Update 'Demand_forcasting/forcasting.py'

Demand_forcasting/forcasting.py

@@ -1,7 +1,5 @@
 from flask import Flask, render_template, send_file, request, redirect, Response
 import os
-
-
 import pandas as pd
 import warnings
 import json
@@ -9,10 +7,6 @@ import requests
 from urllib.request import urlopen
 warnings.filterwarnings("ignore")
 
-
-
-
-
 app = Flask(__name__)
 
 
@@ -317,13 +311,14 @@ def month(Num,df):
     
     #df1=pd.read_csv(r'./upload/' + name)
     #df1=df1[df1['obdate']!='01/01/0001']
-    userdata.columns = ['itemname','sum','journaldate','itemid']
+    #userdata.columns = ['itemname','sum','journaldate','itemid']
+    userdata.columns = ['journaldate','sum','itemid','itemname']
     # import pandas as pd
     # userdata = pd.read_csv(r'C:\Users\Bizga\Desktop\forcast\5yearsitems.csv')
     # itemid = userdata[['itemname', 'itemid']]
     #userdata['journaldate'] = pd.to_datetime(userdata['journaldate'])
     userdata["journaldate"] = userdata["journaldate"].astype(str)
-    userdata[["year", "month", "day"]] = userdata["journaldate"].str.split("-", expand = True)
+    userdata[["day","month","year"]] = userdata["journaldate"].str.split("-", expand = True)
     userdata['Month-Year']=userdata['year'].astype(str)+'-'+userdata['month'].astype(str)
     item_unique_name = userdata['itemname'].unique()
     
@@ -495,6 +490,33 @@ def month(Num,df):
         result['LowerLimit']=result['Predict'].mean()-result['Predict'].std()*3
         result["LowerLimit"][result["LowerLimit"] < 0] = 0
         print(result)
+        # 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)
         
         filePath='path.csv'
 
@@ -574,6 +596,7 @@ def sales_forcast():
     #print(Dataset)
     import pandas as pd
     df=pd.DataFrame(Dataset)
+    #df=pd.read_csv('ItemWiseQuantity_STC.csv')
     print(df)
     # a = Dataset
     #x = a['wise']
@@ -601,7 +624,7 @@ def sales_forcast():
     # import json
     # a={"status":"success"}
     # payload123 = json.dumps(a)
-    return output
+    return "done"
 
 if __name__ == "__main__":
     app.run(host='0.0.0.0', port=8082)