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
def upload_file_url():
try:
# Get file
url = request.args.get("urlData")
data_name = request.args.get("dataName")
separator = request.args.get("separator")
if data_name == "":
data_name = "dataset_not_name"
user_id = request.args.get("userId")
except AttributeError:
print("[error] can't find file_upload (upfile function app.py)")
pass
try:
# Create random id
random_id = str(uuid.uuid4())[:8]
# Random file_name
filename_upload_random = str(random_id) + "_" + "upload.csv"
# get file_path
if os.path.exists(app.config["UPLOAD_FOLDER"]):
file_path_upload = os.path.join(app.config["UPLOAD_FOLDER"],
filename_upload_random)
else:
os.makedirs(app.config["UPLOAD_FOLDER"])
file_path_upload = os.path.join(app.config["UPLOAD_FOLDER"],
filename_upload_random)
# save file
# Save file locally
a = urlretrieve(url, file_path_upload)
# Read file into a DataFrame and print its head
except UnboundLocalError:
print(
"[error] local variable 'filename' referenced before assignment (upfile function app.py)"
)
pass
except ValueError:
data_return_err = {"error": "unknown url"}
return data_return_err
try:
data, col, n, m = DATA.read("csv", file_path_upload, separator)
file_name_csv = data_name + ".csv"
file_path_save_csv = os.path.join(app.config["UPLOAD_FOLDER"],
file_name_csv)
export_csv = data.to_csv(file_path_save_csv, index=None, header=True)
data_str = DATA.convert_str(file_path_save_csv)
data_str = str(data_str)
data_post = {
"jsonData": data_str,
"dataName": data_name,
"userUpload": {
"__type": "Pointer",
"className": "_User",
"objectId": user_id,
},
"delimiter": separator,
"uploadFrom": "url",
}
class_name = "Data"
data = API.post(class_name, data_post)
print(data)
return data
except UnboundLocalError:
print("[error] ")
return "fail, can't upload dataset"