Update app.py

app.py

@@ -1,12 +1,179 @@
 import gradio as gr
 import pandas as pd
 import hopsworks
+from datetime import datetime
+import requests
+import json
 import math
 import os
 
 hopsworks_today_path    = "Resources/today_timetable_prediction/today_timetable_prediction.csv"
 hopsworks_tomorrow_path = "Resources/tomorrow_timetable_prediction/tomorrow_timetable_prediction.csv"
 
+def one_day_forward(year, month, day):
+  '''
+  Return "year", "month" and "day" numbers of the day after the inserted day
+  It works for all the possible years from 1592
+  '''
+  if month == 12:
+    if day == 31:
+      day = 1
+      month = 1
+      year = year + 1
+    else:
+        day = day + 1
+
+  elif month == 2:
+    if (day == 28):
+      if (year % 4 == 0):
+        day = 29
+      else:
+        day = 1
+        month = 3
+    elif (day == 29):
+      day = 1
+      month = 3
+    else:
+      day = day + 1
+    
+  elif month == 4 or month == 6 or month == 9 or month == 11:
+    if (day == 30):
+      month = month + 1
+      day = 1
+    else:
+      day = day + 1
+  
+  else:
+    day = day + 1
+
+  return year, month, day
+
+    
+def get_today_date():
+    '''
+    Return today's year, month and day numbers
+    '''
+    # Get today's date through TimeAPI
+    time_url = "https://worldtimeapi.org/api/timezone/Europe/Stockholm"
+    time_response     = requests.get(time_url)
+    time_responseJson = time_response.json()
+
+    # Extract datetime
+    datetime_str    = time_responseJson["datetime"]
+    datetime_object = datetime.fromisoformat(datetime_str[:-6])  # Remove the timezone offset for parsing
+
+    # Extract components from datetime
+    day   = datetime_object.day
+    month = datetime_object.month
+    year  = datetime_object.year
+
+    return year, month, day
+
+
+def get_year_month_label(year, month, mode):
+  '''
+  Return the year_month in the format wanted by the different APIs file structure, by passing 
+  the year, month and the mode. It pads with 0 when needed. The "mode" can be specified 
+  between "hyphen", "underscore" and "empty" and it determines which divider you will find in
+  the year_month_label between the different input passed (e.g. 2024-01 or 20240105)
+  '''
+  year_month_label = ''
+
+  year_label = str(year)
+  month_label = ''
+  if month not in {10, 11, 12}:
+    month_label = '0' + str(month)
+  else:
+    month_label = str(month)
+  
+  if mode == 'hyphen':
+    year_month_label = year_label + '-' + month_label
+  elif mode == 'underscore':
+    year_month_label = year_label + '_' + month_label
+  elif mode == 'empty':
+    year_month_label = year_label + month_label
+
+  return year_month_label
+
+
+def get_date_label(year, month, day, mode):
+  ''' 
+  Return the date in the format wanted by the different APIs file structure, by passing 
+  the year, month, day and the mode. It pads with 0 when needed. The "mode" can be specified 
+  between "hyphen", "underscore" and "empty" and it determines which divider you will find in
+  the date_label between the different input passed (e.g. 2024-01-05 or 20240105)
+  '''
+
+  date_label = ''
+  year_month_label = get_year_month_label(year, month, mode)
+  
+  day_label = ''
+  if day < 10:
+      day_label = '0' + str(day)
+  else:
+      day_label = str(day)
+  
+  if mode == 'hyphen':
+    date_label = year_month_label + '-' + day_label
+  elif mode == 'underscore':
+    date_label = year_month_label + '_' + day_label
+  elif mode == 'empty':
+    date_label = year_month_label + day_label
+
+  return date_label
+
+    
+def get_name_of_cities():
+    yyyy, mm, dd = get_today_date()
+    yyyy1, mm1, dd1 = one_day_forward(yyyy, mm, dd)
+
+    date_label = get_date_label(yyyy, mm, dd, 'hyphen')
+    date_label1 = get_date_label(yyyy1, mm1, dd1, 'hyphen')
+
+    # Create the request_url, then get the subscription key from Swedavia API and set them in the header
+    swedavia_url     = 'https://api.swedavia.se/flightinfo/v2/ARN/departures/' + date_label
+    swedavia_url1     = 'https://api.swedavia.se/flightinfo/v2/ARN/departures/' + date_label1
+
+    subscription_key = os.environ['SWEDAVIA_API_KEY']
+    headers = {
+        "Ocp-Apim-Subscription-Key": subscription_key,
+        "Accept": "application/json",
+        "Content-Type": 'application/json',
+    }
+
+    # Make the API request for Swedavia API
+    response = requests.get(swedavia_url, headers = headers)
+    response1 = requests.get(swedavia_url, headers = headers)
+    flights_swedavia = response.json()
+    flights_swedavia1 = response1.json()
+
+
+    # Load JSON data into a Python dictionary
+    arrival_airports_info = [{
+        'ArrivalAirportIata': flight.get('flightLegIdentifier', {}).get('arrivalAirportIata'),
+        'ArrivalAirportEnglish': flight.get('arrivalAirportEnglish')} 
+        for flight in flights_swedavia.get('flights', [])]
+    df = pd.DataFrame(arrival_airports_info)
+    arrival_airports_info1 = [{
+        'ArrivalAirportIata': flight.get('flightLegIdentifier', {}).get('arrivalAirportIata'),
+        'ArrivalAirportEnglish': flight.get('arrivalAirportEnglish')} 
+        for flight in flights_swedavia1.get('flights', [])]
+    df1 = pd.DataFrame(arrival_airports_info1)
+
+    total_df = pd.DataFrame({'ArrivalAirportEnglish': pd.concat([df1['ArrivalAirportEnglish'], df['ArrivalAirportEnglish']]).drop_duplicates().reset_index(drop=True),'ArrivalAirportIata': pd.concat([df1['ArrivalAirportIata'], df['ArrivalAirportIata']]).drop_duplicates().reset_index(drop=True)})
+    total_df.sort_values('ArrivalAirportEnglish', inplace=True)
+
+    return total_df
+
+
+def create_single_dataframe_from(dataframe):
+    df = get_name_of_cities()
+    df['ArrivalAirportIata'] = df['ArrivalAirportIata'].str.lower()
+    merged_df = pd.merge(df, dataframe, left_on='ArrivalAirportIata', right_on='airport', how='inner')
+    # Drop the duplicate 'ArrivalAirportIata' column
+    merged_df = merged_df.drop('ArrivalAirportIata', axis=1)
+
+    return merged_df
 
 def get_dataframe(online_dataframe_path):
     # Connect to Hopsworks File System
@@ -19,6 +186,8 @@ def get_dataframe(online_dataframe_path):
     # Read dataframe from local path, drop duplicates, return
     dataframe = pd.read_csv(dataframe_path)
     dataframe.drop_duplicates(inplace=True)
+
+    dataframe = create_single_dataframe_from(dataframe)
     return dataframe
 
 def get_tomorrow_dataframe():
@@ -27,18 +196,42 @@ def get_tomorrow_dataframe():
 def get_today_dataframe():
     return get_dataframe(hopsworks_tomorrow_path)
 
-def get_dataframe_of(day):
-    if   (day.lower() == 'today'):
-        return get_today_dataframe()
-    elif (day.lower() == 'tomorrow'):
-        return get_tomorrow_dataframe()
+def get_metrics():
+    # Connect to Hopsworks File System
+    dataframe = hopsworks.login(api_key_value = os.environ['HOPSWORKS_API_KEY'])
+    dataframe = dataframe.get_feature_store()
+    dataframe = dataframe.get_feature_group(name = 'model_performance', version = 1)
+    dataframe = dataframe.read(dataframe_type = 'pandas')
+    dataframe = dataframe.sort_values('timestamp')
+    dataframe = dataframe[['timestamp', 'mae', 'dateset_size']].rename(columns={'dateset_size':'Dataset Size', 'mae':'Mean Absolute Error', 'timestamp':'Date'})
+    dataframe = dataframe.sort_values(['Date'], ascending = False)
+    return dataframe
+
+selected_columns = ['destination', 'airport code', 'flight number', 'ontime', 'delayed']
+ciccio, pasticcio  = pd.DataFrame(), pd.DataFrame()
+cities_datafram    = get_name_of_cities()
+ciccio             = get_today_dataframe()
+ciccio             = ciccio.rename(columns={'airport':'airport code' , 'ArrivalAirportEnglish':'destination', 'flight_number':'flight number'})
+today_dataframe    = ciccio[selected_columns]
+pasticcio          = get_tomorrow_dataframe()
+pasticcio          = pasticcio.rename(columns={'airport':'airport code','ArrivalAirportEnglish':'destination', 'flight_number':'flight number'})
+tomorrow_dataframe = pasticcio[selected_columns]
+performance_metric = get_metrics()
+
 
 def get_possible_destinations():
-    today_df, tomorrow_df = get_today_dataframe(), get_tomorrow_dataframe()
-    total_df = pd.DataFrame({'airport': pd.concat([today_df['airport'], tomorrow_df['airport']]).drop_duplicates().reset_index(drop=True).sort_values()})
-    total_dest = (total_df['airport']).tolist()
+    global today_dataframe, tomorrow_dataframe
+    today_df, tomorrow_df = today_dataframe, tomorrow_dataframe
+    total_df = pd.DataFrame({'destination': pd.concat([today_df['destination'], tomorrow_df['destination']]).drop_duplicates().reset_index(drop=True).sort_values()})
+    total_dest = (total_df['destination']).tolist()
     return total_dest
 
+def get_dataframe_of(day):
+    global cities_datafram, today_dataframe, tomorrow_dataframe
+    if   (day.lower() == 'today'):
+        return today_dataframe
+    elif (day.lower() == 'tomorrow'):
+        return tomorrow_dataframe
 
 def get_specific_flights(day, max_delay, departure_hour, ampm, weather, destinations, yes):
     df = get_dataframe_of(day)
@@ -56,7 +249,7 @@ def get_specific_flights(day, max_delay, departure_hour, ampm, weather, destinat
 
     # Get flight with less delay than the given and from the destinations selected, of the right day
     df['delay'] = (df['delayed'] - df['ontime']).dt.total_seconds() / 60
-    filtered_df = df.loc[(df['delay'] < max_delay) & (df['airport'].isin(destinations)), ['airport', 'flight_number', 'ontime', 'delayed']]
+    filtered_df = df.loc[(df['delay'] < max_delay) & (df['destination'].isin(destinations)), ['destination', 'flight number', 'ontime', 'delayed']]
  
     # Convert the string to datetime, then the datetime column to HH:MM
     filtered_df['ontime'] = pd.to_datetime(filtered_df['ontime'])
@@ -69,16 +262,9 @@ def get_specific_flights(day, max_delay, departure_hour, ampm, weather, destinat
 def full_day_departure(day):
     return get_dataframe_of(day)
 
-def get_metrics():
-    # Connect to Hopsworks File System
-    dataframe = hopsworks.login(api_key_value = os.environ['HOPSWORKS_API_KEY'])
-    dataframe = dataframe.get_feature_store()
-    dataframe = dataframe.get_feature_group(name = 'model_performance', version = 1)
-    dataframe = dataframe.read(dataframe_type = 'pandas')
-    dataframe = dataframe.sort_values('timestamp')
-    dataframe = dataframe[['timestamp', 'mae', 'dateset_size']].rename(columns={'dateset_size':'Dataset Size', 'mae':'Mean Absolute Error', 'timestamp':'Date'})
-    dataframe = dataframe.sort_values(['Date'], ascending = False)
-    return dataframe
+def get_performance():
+    global performance_metric
+    return performance_metric
 
 specific_flights = gr.Interface(
     get_specific_flights,
@@ -104,9 +290,9 @@ total_departure = gr.Interface(
     "dataframe",
 )
 
-metrics = gr.Interface(fn = get_metrics, inputs=None, outputs='dataframe', allow_flagging="never")
+metrics = gr.Interface(fn = get_performance, inputs=None, outputs='dataframe', allow_flagging="never")
 
 #flights.launch()
 
-    interface = gr.TabbedInterface([specific_flights, total_departure, metrics], {"Full Day Departure", "Specific Flights", "Model Performances"})
+interface = gr.TabbedInterface([specific_flights, total_departure, metrics], {"Full Day Departure", "Specific Flights", "Model Performances"})
 interface.launch()