main.py

# -*- coding: utf-8 -*-
"""flaskProto.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1VC7pgAb9uuRwD0Bm89x8sAvKi7uliBoF
"""

#cd drive/'My Drive'/datasetDevhack
import os
import flask
import pandas as pd
import tensorflow as tf
from keras.models import load_model
import requests
import datetime
from sklearn import preprocessing
import numpy as np
from sklearn.preprocessing import StandardScaler
import json
import pickle
from sklearn.pipeline import Pipeline

# instantiate flask 
app = flask.Flask(__name__)

# load the model, and pass in the custom metric function
global graph
graph = tf.get_default_graph()

holidays_tt = ["2020-01-01",
                "2020-01-15",
                "2020-01-26",
                "2020-02-21",
                "2020-03-10",
                "2020-03-25",
                "2020-04-02",
                "2020-04-06",
                "2020-04-10",
                "2020-05-01",
                "2020-05-07",
                "2020-05-25",
                "2020-06-23",
                "2020-08-01",
                "2020-08-03",
                "2020-08-12",
                "2020-08-15",
                "2020-08-22",
                "2020-08-30",
                "2020-08-31",
                "2020-10-02",
                "2020-10-25",
                "2020-10-30",
                "2020-11-14",
                "2020-11-30",
                "2020-12-25"
                ]
url = "https://api.openweathermap.org/data/2.5/weather?q=Bengaluru,in&APPID=b1a275b64af38a8f9823800a58345b93"
# homepage
@app.route("/", methods=["GET","POST"])
def homepage():
    return flask.render_template("index.html")

# define a predict function as an endpoint 
# @app.route("/feedback", methods=["POST"])
# def predict():  
#     formFinal = []
#     formData1 = int(flask.request.form['formData1'])
#     print(formData1)
#     formData2 = int(flask.request.form['formData2'])
#     print(formData2)
#     formFinal.append(formData1)
#     formFinal.append(formData2)
#     print(formFinal)


mar = [0.14,0.53,0.24,0.13,0.67,0.87,0.22,0.23,0.12,0.56,0.23,0.25,0.78,0.12]
model = load_model('final_model.h5')


@app.route("/predict", methods=["POST"])
def predict():
    dat = flask.request.form['date']
    #print(dat)
    time = flask.request.form['time']
   # print(time)
    
    #holiday
    if str(dat) in holidays_tt:
        holiday=1
    else:
        holiday=0
    
    #print("Holiday =", holiday)
    
    
    response = requests.get(url).json()
    temp = float(response["main"]["temp"]) - 273.15
    temp_min = float(response["main"]["temp_min"]) - 283.15 #made it 283.15 from 273.15
    temp_max = float(response["main"]["temp_max"]) - 273.15
    pressure = response["main"]["pressure"]
    humidity = response["main"]["humidity"]
    #print(temp, temp_min, temp_max, pressure, humidity)
    
    #dat = "2023-11-01"
    #time = "01:01"
    
    #week
    date_time_obj = datetime.datetime.strptime(dat, '%Y-%m-%d')
    week = datetime.date(date_time_obj.year,date_time_obj.month,date_time_obj.day).isocalendar()[1]
    
    if week<26:
        week = week + 25
        
    
    #hour
    hour = int(time[:-3])
    
    #population
    dic = {
       "HSR Division" : 105265,
       "Koramangala Division" : 63987,
       "Indiranagar" : 58830,
       "Shivajinagar" : 57437,
       "Hebbal" : 54301,
       "Whitefield" : 84428,
       "Malleshwaram" : 57107,
       "Rajaji Nagara Division" : 55250,
       "Jayanagar" : 56658,
       "Jalahalli" : 63391,
       "Kengeri Division" : 68087,
       "R R NAGAR" : 82848,
       "Vidhanasoudha" : 69057,
       "Peenya Division" : 96549
       }
    
    
    lb = preprocessing.LabelBinarizer()
    lb.fit(['HSR Division', 'Koramangala Division', 'Indiranagar',
       'Shivajinagar', 'Hebbal', 'Whitefield', 'Malleshwaram',
       'Rajaji Nagara Division', 'Jayanagar', 'Jalahalli',
       'Kengeri Division', 'R R NAGAR', 'Vidhanasoudha',
       'Peenya Division'])
    
    
    lt = list(dic.keys())
    
    df = pd.DataFrame(lt)
    
    divs = lb.transform(df)
    
    divs = pd.DataFrame(divs)
    
    #divs['Week'] = week
    
    #divs['Week'] = np.array(([week]*13).T
    
    week = [week]*14
    
    temp_max = [temp_max]*14
    temp_min = [temp_min]*14
                        
    holiday = [holiday]*14  
    
    divs = pd.concat([pd.DataFrame(temp_max), divs], axis=1)
    divs = pd.concat([pd.DataFrame(temp_min), divs], axis=1)
    divs = pd.concat([pd.DataFrame(week), divs], axis=1)
    divs = pd.concat([divs, pd.DataFrame(holiday)], axis=1)
    
        
    pop = [dic[x] for x in lt]
    
    #population
    divs = pd.concat([divs, pd.DataFrame(pop)], axis=1)
    
    
    hour = [hour]*14
    
    divs = pd.concat([ divs, pd.DataFrame(hour)], axis=1)
    
    
    smol = pd.read_excel('smol.xlsx')
    smol = smol.iloc[:,1:]
    #divs = pd.read_excel("pls.xlsx")
    
   # divs.to_csv("pls.csv")
    
    #print(smol.shape)
    #print(divs.shape)

    #fin = pd.concat([divs, smol])
    
    df = pd.DataFrame( np.concatenate( (divs.values, smol.values), axis=0 ) )
    
    from sklearn.preprocessing import StandardScaler
    sc_X = StandardScaler()
    df = sc_X.fit_transform(df)

    
    with graph.as_default():
        prd = model.predict(df)
        
    
    #print("divs is: ", divs)
    prd = abs(prd[0:14])#-mar)
    
    print("fin",prd)
    newprd = prd.tolist()
    #print(newprd)


    return flask.render_template("index.html", data = newprd)
    
    
    
    
# start the flask app, allow remote connections 
if __name__ == "__main__":
    app.run(host='0.0.0.0', port=8000)