def load_model():
try:
model_id = request.args.get("modelId")
classModel = request.args.get("classModel")
if (classModel == "SystemModelVn"):
r = API.get_class("DatasetSurveyBalance")
arr = str(r.data, "utf-8")
r_json = json.loads(arr)
data = r_json["results"]
dataFrame = pd.DataFrame(data)
dataFrame = dataFrame.iloc[:, 3:]
dataFrame.drop('obesity', inplace=True, axis=1)
new_record = request.args.get("record")
new_record = new_record.split(",")
df_new_record = pd.DataFrame([new_record])
df_new_record.columns = dataFrame.columns
for i in range(len(df_new_record)):
for feature in list(df_new_record.columns):
if df_new_record[feature][i] == "":
df_new_record[feature][i] = list(
dataFrame[feature].value_counts().index)[list(
dataFrame[feature].value_counts()).index(
min(dataFrame[feature].value_counts()))]
if model_id == None:
return "[error] modelId not found check (keys) modelId and values"
else:
r = API.get_model(classModel, model_id)
modelUrl = r["modelFile"]["url"]
Nu_SVC_classifier = joblib.load(urlopen(modelUrl))
data_transform = {
"0": "Thiếu cân (Insufficient weight)",
"1": "Bình thường (Normal weight)",
"2": "Thừa cân loại 1 (Overweight level 1)",
"3": "Thừa cân loại 2 (Overweight level 2)",
"4": "Béo phì loại 1 (Obesity type I)",
"5": "Béo phì loại 2 (Obesity type II)",
"6": "Béo phì loại 3 (Obesity type III)",
}
KQ = np.array(Nu_SVC_classifier.predict(df_new_record))
dataReturn = {
"dataPredict": [],
}
for rs in KQ:
dataReturn["dataPredict"].append(data_transform[str(rs)])
return dataReturn
except:
print("[error] check key (inputColumns) and value")
return "[error] check key (inputColumns) and value (check type inputColumns)"
pass
def create_api_model():
# try:
if request.headers["CONTENT_TYPE"] == "application/json":
try:
data_request = request.json
modelId = data_request["modelId"]
if modelId == None:
return "[error] modelId not found check (keys) modelId and values "
else:
data_test_json = data_request["data"]
data_test_dataFrame = pd.DataFrame(data_test_json)
r = API.get_model("Model", modelId)
modelUrl = r["modelFile"]["url"]
Nu_SVC_classifier = joblib.load(urlopen(modelUrl))
KQ = np.array(Nu_SVC_classifier.predict(data_test_dataFrame))
data_transform = {
"0": "Thiếu cân (Insufficient weight)",
"1": "Bình thường (Normal weight)",
"2": "Thừa cân loại 1 (Overweight level 1)",
"3": "Thừa cân loại 2 (Overweight level 2)",
"4": "Béo phì loại 1 (Obesity type I)",
"5": "git Béo phì loại 2 (Obesity type II)",
"6": "Béo phì loại 3 (Obesity type III)",
}
dataReturn = {
"result": [],
}
for rs in KQ:
dataReturn["result"].append(data_transform[str(rs)])
return dataReturn
except:
print("[error] check key (inputColumns) and value")
return (
"[error] check key (inputColumns) and value (check type inputColumns)"
)
pass
else:
modelId = request.form.get("modelId")
if modelId == None:
return "[error] modelId not found check (keys) modelId and values "
else:
file_test = request.files.getlist("data")[0]
file_name = secure_filename(file_test.filename)
if file_name == "":
return "[error] Can't find data, check keys 'data' and values"
else:
try:
filename_random = str(uuid.uuid4())[:8] + "_" + file_name
if os.path.exists(app.config["DATA_API_FOLDER"]):
file_path_test = os.path.join(
app.config["DATA_API_FOLDER"], filename_random)
else:
os.makedirs(app.config["DATA_API_FOLDER"])
file_path_test = os.path.join(
app.config["DATA_API_FOLDER"], filename_random)
file_test.save(file_path_test)
df_test, columns, n, m = DATA.read("csv", file_path_test,
",")
except Exception as e:
print(e)
return "[error] can't save data, request fail"
pass
try:
col_feature_test_string = request.form.getlist(
"inputColumns")[0]
col_feature_test_list = ast.literal_eval(
col_feature_test_string)
col_feature_test_array = np.array(col_feature_test_list)
r = API.get_model("Model", modelId)
modelUrl = r["modelFile"]["url"]
Nu_SVC_classifier = joblib.load(urlopen(modelUrl))
except:
print("[error] request fail")
notification = (
"[error] request fail check key 'modelId', model " +
str(modelId) + " not found")
return notification
pass
try:
data_test = df_test.iloc[:, col_feature_test_array]
KQ = np.array(Nu_SVC_classifier.predict(data_test))
data_transform = {
"0": "Thiếu cân (Insufficient weight)",
"1": "Bình thường (Normal weight)",
"2": "Thừa cân loại 1 (Overweight level 1)",
"3": "Thừa cân loại 2 (Overweight level 2)",
"4": "Béo phì loại 1 (Obesity type I)",
"5": "Béo phì loại 2 (Obesity type II)",
"6": "Béo phì loại 3 (Obesity type III)",
}
dataReturn = {
"result": [],
}
for rs in KQ:
dataReturn["result"].append(data_transform[str(rs)])
os.remove(file_path_test)
return dataReturn
except IndexError:
print("[error] check key (inputColumns) and value")
return "[error] check key (inputColumns) and value check (number inputColumns)"
pass
except ValueError:
print("[error] check key (inputColumns) and value")
return "[error] check key (inputColumns) and value (check type inputColumns)"
pass