatus = '1' ")
dataStatus1 = mycursor.fetchone()
mycursor.execute(
"SELECT COUNT(*) FROM Predict_Data WHERE DataStatus = '2' ")
dataStatus2 = mycursor.fetchone()
mycursor.execute('SELECT * FROM Sys_Status WHERE Name = "preData" ')
preDataResult = mycursor.fetchone()
mycursor.execute('SELECT * FROM Sys_Status WHERE Name = "Training" ')
trainingResult = mycursor.fetchone()
predataStatus = int(preDataResult['Status'])
trainingStatus = int(trainingResult['Status'])
countData1 = int(dataStatus1['COUNT(*)'])
countData2 = int(dataStatus2['COUNT(*)'])
#if predataStatus == 0:
# if countData1 >= minPreProcess:
# mycursor1.execute(
# "UPDATE Sys_Status SET Status = %s WHERE Name = %s", ('1', "preData"))
# mydb.commit()
# appscript.app('Terminal').do_script(
# "python /Users/admin/Documents/Project/preData.py /Users/admin/Documents/Project/")
mycursor.execute('SELECT * FROM Sys_Status WHERE Name = "preData" ')
preDataResult1 = mycursor.fetchone()
predataStatus1 = int(preDataResult1['Status'])
#if trainingStatus == 0 and predataStatus1 == 0:
# if countData2 >= minTraining:
# mycursor1.execute(
# "UPDATE Sys_Status SET Status = %s WHERE Name = %s", ('1', "Training"))
# mydb.commit()
# appscript.app('Terminal').do_script(
# "python /Users/admin/Documents/Project/Training.py /Users/admin/Documents/Project/")
return ('{"Status" : 1}')
@BackEndApp.route('xxxxxxx', methods=['GET'])
def predictV2():
def partition(start, end):
pivot = y['data'][0][start]
low = start + 1
high = end
while True:
while low <= high and y['data'][0][high] >= pivot:
high = high - 1
while low <= high and y['data'][0][low] <= pivot:
low = low + 1
if low <= high:
y['data'][0][low], y['data'][0][high] = y['data'][0][
high], y['data'][0][low]
y['columns'][low], y['columns'][high] = y['columns'][
high], y['columns'][low]
else:
break
y['data'][0][start], y['data'][0][high] = y['data'][0][high], y[
'data'][0][start]
y['columns'][start], y['columns'][high] = y['columns'][high], y[
'columns'][start]
return (high)
def quick_sort(start, end):
if start >= end:
return
p = partition(start, end)
quick_sort(start, p - 1)
quick_sort(p + 1, end)
resp = str(request.args.get("ft"))
ft = '[['
for i in range(13):
if i < 12:
ft += str(float(resp[i])) + ','
if i == 12:
ft += str(float(resp[i])) + ']]}'
result = model.predict(
'{"columns": ["f1", "f2", "f3", "f4","f5","f6","f7", "f8", "f9", "f10","f11","f12","f13"], "data": '
+ ft)
JsonData = str(result).replace('probability(', '')
JsonData = JsonData.replace(')', '')
y = json.loads(JsonData)
for i in range(26):
y['data'][0][i] = round(float(y['data'][0][i]) * 100, 2)
quick_sort(0, 25)
return json.dumps(y)
@BackEndApp.route("/get-image/", methods=['GET'])
def Get_Image():
try:
image_name = str(request.args.get("name"))
return send_from_directory('IMG/',
filename=image_name,
as_attachment=True,
mimetype='image/jpeg',
attachment_filename="nom_image")
except FileNotFoundError:
pass
@BackEndApp.route("/get-dataset/", methods=['GET'])
def Get_Dataset():
filename = "train_new_dataset_cactus.xlsx"
try:
return send_from_directory('dataset/',
filename=filename,
as_attachment=True)
except FileNotFoundError:
print("File not found")
@BackEndApp.route('/register', methods=['POST'])
def register():
resp = request.get_json()
email = str(resp["email"])
password = str(resp["password"])
name = str(resp["name"])
surname = str(resp["surname"])
uid = str(uuid.uuid4())
role = 'M'
sql = "INSERT INTO User_Data (Email,Password,Name,Surname,Role,Uid,isLogin,EmV) VALUES (%s, %s, %s, %s, %s,%s,%s,%s)"
val = (email, password, name, surname, role, uid, '0', '0')
mycursor1.execute(sql, val)
mydb.commit()
sendEmail_EmV(email, name, surname, uid)
return ("ok")
@BackEndApp.route('/edit/name', methods=['POST'])
def editName():
resp = request.get_json()
nameOld = resp['nameOld']
nameNew = resp['nameNew']
userId = resp['userId']
mycursor1.execute("UPDATE User_Data SET Name = %s WHERE Code = %s",
(nameNew, userId))
mydb.commit()
Time = int(time.time())
Desciption = "Old Name is " + nameOld + ",New Name is " + nameNew
mycursor1.execute(
"INSERT INTO Log (JobName,Description,Time,User_id,IP) VALUES (%s,%s,%s,%s,%s)",
("UPDATE SET Name", Desciption, Time, userId, request.remote_addr))
mydb.commit()
return ('{"status" : 1}')
@BackEndApp.route('/edit/surname', methods=['POST'])
def editSurnName():
resp = request.get_json()
surnameOld = resp['surnameOld']
surnameNew = resp['surnameNew']
userId = resp['userId']
mycursor1.execute("UPDATE User_Data SET Surname = %s WHERE Code = %s",
(surnameNew, userId))
mydb.commit()
Time = int(time.time())
Desciption = "Old Surname is " + surnameOld + ",New Surname is " + surnameNew
mycursor1.execute(
"INSERT INTO Log (JobName,Description,Time,User_id,IP) VALUES (%s, %s, %s, %s,%s)",
("UPDATE Surname", Desciption, Time, userId, request.remote_addr))
mydb.commit()
return ('{"status" : 1}')
gmail_user = '******'
gmail_password = '******'
def sendEmail_EmV(email, Name, Surname, Uid):
try:
server = smtplib.SMTP('smtp.gmail.com', 587)
server.starttls()
server.login(gmail_user, gmail_password)
send_from = gmail_user
to = str(email)
subject = 'ระบบยืนยันอีเมล์จากเว็ปไซต์การสืบค้นอนุกรมวิธานแคคตัส'
url = "http://*****:*****@BackEndApp.route('/forgot/', methods=['GET'])
def forgot():
email = str(request.args.get("email"))
mycursor.execute("SELECT * FROM User_Data WHERE Email = '" + email +
"'")
check = mycursor.fetchone()
if check:
statusSend = sendEmail_CPass(email, check['Name'],
check['Surname'], check['Uid'])
if statusSend == 1:
status = {"status": 1}
elif statusSend == 0:
status = {"status": 2}
else:
status = {"status": 0}
jsonData = json.dumps(status)
return (jsonData)
def sendEmail_CPass(email, Name, Surname, Uid):
try:
server = smtplib.SMTP('smtp.gmail.com', 587)
server.starttls()
server.login(gmail_user, gmail_password)
send_from = gmail_user
to = str(email)
subject = 'ระบบยืนยันอีเมล์จากเว็ปไซต์การสืบค้นอนุกรมวิธานแคคตัส'
url = "http://*****:*****@BackEndApp.route('/forgot/password/', methods=['GET'])
def forgotPassword():
email = str(request.args.get("email"))
uid = str(request.args.get("uid"))
mycursor.execute(
"SELECT * FROM User_Data WHERE Email = %s AND Uid = %s",
(email, uid))
check = mycursor.fetchone()
if check:
return render_template('CPass.html', text=email)
else:
return render_template('notFound.html')
@BackEndApp.route('/changePasswordE', methods=['POST'])
def changePasswordE():
resp = request.get_json()
email = resp['email']
password = resp['password']
mycursor1.execute(
"UPDATE User_Data SET Password = %s WHERE Email = %s",
(password, email))
mydb.commit()
uid = str(uuid.uuid4())
mycursor1.execute("UPDATE User_Data SET Uid = %s WHERE Email = %s",
(uid, email))
mydb.commit()
return ('{"status" : 1}')
@BackEndApp.route('/changePassword', methods=['POST'])
def changePassword():
resp = request.get_json()
oldPassword = resp['oldPassword']
newPassword = resp['newPassword']
userID = int(resp['userId'])
mycursor.execute("SELECT * FROM User_Data WHERE Code = " + str(userID))
check = mycursor.fetchall()
if check:
if check[0]['Password'] == oldPassword:
mycursor1.execute(
"UPDATE User_Data SET Password = %s WHERE Code = %s",
(newPassword, userID))
mydb.commit()
status = {"status": 1}
else:
status = {"status": 0}
else:
status = {"status": 0}
jsonData = json.dumps(status)
return (jsonData)
@BackEndApp.route('/email-verifier/', methods=['GET'])
def email_verifier():
email = str(request.args.get("email"))
uid = str(request.args.get("uid"))
mycursor.execute(
'SELECT * FROM User_Data WHERE Email = %s AND Uid = %s',
(email, uid))
check = mycursor.fetchone()
if check:
text = "ยืนยันอีเมล์สำเร็จ ยินดีต้อนรับคุณ " + \
check["Name"]+" เข้าเป็นสมาชิก"
mycursor1.execute("UPDATE User_Data SET Emv = %s WHERE Code = %s",
('1', check["Code"]))
mydb.commit()
uid = str(uuid.uuid4())
mycursor1.execute("UPDATE User_Data SET Uid = %s WHERE Code = %s",
(uid, check["Code"]))
mydb.commit()
else:
text = "ยืนยันอีเมลล์ไม่สำเร็จกรุณาสมัครสมาชิกหรือตรวจสอบอีเมลล์อีกครั้ง"
return render_template('Emv.html', text=text)
@BackEndApp.route('/checkEmail/', methods=['GET'])
def checkEmail():
email = str(request.args.get("email"))
mycursor.execute("SELECT * FROM User_Data WHERE Email = '" + email +
"'")
check = mycursor.fetchall()
if check:
status = {"status": 1}
else:
status = {"status": 0}
jsonData = json.dumps(status)
return (jsonData)
@BackEndApp.route('/login', methods=['POST'])
def login():
resp = request.get_json()
email = str(resp["email"])
password = str(resp["password"])
mycursor.execute(
'SELECT * FROM User_Data WHERE Email = %s AND Password = %s',
(email, password))
account = mycursor.fetchone()
if account:
code = str(account["Code"])
if int(account["EmV"]) == 0:
status = 1
elif int(account["EmV"]) == 1:
status = 2
elif int(account["EmV"]) == 2:
status = 3
mycursor1.execute(
"UPDATE User_Data SET isLogin = %s WHERE Code = %s",
(str(int(time.time())), str(code)))
mydb.commit()
user = {
"Code": code,
"Email": account["Email"],
"Name": account["Name"],
"Surname": account["Surname"],
"Role": account["Role"],
"Uid": account["Uid"],
"Status": status
}
Data = json.dumps(user)
else:
Data = '{"Code" : -1,"Email" : "null","Name" : "null","Surname" : "null","Role" : "null","Uid": "null","Status" : 0}'
return (Data)
@BackEndApp.route('/ban/account/', methods=['GET'])
def banAccount():
mycursor1.execute("UPDATE User_Data SET EmV = %s WHERE Code = %s",
('2', str(request.args.get("id"))))
mydb.commit()
return ('{"status" : 1}')
@BackEndApp.route('/unban/account/', methods=['GET'])
def UnbanAccount():
mycursor1.execute("UPDATE User_Data SET EmV = %s WHERE Code = %s",
('1', str(request.args.get("id"))))
mydb.commit()
return ('{"status" : 1}')
return BackEndApp
from pypmml import Model
_model = Model.fromFile('./resources/model.pmml')
def predict(X: object):
return _model.predict(X)
cors = CORS(BackEndApp, resources={r"/*": {"origins": "*"}})
mydb = mysql.connector.connect(
host="127.0.0.1",
user="******",
passwd="",
database="cactus",
raise_on_warnings=True)
mycursor = mydb.cursor(dictionary=True)
mycursor1 = mydb.cursor()
mycursor.execute("SELECT * FROM Model_Data WHERE Status = '1'")
Model2 = mycursor.fetchone()
model = Model.fromFile("ModelGBT/" + Model2['Name'])
Accuracy = float(Model2['Accuracy'])
print("LoadModel...({0})".format(Model2['Name']))
with open('dataFT.json', encoding="utf8") as json_file:
jsonFTData = json.load(json_file)
print("====Server Start====")
@BackEndApp.route('/')
def hello():
return '{"result" : "Hello","No" : 1}'
@BackEndApp.route('/Model/Accuracy/')
def ModelAccuracy():
