from cmath import log
from dataclasses import dataclass
from distutils.command.upload import upload
from gc import callbacks
import flask
from flask import request, jsonify
import newmlservice
import tensorflow as tf
import pandas as pd
import json
import requests
import config

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,modelId):
        self.x_test = x_test
        self.y_test = y_test
        self.modelId=modelId
    #
    def on_epoch_end(self, epoch, logs=None):
        #print('Evaluation: ', self.model.evaluate(self.x_test,self.y_test),"\n")
       
        #print(epoch)
        
        #print(logs)
        
        #ml_socket.send(epoch)
        #file = request.files.get("file")
        url = config.api_url + "/Model/epoch"
        r=requests.post(url, json={"Stat":str(logs),"ModelId":str(self.modelId),"EpochNum":epoch}).text
        
        #print(r)
        #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*************************************************")
    paramsModel = json.loads(request.form["model"])
    paramsExperiment = json.loads(request.form["experiment"])
    paramsDataset = json.loads(request.form["dataset"])
    f = request.files.get("file")
    if(paramsDataset['delimiter']=='novi red'):
        separation='\n'

    elif(paramsDataset['delimiter']=='razmak'):
        separation=' '
    else:
        separation=paramsDataset['delimiter']
    data = pd.read_csv(f,sep=separation)
    

    #dataset, paramsModel, paramsExperiment, callback)
    filepath,result,finalMetrics= 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,data={"uploaderId":paramsExperiment['uploaderId']})
    fileId=r.text
    m = []
    for attribute, value in result.items():
        m.append({"Name" : attribute, "JsonValue" : value})
    predictor = {
        "_id" : "",
        "uploaderId" : paramsModel["uploaderId"],
        "inputs" : paramsExperiment["inputColumns"],
        "output" : paramsExperiment["outputColumn"],
        "isPublic" : False,
        "accessibleByLink" : False,
        "experimentId" : paramsExperiment["_id"],
        "modelId" : paramsModel["_id"],
        "h5FileId" : fileId,
        "metrics" : m,
        "finalMetrics":finalMetrics
        
    }
    #print(predictor)
    url = config.api_url + "/Predictor/add"
    r = requests.post(url, json=predictor).text
    print(r)
    return r

@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,train_callback)
    return result

@app.route('/manageH5', methods = ['POST'])
def manageH5():
    h5 = request.files.get("h5file")
    model = tf.keras.models.load_model(h5)

    paramsExperiment = json.loads(request.form["experiment"])
    paramsModel = json.loads(request.form["model"])
    paramsDataset = json.loads(request.form["dataset"])
    
    f = request.files.get("file")
    if(paramsDataset['delimiter']=='novi red'):
        separation='\n'
    elif(paramsDataset['delimiter']=='razmak'):
        separation=' '
    else:
        separation=paramsDataset['delimiter']
    
    data = pd.read_csv(f,sep=separation)
    
    result = newmlservice.manageH5(data,paramsModel,paramsExperiment,paramsDataset,model,train_callback)
    return result


@app.route('/preprocess',methods=['POST'])
def returnColumnsInfo():
    print("********************************PREPROCESS*******************************")
   
    dataset = json.loads(request.form["dataset"])
    file = request.files.get("file")
    if(dataset['delimiter']=='novi red'):
        separation='\n'

    elif(dataset['delimiter']=='razmak'):
        separation=' '
    else:
        separation=dataset['delimiter']
    data=pd.read_csv(file,sep=separation)
    '''
    #f = request.json['filepath']
    #data=pd.read_csv(f)
    dataset={}
    '''
    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"][0:6]
        col["uniqueValuesCount"] = col["uniqueValuesCount"][0:6]
        col['uniqueValuesPercent']=col['uniqueValuesPercent'][0:6]
    dataset["columnInfo"] = preprocess["columnInfo"]
    dataset["nullCols"] = preprocess["allNullColl"]
    dataset["nullRows"] = preprocess["allNullRows"]
    dataset["colCount"] = preprocess["colCount"]
    dataset["rowCount"] = preprocess["rowCount"]
    dataset["cMatrix"]=preprocess['cMatrix']
    dataset["isPreProcess"] = True

    return jsonify(dataset)
    
print("App loaded.")
app.run()