Update app.py

app.py

@@ -22,7 +22,7 @@ market = ['Badakhshan', 'Badghis', 'Baghlan', 'Balkh', 'Bamyan', 'Daykundi', 'Fa
 commodity = ['Bread', 'Oil_cooking', 'Pulses', 'Rice_high', 'Rice_low', 'Salt', 'Sugar', 'Wheat', 'Wheatflour_high', 'Wheatflour_low']
 
 
-def predict_price(year, month, market, food):
+def predict_price(year, month, markets, food):
   market_empty = np.zeros(34)
   market_name = ['Badakhshan', 'Badghis', 'Baghlan', 'Balkh', 'Bamyan', 'Daykundi', 'Farah', 'Faryab', 'Ghazni', 'Ghor', 'Hilmand', 'Hirat', 'Jawzjan' 'Kabul', 'Kandahar', 'Kapisa', 'Khost', 'Kunar', 'Kunduz', 'Laghman', 'Logar', 'Maidan Wardak', 'Nangarhar', 'Nimroz', 'Nuristan', 'Paktika', 'Paktya', 'Panjsher', 'Parwan', 'Samangan', 'Sar-e-Pul', 'Takhar', 'Uruzgan', 'Zabul']
   market = []
@@ -44,7 +44,7 @@ def predict_price(year, month, market, food):
 
   date = ((year*10000+month*100+15)-20200000)/100000
 
-  input_data = np.concatenate([arrays_with_names[market], commodity_with_names[food], [date]]).reshape(1, -1)
+  input_data = np.concatenate([arrays_with_names[markets], commodity_with_names[food], [date]]).reshape(1, -1)
  
   prediction = model.predict(input_data)