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import flask
from flask import request, jsonify
import ml_socket
import newmlservice
import tensorflow as tf
import pandas as pd
app = flask.Flask(__name__)
app.config["DEBUG"] = True
app.config["SERVER_NAME"] = "127.0.0.1:5543"
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)
#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.json["dataset"]
dataset = pd.read_csv(f)
#
result = newmlservice.train(dataset, request.json["model"], train_callback)
print(result)
return jsonify(result)
@app.route('/predict', methods = ['POST'])
def predict():
f = request.json['filepath']
dataset = pd.read_csv(f)
m = request.json['modelpath']
model = tf.keras.models.load_model(m)
print("********************************model loaded*******************************")
newmlservice.manageH5(dataset,request.json['model'],model)
return "done"
@app.route('/preprocess',methods=['POST'])
def returnColumnsInfo():
f=request.json['filepathcolinfo']
dataset=pd.read_csv(f)
result=newmlservice.returnColumnsInfo(dataset)
return jsonify(result)
print("App loaded.")
ml_socket.start()
app.run()
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