The app file for the PG-Stock-Prediction Web App

app.py

import numpy as np
from flask import Flask, request, jsonify, render_template
import pickle
from datetime import datetime , timedelta
import pandas as pd
import pycaret
import requests
from wwo_hist import retrieve_hist_data
from pycaret.regression import *
import matplotlib.pyplot as plt
from pycaret.regression import *
from wwo_hist import retrieve_hist_data

app = Flask(__name__)
#model = pickle.load(open('model.pkl', 'rb'))

@app.route('/')
def home():
    return render_template('index.html')

@app.route('/predict',methods=['POST'])
def predict():
    '''
    For rendering results on HTML GUI
    '''
    int_features = [x for x in request.form.values()]
    print(int_features)

    date=int_features[0]
    
    group=int_features[1]


    converted_datetime=pd.to_datetime(date).date()

    # this function makes sure that when the user enters a date, it calculates the ending of the month the user entered  
    def dateaddition (converted_datetime):
        if int(str(converted_datetime).split("-")[0])%4 ==0:
            if (int(str(converted_datetime).split("-")[1])) ==2:
                added_days=29
            elif int(str(converted_datetime).split("-")[1]) %2 ==0 and int(str(converted_datetime).split("-")[1]) <=6:   
                added_days=30
            elif int(str(converted_datetime).split("-")[1]) %2 ==1 and int(str(converted_datetime).split("-")[1]) <=6:
                added_days=31
            elif  int(str(converted_datetime).split("-")[1]) %2 ==0 and int(str(converted_datetime).split("-")[1]) >=7:
                added_days=31
            elif int(str(converted_datetime).split("-")[1]) %2 ==1 and int(str(converted_datetime).split("-")[1]) >=7:
                added_days=30  
        elif int(str(converted_datetime).split("-")[0])%4 !=0:
            if (int(str(converted_datetime).split("-")[1])) ==2:
                added_days=28
            elif int(str(converted_datetime).split("-")[1]) %2 ==0 and int(str(converted_datetime).split("-")[1]) <=6:   
                added_days=30
            elif int(str(converted_datetime).split("-")[1]) %2 ==1 and int(str(converted_datetime).split("-")[1]) <=6:
                added_days=31
            elif  int(str(converted_datetime).split("-")[1]) %2 ==0 and int(str(converted_datetime).split("-")[1]) >=7:
                added_days=31
            elif int(str(converted_datetime).split("-")[1]) %2 ==1 and int(str(converted_datetime).split("-")[1]) >=7:
                added_days=30  

        return added_days


    #converting the dates to strings to pass them through the weather-collecting api
    start_date = converted_datetime.strftime("%d-%b-%Y")

    # calculating the end date based on the function dateaddition
    end_date= pd.to_datetime(converted_datetime+timedelta(dateaddition(converted_datetime)-1)).date()

    #if the user enters the present month, the end date will be calculated based on the present day 
    if end_date > datetime.now().date():
        end_date= datetime.now().date().strftime("%d-%b-%Y")
    else:
        end_date=end_date.strftime("%d-%b-%Y")


    # this is an api to collect the necessary weather data we need 
    frequency = 24
    start_date = start_date
    end_date = end_date
    api_key = '12b2c18a34194a8ca93113127200405'
    location_list = ['Mlawa']
    hist_weather_data = retrieve_hist_data(api_key,
                                        location_list,
                                        start_date,
                                        end_date,
                                        frequency,
                                        location_label = False,
                                        export_csv = True,
                                        store_df = True)

    #the weather data is stored in the file "Mlawa.csv", hence we read from that 
    monthly_weather_data=pd.read_csv("Mlawa.csv")


    def final_weather(monthly_weather_data):
        #dropping the unecessary columns
        monthly_weather_data=monthly_weather_data.drop(["date_time","totalSnow_cm","sunHour","uvIndex.1","uvIndex","moon_illumination","moonrise","moonset","sunrise","DewPointC","sunset","WindChillC","WindGustKmph","precipMM","pressure","visibility","winddirDegree","windspeedKmph","tempC"],axis=1)
        monthly_weather_data["avg_temp"]=(monthly_weather_data["maxtempC"]+monthly_weather_data["mintempC"])/2
        monthly_weather_data=monthly_weather_data.drop(["maxtempC","mintempC"],axis=1)
        # rearranging the data
        monthly_weather_data=monthly_weather_data[["avg_temp","FeelsLikeC","HeatIndexC","cloudcover","humidity",]]
        values=[]
        monthly_averages=[]

        def mean_data(data):
        
            for key,value in data.iteritems():
                values.append(value.mean())
                print(key)
        return values

        #categorising the heat_index data into three different types
        def cat_heat(heatindex):
            if heatindex < -2:
                return (0)
            elif heatindex >=-1 and heatindex<=14:
                return (1)
            else:
                return (2)   


        #categorising the cloud cover into 4 different types
        def cat_cloud(cloudcover):
            if cloudcover < 25:
                return(0)
            elif cloudcover >=25 and cloudcover<50:
                return(1)
            elif cloudcover >=50 and cloudcover<75:
                return(1)    
            else:
                return(3)

        monthly_averages=np.around(mean_data(monthly_weather_data),2)
        monthly_averages[2]=cat_heat(monthly_averages[2])
        monthly_averages[3]=cat_cloud(monthly_averages[3])

        return monthly_averages

    monthly_weather_data=final_weather(monthly_weather_data)
        
    # print(monthly_weather_data)

    final_data=pd.read_csv("GROUP_OF_ITEMS_FINAL/"+group.upper()+".csv")

    final_data=final_data.drop(["Unnamed: 0"],axis=1)

    # print(final_data.head())


    final_data["ishol/week"]=9
    final_data["group"]=group
    final_data["monthly_Avgtemp"]=monthly_weather_data[0]
    final_data["monthly_avg_FeelsLikeC"]=monthly_weather_data[1]
    final_data["monthly_avg_HeatIndexC"]=monthly_weather_data[2]
    final_data["monthly_avg_cloudcover"]=monthly_weather_data[3]
    final_data["monthly_avg_humidity"]=monthly_weather_data[4]

    # # # test_data="pd.read_csv("GROUP_OF_DATASETS/SWEETS.csv")
    # # test_data=test_data.rename(columns={0:"weekend"})
    # # test_data=test_data.drop(test_data["quantity"])

    # print(final_data.head())

    if group=="ALCOHOL" or "KETCH_CONCETRATE_MUSTARD_MAJO_HORSERADISH" or "SPICES" or "GENERAL" or "BREAD" or "CHEWING_GUM_LOLIPOPS" or "GENERAL_FOOD":
        loaded_model=load_model("MODELS/gb")
    
    elif group== "COFFEE TEA" or "CIGARETTES" or "CHIPS_FLAKES" or "ICE_CREAMS_FROZEN" or "POULTRY" or "SWEETS":
        loaded_model=load_model("MODELS/extreme_gb")

    elif group== "GROATS_RICE_PASTA" or "OCCASIONAL":
        loaded_model=load_model("MODELS/adab")

    elif group== "CHEMISTRY" or "GENERAL_ITEMS" or "VEGETABLES":
        loaded_model=load_model("MODELS/rf")

    elif group== "DAIRY_CHESSE":
        loaded_model=load_model("MODELS/catb")

    pred=predict_model(loaded_model, data= final_data)

    # print(pred.head())
    final_sales=(np.exp(pred["Label"]))
    final_sales=np.round(final_sales,0)


    pred['Label']=final_sales


    
    '''
    fmt = '{:<8}{:<80}{}'


    print(fmt.format('', 'NAME', 'QUANTITY'))
    for i, (name, sales) in enumerate(zip(pred["name"], final_sales )):
        print(fmt.format(i, name, sales))

    print(prediction)

    '''



    output = pred
    
    s=""

    for index,row in output.iterrows():
        s+='Quantity of product {} predicted is  {}\n'.format(row['name'],row['Label'])


    return render_template('index.html', prediction_text=s)





    

if __name__ == "__main__":
    app.run(debug=True)