from flask import Flask,render_template, redirect, url_for, request

# from flask_uploads import UploadSet, configure_uploads, IMAGES

#from flask.ext.images import resized_img_src

import os

import pandas as pd

from sklearn import preprocessing

import h2o as h

import numpy as np

import seaborn as sns

h.init(port=8000)

from h2o.estimators.gbm import H2OGradientBoostingEstimator

from h2o.estimators.random_forest import H2ORandomForestEstimator

def smart_fav_processing(service_usage,hard_key_usage):

return h.H2OFrame(dfb)

#app = Flask (__name__,template_folder='templates')

app = Flask (__name__,template_folder='static')

#@app.route('/hello', methods=['GET', 'POST'])

@app.route('/')

def index():

return render_template('packages.html')

# @app.route('/index', methods=['GET', 'POST'])

# def show_index():

# return render_template("new.html")

df=pd.read_csv("D:/AI/AI_Hub/Head Unit Data/data/vw_service_usage_test.txt",sep='\\t',engine='python')

df3=pd.read_csv("D:/AI/AI_Hub/Head Unit Data/data/vw_hard_key_usage_test.txt",sep='\\t',engine='python')

def readFiles():

# return service_usage.to_html(), hard_key_usage.to_html()

@app.route('/home/')

def data():

return render_template('new.html')

# @app.route('/home/Data_List/')

# def data():

# # ---- view data sources ---

# return 'a'

@app.route('/home/Work_Packages/')

def packages():

return render_template('packages.html')

@app.route('/home/my-link/')

def my_link():

titles = [ 'na','service_usage', 'hard_key_usage'])

# def my_link():

# service_usage,hard_key_usage= readFiles()

# return service_usage.to_html()

# def my_link():

# df=pd.read_csv("D:/AI/AI_Hub/Head Unit Data/data/vw_service_usage_test.txt",sep='\\t',engine='python')

# df3=pd.read_csv("D:/AI/AI_Hub/Head Unit Data/data/vw_hard_key_usage.txt",sep='\\t',engine='python')

# #model = h.load_model('D:/AI/AI_Hub/Head Unit Data/rf_covType_v1')

# # test= smart_fav_processing(df,df3)

# # predictions = model.predict(test[:-7])

# # pred_df= predictions.as_data_frame()

# # sort_pred = np.argsort(pred_df.iloc[0:,1:8], axis=1)

# # top3=[(sort_pred.columns[(sort_pred == 0).iloc[0]])[0],(sort_pred.columns[(sort_pred == 1).iloc[0]])[0],(sort_pred.columns[(sort_pred == 2).iloc[0]])[0]]

# # for i in range(0,len(top3)):

# # if top3[i]=='sat':

# # top3[i]='Satellite Radio'

# # if top3[i]=='fm':

# # top3[i]='FM Radio'

# # if top3[i]=='usb1':

# # top3[i]='USB Device'

# return df.head(n=3), df3.head(n=3)

@app.route('/home/my-link2/')

def my_link2():

#return ( test.to_html())

@app.route('/home/link3/')

def link3():

return render_template('pred.html', your_list=[top3[0], top3[1], top3[2]])

@app.route('/home/link4/')

def plot():

return render_template('plot.html')

if __name__ == "__main__":

app.run(port=8080)

######SPYRE

# from spyre import server

# import pandas as pd

# import h2o as h

# class UserUploadApp(server.App):

# title = "Custom File Upload Example"

# results = [{

# "type": "text",

# "key": "words",

# "label": "prediction",

# "value": '',

# "action_id": "simple_html_output"

# }]

# controls = [{

# "type": "upload",

# "id": "ubutton",

# },

# {

# "type": "button",

# "label": "Upload1",

# "id": "update_data1"

# },

# {"type": "upload",

# "id": "ubutton"

# },

# {

# "type": "button",

# "label": "Upload2",

# "id": "update_data2"

# }]

# tabs = ["Text", "Table1", "Table2","Plot"]

# outputs = [{

# "type": "plot",

# "id": "plot",

# "control_id": "update_data",

# "tab": "Plot",

# "on_page_load": True

# }, {

# "type": "table",

# "id": "table_id",

# "control_id": "update_data1",

# "tab": "Table1",

# "on_page_load": True

# }, {

# "type": "table",

# "id": "table_id2",

# "control_id": "update_data2",

# "tab": "Table2",

# "on_page_load": True

# }, {

# "type": "html",

# "id": "html2",

# #"control_id": "update_data",

# "tab": "Text"

# }]

# def __init__(self):

# self.upload_data = None

# self.upload_file = None

# def html1(self, params):

# text = (

# "Upload a CSV and press refresh. There's a sample csv in "

# "the examples directory that you could try."

# )

# if self.upload_data is not None:

# text = self.upload_data

# return text

# def html2(self,params):

# text = (

# "df.columns[1]"

# )

# if self.upload_data is not None:

# text = self.upload_data

# return text

# def storeUpload(self, file):

# self.upload_file = file

# self.upload_data = file.read()

# #self.update_data2 = file.read()

# def getData(self, params):

# df = None

# #h.init(strict_version_check = False)

# #model=h.load_model('D:/AI/AI_Hub/Head Unit Data/rf_covType_v1')

# #df3=None

# if self.upload_file is not None:

# self.upload_file.seek(0)

# df = pd.read_csv(self.upload_file,sep='\\t',engine='python')

# #df3 = pd.read_csv(self.upload_file,sep='\\t',engine='python')

# return df.driver_type.value_counts()

# return df

# def modell(df):

# df = pd.read_csv(self.upload_file,sep='\\t',engine='python')

# return df.columns[1]

# if __name__ == '__main__':

# app = UserUploadApp()

# app.launch()