Update app.py

app.py

@@ -16,14 +16,13 @@ fs = project.get_feature_store()
 	
 
 def air_quality(city):
-
-    today=datetime.date.today()
-    weather_df = get_weather_data_weekly(today)
-
-    weather_df['aqi'] = 0
-    weather_df['city'] = 0
-
-    #weather_df = weather_df.drop(columns=["precipprob", "uvindex", "date","city","conditions"]).fillna(0)
+    weather_df = pd.DataFrame()
+    for i in range(8):
+        weather_data = get_weather_df([get_weather_data("Helsinki",(datetime.now() + timedelta(days=i)).strftime("%Y-%m-%d"))])
+        weather_df = weather_df.append(weather_data)
+            
+    #weather_df = weather_df.drop(columns=["tempmax", "tempmin", "feelslikemax", "feelslikemin", "feelslike", "dew", "precipprob", "precipcover", "snow", "snowdepth", "windgust", "windspeed", "winddir", "solarradiation","solarenergy","pressure"]).fillna(0)
+        
     weather_df.drop(['tempmax'], inplace = True, axis = 1)
     weather_df.drop('tempmin', inplace = True, axis = 1)
     weather_df.drop('feelslikemax', inplace = True, axis = 1)
@@ -40,18 +39,20 @@ def air_quality(city):
     weather_df.drop('solarradiation', inplace = True, axis = 1)
     weather_df.drop('solarenergy', inplace = True, axis = 1)
     weather_df.drop('pressure', inplace = True, axis = 1)
-    
+
+    weather_df = data_encoder(weather_df)
+
     mr = project.get_model_registry()
     model = mr.get_model("aqi_model_gb", version=1)
     model_dir = model.download()
     model = joblib.load(model_dir + "/gb_model.pkl")
-	    
+            
     preds = model.predict(weather_df)
-	
+        
     predictions = ''
     for k in range(7):
         predictions += "Predicted AQI on  " + (datetime.now() + timedelta(days=k)).strftime('%Y-%m-%d') + ":      " + str(int(preds[k]))+"\n"
-	        
+                
         print(predictions)
     return predictions
 	
@@ -62,4 +63,4 @@ description="Input a value to get next weeks AQI prediction for London", inputs=
 
 	    
 if __name__ == "__main__":
-	demo.launch()
+	demo.launch()