Upload files to 'Demand_forcasting'
This commit is contained in:
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from flask import Flask, render_template, send_file, request, redirect, Response
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import os
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import pandas as pd
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import warnings
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import json
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import requests
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from urllib.request import urlopen
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warnings.filterwarnings("ignore")
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app = Flask(__name__)
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@app.route("/", methods=["GET"])
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def home():
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return 'forcasting app running'
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######################################################################################################################
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list_output=[]
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def day(Num,df):
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#url='https://qa.bizgaze.app/apis/v4/bizgaze/integrations/demandforecast/getitemdata'
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url='https://test.bizgaze.app/apis/v4/bizgaze/integrations/demandforecast/getitemdata'
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response = urlopen(url)
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data_json = json.loads(response.read())
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headers = {
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'Authorization':'stat 27e6b51b278d444aa0b70ed60419b04c',
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#'Authorization':'stat 873f2e6f70b3483e983972f96fbf5ea4',#qa
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'Content-Type': 'application/json'
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}
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response = requests.request("GET", url, headers=headers, data=data_json)
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#print("##############################################################")
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a=response.text
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# print(response.text)
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import pandas as pd
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df2 = pd.read_json(response.text, orient ='index')
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df2=df2.reset_index()
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df2.columns = ['key','value']
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#print(df2)
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a=df2['value'][0]
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j=json.loads(a)
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userdata = pd.DataFrame(j)
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#df1
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itemid=userdata[['itemname','itemid']]
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itemid.columns = ['ItemName', 'ItemId']
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#df1=pd.read_csv(r'./upload/' + name)
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#df1=df1[df1['obdate']!='01/01/0001']
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userdata.columns = ['journaldate','sum','itemname','itemid']
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# import pandas as pd
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# userdata = pd.read_csv(r'C:\Users\Bizga\Desktop\forcast\5yearsitems.csv')
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# itemid = userdata[['itemname', 'itemid']]
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#userdata['journaldate'] = pd.to_datetime(userdata['journaldate'])
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userdata["journaldate"] = userdata["journaldate"].astype(str)
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userdata[["year", "month", "day"]] = userdata["journaldate"].str.split("/", expand = True)
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#userdata['Month-Year']=userdata['year'].astype(str)+'-'+userdata['month'].astype(str)
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item_unique_name = userdata['itemname'].unique()
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#df=pd.read_csv("C:\\Users\\Bizgaze\\2021_2022.csv")
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# Group the DataFrame by the 'item' column
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grouped = userdata.groupby('itemname')
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# Print the unique items in the 'item' column
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#print(grouped.groups.keys())
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# Iterate over the unique items and print the group data
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for item, userdata in grouped:
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print("itemname: ", item)
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item_id = userdata.iloc[-1]['itemid']
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print(item_id)
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userdata= userdata.groupby('journaldate').sum()
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userdata= userdata.reset_index()
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#print(userdata)
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fulldata=userdata[['journaldate','sum']]
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fulldata.columns = ["Dates","SALES"]
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#************************************************************************************************************************
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## Use Techniques Differencing
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import pandas as pd
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from pandas import DataFrame
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# userdata=pd.read_csv(r"C:\Users\Bizgaze\ipynb files\TS forcasting\working\139470.csv")
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userdata.columns = ['Date', 'sales','sku']
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from statsmodels.tsa.stattools import adfuller
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DATE=[]
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SALES=[]
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def adf_test(series,userdata):
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result=adfuller(series)
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print('ADF Statistics: {}'.format(result[0]))
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print('p- value: {}'.format(result[1]))
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if result[1] <= 0.05:
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print("strong evidence against the null hypothesis, reject the null hypothesis. Data has no unit root and is stationary")
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else:
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#print(userdata)
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print(stationary_test(userdata))
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print("weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary ")
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#********************************************* stationary or non-stationary **********************************************************
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def stationary_test(userdata):
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data=pd.DataFrame(userdata)
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for i in range(1,13):
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print(i)
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sales_data=DataFrame()
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data['sales']=data['sales'].shift(i)
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data.dropna(inplace=True)
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#print( userdata['sales'])
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try:
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X=adf_test(data["sales"],userdata="nothing")
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if "non-stationary" in str(X):
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print("non-stationary")
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else:
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print("stationary")
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#print(userdata[["Date","sales"]])
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#df_sale=pd.DataFrame(userdata)
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DATE.append(data["Date"])
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SALES.append(data["sales"])
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#df4 = pd.concat([data, sales_data], axis=1)
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return "done"
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break
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except ValueError:
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pass
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try:
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adf_test(userdata["sales"],userdata)
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except ValueError:
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pass
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sales=pd.DataFrame(SALES).T
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dates=pd.DataFrame(DATE).T
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try:
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df4 = pd.concat([dates["Date"],sales["sales"]], axis=1)
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df4=df4.dropna()
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print(df4)
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except KeyError:
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df4=userdata[['Date','sales']]
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df4=df4.dropna()
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print(df4)
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pass
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#####################################################################################################################
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userdata=df4
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a = userdata.iloc[-1]['Date']
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userdata['Date'] = pd.to_datetime(userdata['Date'])
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userdata["Date"] = userdata["Date"].astype(str)
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userdata[["year", "month", "day"]] = userdata["Date"].str.split("-", expand = True)
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#userdata[["year", "month"]] = userdata["Month"].str.split("-", expand=True)
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#userdata = userdata[["year","month",'sum']]
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userdata["year"] = userdata["year"].astype(int)
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userdata["month"] = userdata["month"].astype(int)
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userdata["day"] = userdata["day"].astype(int)
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#####################################################################################################################
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list_dates=[]
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import datetime
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days=int(Num)+1
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import pandas as pd
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base_date=pd.to_datetime(a)
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for x in range(1,days):
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dates=(base_date + datetime.timedelta(days=x))
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dates=str(dates).replace(" 00:00:00","")
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#print(dates)
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list_dates.append(dates)
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fut_date = pd.DataFrame(list_dates)
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fut_date.columns = ["Dates"]
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future_dates=pd.DataFrame(list_dates)
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future_dates.columns=["Dates"]
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future_dates[["year", "month", "day"]] = future_dates["Dates"].str.split("-", expand=True)
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future_dates.drop(['Dates'], axis=1, inplace=True)
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future_dates["year"] = future_dates["year"].astype(int)
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future_dates["month"] = future_dates["month"].astype(int)
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future_dates["day"] = future_dates["day"].astype(int)
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#print(future_dates)
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###############################################################################
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userdata['sales']=userdata["sales"].astype(float)
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dependent = userdata[['year','month','day']]
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independent = userdata['sales']
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import numpy as np
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import pandas as pd
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import xgboost
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import GridSearchCV
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from sklearn.metrics import roc_auc_score
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import matplotlib.pyplot as plt
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#model = xgboost.XGBRegressor()
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from sklearn.ensemble import RandomForestRegressor
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model = RandomForestRegressor(random_state=1,n_jobs=-1)
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#model.fit(dependent, independent)
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model.fit(dependent, independent)
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#future=pd.read_csv('future_dates.csv')
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future_prediction = model.predict(future_dates)
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#print(future_prediction)
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df=pd.DataFrame(future_prediction)
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df.columns = ["SALES"]
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frames = [fut_date, df]
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result = pd.concat(frames,axis=1)
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result['itemname'] = item
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result['itemid'] =item_id
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result.columns = ['Date','Predict','ItemName','ItemId']
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#result['Predict']=result["Predict"].astype(int)
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result['UpperLimit']=result["Predict"].mean()+result['Predict'].std()*3
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result['LowerLimit']=result['Predict'].mean()-result['Predict'].std()*3
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print(result)
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result.to_json('forcast.json', orient="records")
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# result['ItemName'] = item
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# result['ItemId'] =item_id
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# print(result)
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# frames = [fulldata, result]
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# final = pd.concat(frames)
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# print('********************************************************')
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# final['itemname'] = item
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# final['itemid'] =item_id
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# final.columns = ['Date','Predict','ItemName','ItemId']
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# print(final)
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# final.to_json('forcast.json', orient="records")
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with open('forcast.json', 'r') as json_file:
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json_load = json.load(json_file)
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#url = "https://demo.bizgaze.app/apis/v4/bizgaze/integrations/demandforecast/saveforecast/List"
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url='https://qa.bizgaze.app/apis/v4/bizgaze/integrations/demandforecast/saveforecast/List'
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payload = json.dumps(json_load)#.replace("]", "").replace("[", "")
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print(payload)
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headers = {
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#'Authorization': 'stat 263162e61f084d3392f162eb7ec39b2c',#demo
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'Authorization': 'stat 873f2e6f70b3483e983972f96fbf5ea4',#test
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'Content-Type': 'application/json'
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}
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response = requests.request("POST", url, headers=headers, data=payload)
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print("##############################################################")
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print(response.text)
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import time
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time.sleep(1)
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#############################################################################################################################################################
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def month(Num,df):
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# #url='https://qa.bizgaze.app/apis/v4/bizgaze/integrations/demandforecast/getitemdata'
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# url='https://test.bizgaze.app/apis/v4/bizgaze/integrations/demandforecast/getitemdata'
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# response = urlopen(url)
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# data_json = json.loads(response.read())
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# headers = {
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# 'Authorization':'stat 27e6b51b278d444aa0b70ed60419b04c',
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# #'Authorization':'stat 873f2e6f70b3483e983972f96fbf5ea4',#qa
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# 'Content-Type': 'application/json'
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# }
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# response = requests.request("GET", url, headers=headers, data=data_json)
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# #print("##############################################################")
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# a=response.text
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# import pandas as pd
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# df2 = pd.read_json(response.text, orient ='index')
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# df2=df2.reset_index()
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# df2.columns = ['key','value']
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# #print(df2)
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# a=df2['value'][0]
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# j=json.loads(a)
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# userdata = pd.DataFrame(j)
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# #df1
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filePath='path.csv'
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if os.path.exists(filePath):
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print('file exist')
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os.remove('path.csv')
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else:
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print("file as it doesn't exists")
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pass
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userdata=df
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itemid=userdata[['itemname','itemid']]
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itemid.columns = ['ItemName', 'ItemId']
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#df1=pd.read_csv(r'./upload/' + name)
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#df1=df1[df1['obdate']!='01/01/0001']
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userdata.columns = ['itemname','sum','journaldate','itemid']
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# import pandas as pd
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# userdata = pd.read_csv(r'C:\Users\Bizga\Desktop\forcast\5yearsitems.csv')
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# itemid = userdata[['itemname', 'itemid']]
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#userdata['journaldate'] = pd.to_datetime(userdata['journaldate'])
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userdata["journaldate"] = userdata["journaldate"].astype(str)
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userdata[["year", "month", "day"]] = userdata["journaldate"].str.split("-", expand = True)
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userdata['Month-Year']=userdata['year'].astype(str)+'-'+userdata['month'].astype(str)
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item_unique_name = userdata['itemname'].unique()
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#df=pd.read_csv("C:\\Users\\Bizgaze\\2021_2022.csv")
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# Group the DataFrame by the 'item' column
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grouped = userdata.groupby('itemname')
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# Print the unique items in the 'item' column
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#print(grouped.groups.keys())
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# Iterate over the unique items and print the group data
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for item, userdata in grouped:
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print("itemname: ", item)
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item_id = userdata.iloc[-1]['itemid']
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print(item_id)
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userdata= userdata.groupby('Month-Year').sum()
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userdata= userdata.reset_index()
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#print(userdata)
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fulldata=userdata[['Month-Year','sum']]
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fulldata.columns = ["Dates","SALES"]
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#************************************************************************************************************************
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## Use Techniques Differencing
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import pandas as pd
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from pandas import DataFrame
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# userdata=pd.read_csv(r"C:\Users\Bizgaze\ipynb files\TS forcasting\working\139470.csv")
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userdata.columns = ['Month', 'sales','sku']
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from statsmodels.tsa.stattools import adfuller
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DATE=[]
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SALES=[]
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def adf_test(series,userdata):
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result=adfuller(series)
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print('ADF Statistics: {}'.format(result[0]))
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print('p- value: {}'.format(result[1]))
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if result[1] <= 0.05:
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print("strong evidence against the null hypothesis, reject the null hypothesis. Data has no unit root and is stationary")
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else:
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#print(userdata)
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print(stationary_test(userdata))
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print("weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary ")
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#********************************************* stationary or non-stationary **********************************************************
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def stationary_test(userdata):
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data=pd.DataFrame(userdata)
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for i in range(1,13):
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print(i)
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sales_data=DataFrame()
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data['sales']=data['sales'].shift(i)
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data.dropna(inplace=True)
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#print( userdata['sales'])
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try:
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X=adf_test(data["sales"],userdata="nothing")
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if "non-stationary" in str(X):
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print("non-stationary")
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else:
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print("stationary")
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#print(userdata[["Month","sales"]])
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#df_sale=pd.DataFrame(userdata)
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DATE.append(data["Month"])
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SALES.append(data["sales"])
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#df4 = pd.concat([data, sales_data], axis=1)
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return "done"
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break
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except ValueError:
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pass
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try:
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adf_test(userdata["sales"],userdata)
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except ValueError:
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pass
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sales=pd.DataFrame(SALES).T
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dates=pd.DataFrame(DATE).T
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try:
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df4 = pd.concat([dates["Month"],sales["sales"]], axis=1)
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df4=df4.dropna()
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print(df4)
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except KeyError:
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df4=userdata[['Month','sales']]
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df4=df4.dropna()
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print(df4)
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pass
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#####################################################################################################################
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userdata=df4
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a = userdata.iloc[-1]['Month']
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userdata[["year", "month"]] = userdata["Month"].str.split("-", expand=True)
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#userdata = userdata[["year","month",'sum']]
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userdata["year"] = userdata["year"].astype(int)
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userdata["month"] = userdata["month"].astype(int)
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#####################################################################################################################
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#a = userdata.iloc[-1]['Month-Year']
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from datetime import datetime
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from dateutil.relativedelta import relativedelta
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import pandas as pd
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months_value = int(Num)+1
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base_month = pd.to_datetime(a)
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list_months = []
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def months(MD):
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date_after_month = ((base_month + relativedelta(months=MD)).strftime('%Y-%m'))
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# print
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list_months.append(date_after_month)
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for i in range(1, months_value):
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months(i)
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future_dates = pd.DataFrame(list_months)
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future_dates.columns = ["Dates"]
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fut_date = pd.DataFrame(list_months)
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fut_date.columns = ["Dates"]
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future_dates[["year", "month"]] = future_dates["Dates"].str.split("-", expand=True)
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future_dates.drop(['Dates'], axis=1, inplace=True)
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future_dates["year"] = future_dates["year"].astype(int)
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future_dates["month"] = future_dates["month"].astype(int)
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||||
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||||
|
||||
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||||
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||||
###############################################################################
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||||
userdata['sales']=userdata["sales"].astype(float)
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||||
dependent = userdata[['year','month']]
|
||||
independent = userdata['sales']
|
||||
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||||
import numpy as np
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import pandas as pd
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|
||||
import xgboost
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.model_selection import GridSearchCV
|
||||
from sklearn.metrics import roc_auc_score
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
#model = xgboost.XGBRegressor()
|
||||
from sklearn.ensemble import RandomForestRegressor
|
||||
model = RandomForestRegressor(random_state=1,n_jobs=-1)
|
||||
model.fit(dependent, independent)
|
||||
#future=pd.read_csv('future_dates.csv')
|
||||
|
||||
|
||||
future_prediction = model.predict(future_dates)
|
||||
#print(future_prediction)
|
||||
df=pd.DataFrame(future_prediction)
|
||||
df.columns = ["SALES"]
|
||||
frames = [fut_date, df]
|
||||
|
||||
|
||||
result = pd.concat(frames,axis=1)
|
||||
result['itemname'] = item
|
||||
result['itemid'] =item_id
|
||||
result.columns = ['Date','Predict','ItemName','ItemId']
|
||||
#result['Predict']=result["Predict"].astype(int)
|
||||
result['UpperLimit']=result["Predict"].mean()+result['Predict'].std()*3
|
||||
result['LowerLimit']=result['Predict'].mean()-result['Predict'].std()*3
|
||||
result["LowerLimit"][result["LowerLimit"] < 0] = 0
|
||||
print(result)
|
||||
|
||||
filePath='path.csv'
|
||||
|
||||
|
||||
if os.path.exists(filePath):
|
||||
print('file exist')
|
||||
#userdata = pd.DataFrame(data)
|
||||
result.to_csv('path.csv', mode='a',index=False, header=False)
|
||||
|
||||
else:
|
||||
print("file as it doesn't exists")
|
||||
#result = pd.DataFrame(data)
|
||||
result.to_csv('path.csv', index=False)
|
||||
|
||||
|
||||
|
||||
result=pd.read_csv('path.csv')
|
||||
result.to_json('forcast.json', orient="records")
|
||||
import json
|
||||
|
||||
# open the JSON file and read its contents
|
||||
with open(r'forcast.json', 'r') as f:
|
||||
json_data = json.load(f)
|
||||
|
||||
# print the JSON data
|
||||
#print(json_data)
|
||||
|
||||
#output={"response":"success","result":json_data}
|
||||
#print(output)
|
||||
return json_data
|
||||
|
||||
# frames = [fulldata, result]
|
||||
|
||||
# final = pd.concat(frames)
|
||||
# print('********************************************************')
|
||||
# final['itemname'] = item
|
||||
# final['itemid'] =item_id
|
||||
# final.columns = ['Date','Predict','ItemName','ItemId']
|
||||
# final['upper_limit']=final["Predict"]+final['Predict']*0.2
|
||||
# # final['lower_limit']=final['Predict']-final['Predict']*0.2
|
||||
# # print(final)
|
||||
# # final.to_json('forcast.json', orient="records")
|
||||
|
||||
# with open('forcast.json', 'r') as json_file:
|
||||
# json_load = json.load(json_file)
|
||||
# #url = "https://demo.bizgaze.app/apis/v4/bizgaze/integrations/demandforecast/saveforecast/List"
|
||||
# url='https://qa.bizgaze.app/apis/v4/bizgaze/integrations/demandforecast/saveforecast/List'
|
||||
|
||||
# payload = json.dumps(json_load)#.replace("]", "").replace("[", "")
|
||||
# print(payload)
|
||||
# headers = {
|
||||
# #'Authorization': 'stat 263162e61f084d3392f162eb7ec39b2c',#demo
|
||||
# 'Authorization': 'stat 873f2e6f70b3483e983972f96fbf5ea4',#test
|
||||
# 'Content-Type': 'application/json'
|
||||
# }
|
||||
# response = requests.request("POST", url, headers=headers, data=payload)
|
||||
# print("##############################################################")
|
||||
# print(response.text)
|
||||
|
||||
|
||||
# import time
|
||||
# time.sleep(1)
|
||||
|
||||
|
||||
|
||||
###############################################################################################################################################################
|
||||
|
||||
#####################################################################################################################
|
||||
|
||||
@app.route("/sales_forcast", methods=["GET", "POST"])
|
||||
def sales_forcast():
|
||||
#wise= request.args.get('wise').replace('{','').replace('}','')
|
||||
#Num= request.args.get('value').replace('{','').replace('}','')
|
||||
#print(wise)
|
||||
#print(Num)
|
||||
Dataset = request.get_json()
|
||||
#print(Dataset)
|
||||
import pandas as pd
|
||||
df=pd.DataFrame(Dataset)
|
||||
print(df)
|
||||
# a = Dataset
|
||||
#x = a['wise']
|
||||
# cmd = "python C:\\Users\\Bizga\\Desktop\\forcast\\XGdaywise.py"
|
||||
# os.system(cmd)
|
||||
|
||||
#split=wise
|
||||
wise='month'
|
||||
Num=5
|
||||
if wise=='days':
|
||||
print('daywise groupby')
|
||||
day(Num,df)
|
||||
# cmd = "python C:\\Users\\Bizga\\Desktop\\forcast\\XGdaywise.py"+" "+ Num
|
||||
# os.system(cmd)
|
||||
else:
|
||||
print('monthwise groupby')
|
||||
output=month(Num,df)
|
||||
payload = json.dumps(output)
|
||||
payload_list="["+payload+"]"
|
||||
#payload_list.append(payload)
|
||||
# print(payload)
|
||||
|
||||
# cmd = "python C:\\Users\\Bizga\\Desktop\\forcast\\xgmonthwise.py"+" "+ Num
|
||||
# os.system(cmd)
|
||||
# import json
|
||||
# a={"status":"success"}
|
||||
# payload123 = json.dumps(a)
|
||||
return output
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run(host='0.0.0.0', port=5003)
|
||||
Reference in New Issue
Block a user