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from dataclasses import dataclass
from gc import callbacks
from xmlrpc.client import DateTime
import flask
from flask import request, jsonify
import newmlservice
import tensorflow as tf
import pandas as pd
import json
import requests
import config
from datetime import datetime
app = flask.Flask(__name__)
app.config["DEBUG"] = True
app.config["SERVER_NAME"] = config.hostIP
@dataclass
class Predictor:
_id : str
username: str
inputs : list
output : str
isPublic: bool
accessibleByLink: bool
dateCreated: DateTime
experimentId: str
modelId: str
h5FileId: str
metrics: list
class train_callback(tf.keras.callbacks.Callback):
def __init__(self, x_test, y_test):
self.x_test = x_test
self.y_test = y_test
#
def on_epoch_end(self, epoch, logs=None):
print(epoch)
#ml_socket.send(epoch)
#file = request.files.get("file")
url = config.api_url + "/Model/epoch"
requests.post(url, epoch).text
#print('Evaluation: ', self.model.evaluate(self.x_test,self.y_test),"\n") #broj parametara zavisi od izabranih metrika loss je default
@app.route('/train', methods = ['POST'])
def train():
print("******************************TRAIN*************************************************")
f = request.files.get("file")
data = pd.read_csv(f)
paramsModel = json.loads(request.form["model"])
paramsExperiment = json.loads(request.form["experiment"])
paramsDataset = json.loads(request.form["dataset"])
#dataset, paramsModel, paramsExperiment, callback)
filepath,result = newmlservice.train(data, paramsModel, paramsExperiment,paramsDataset, train_callback)
"""
f = request.json['filepath']
dataset = pd.read_csv(f)
filepath,result=newmlservice.train(dataset,request.json['model'],train_callback)
print(result)
"""
url = config.api_url + "/file/h5"
files = {'file': open(filepath, 'rb')}
r=requests.post(url, files=files)
fileId=r.text
predictor = Predictor(
_id = "",
username = paramsModel["username"],
inputs = paramsExperiment["inputColumns"],
output = paramsExperiment["outputColumn"],
isPublic = False,
accessibleByLink = False,
dateCreated = datetime.now(),
experimentId = paramsExperiment["_id"],
modelId = paramsModel["_id"],
h5FileId = fileId,
metrics=[]
)
print(result)
print(predictor)
return jsonify(result)
@app.route('/predict', methods = ['POST'])
def predict():
h5 = request.files.get("h5file")
model = tf.keras.models.load_model(h5)
paramsExperiment = json.loads(request.form["experiment"])
paramsPredictor = json.loads(request.form["predictor"])
print("********************************model loaded*******************************")
result = newmlservice.predict(paramsExperiment, paramsPredictor, model)
return result
@app.route('/preprocess',methods=['POST'])
def returnColumnsInfo():
print("********************************PREPROCESS*******************************")
dataset = json.loads(request.form["dataset"])
file = request.files.get("file")
data=pd.read_csv(file)
#dataset={}
#f = request.json['filepath']
#data=pd.read_csv(f)
preprocess = newmlservice.returnColumnsInfo(data)
#samo 10 jedinstvenih posto ih ima previse, bilo bi dobro da promenimo ovo da to budu 10 najzastupljenijih vrednosti
for col in preprocess["columnInfo"]:
col["uniqueValues"] = col["uniqueValues"][0:10]
col["uniqueValuesCount"] = col["uniqueValuesCount"][0:10]
dataset["columnInfo"] = preprocess["columnInfo"]
dataset["nullCols"] = preprocess["allNullColl"]
dataset["nullRows"] = preprocess["allNullRows"]
dataset["colCount"] = preprocess["colCount"]
dataset["rowCount"] = preprocess["rowCount"]
dataset["isPreProcess"] = True
print(dataset)
return jsonify(dataset)
print("App loaded.")
app.run()
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