Working API

api.py

@@ -30,7 +30,7 @@
 # Load our model into memory.
 # Please update this path to reflect your own trained model.
 static_model = load_model(
-    path_to_model='assets/trained-models/load_shortfall_simple_lm_regression.pkl')
+    path_to_model='./assets/trained-models/linear_model.pkl')
 
 print ('-'*40)
 print ('Model successfully loaded')
@@ -62,4 +62,4 @@ def model_prediction():
 # This will allow Flask to automatically restart itself everytime you
 # update your API code.
 if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=5000, debug=False)
+    app.run(host='0.0.0.0', port=5000, debug=True)

model.py

@@ -26,8 +26,12 @@
 import pandas as pd
 import pickle
 import json
+from matplotlib.pyplot import axis
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import *
 
 def _preprocess_data(data):
+
     """Private helper function to preprocess data for model prediction.
 
     NB: If you have utilised feature engineering/selection in order to create
@@ -58,10 +62,14 @@ def _preprocess_data(data):
     # ---------------------------------------------------------------
 
     # ----------- Replace this code with your own preprocessing steps --------
-    predict_vector = feature_vector_df[['Madrid_wind_speed','Bilbao_rain_1h','Valencia_wind_speed']]
-    # ------------------------------------------------------------------------
+
 
+
+
+    feature_vector_df.fillna(0, inplace = True)
+    predict_vector = feature_vector_df[['Madrid_temp', 'Barcelona_temp_min', 'Bilbao_pressure', 'Seville_temp', 'Valencia_wind_speed']]
     return predict_vector
+  # ------------------------------------------------------------------------
 
 def load_model(path_to_model:str):
     """Adapter function to load our pretrained model into memory.