Update app.py

app.py

@@ -1,76 +1,80 @@
 import gradio as gr
 import pandas as pd
 import hopsworks
+import math
 import os
 
-# Get data from online source
-hopsworks_api_key  = os.environ['HOPSWORKS_API_KEY']
-project            = hopsworks.login(api_key_value = hopsworks_api_key)
-dataset_api        = project.get_dataset_api()
-today_path         = dataset_api.download("Resources/today_timetable_prediction/today_timetable_prediction.csv", overwrite=True)
-tomorrow_path      = dataset_api.download("Resources/tomorrow_timetable_prediction/tomorrow_timetable_prediction.csv", overwrite=True)
-
-
 def download_data(path):
     os.path.abspath(path)
     df = pd.read_csv(path)
+    wanted_df = df[['airport', 'flight_number', 'ontime', 'delayed']].copy()
+    return wanted_df
 
-    wanted_df = pd.DataFrame()
+def access_online_dataframe():
+    # Get data from online source
+    hopsworks_api_key  = os.environ['HOPSWORKS_API_KEY']
+    project            = hopsworks.login(api_key_value = hopsworks_api_key)
+    dataset_api        = project.get_dataset_api()
+    today_path         = dataset_api.download("Resources/today_timetable_prediction/today_timetable_prediction.csv", overwrite=True)
+    tomorrow_path      = dataset_api.download("Resources/tomorrow_timetable_prediction/tomorrow_timetable_prediction.csv", overwrite=True)
+    today_df           = download_data(today_path)
+    tomorrow_df        = download_data(tomorrow_path)
+    return today_df, tomorrow_df
 
-    headers=["FlightNumber", "Destination", "ScheduleDep", "ActualDep"]
-    columns = ['airport', 'flight_number', 'ontime', 'delayed']
 
-    wanted_df[headers] = df[columns]
-    return wanted_df
+def get_possible_destinations():
+    today_df, tomorrow_df = access_online_dataframe()
+    total_df = pd.DataFrame({'airport': pd.concat([today_df['airport'], tomorrow_df['airport']]).drop_duplicates().reset_index(drop=True)})
+    total_dest = (total_df['airport']).tolist()
+    return total_dest
 
-def get_num_rows(path):
-    os.path.abspath(path)
-    df = pd.read_csv(path)
-    return df.shape[0]
 
-io1 = gr.Interface(
-    
-    [
-        gr.Dataframe(
-            headers       = ["FlightNumber", "Destination", "ScheduleDep", "ActualDep"],
-            datatype      = ["str", "str", "str", "str"],
-            row_count     = get_num_rows(today_path),
-            col_count     = (4,"fixed"),
-            interactive   = False,
-            column_widths = '20%',
-            show_label    = True,
-            label         = 'Today Flight Delays Prediction',
-            values        = download_data(today_path)
-        ),
-    ],
-    "dataframe",
-    description="Here you can see how much delayed your flight will be!",
-)
+def get_specific_flights(day, max_delay, departure_hour, ampm, weather, destinations, yes):
+    today_df, tomorrow_df = access_online_dataframe()
+    df = pd.DataFrame()
+    if(day == 'today'):
+        df = today_df
+    else:
+        df = tomorrow_df
+
+    # Remove unwanted destinations
+    destinations = [dest for dest in destinations if dest not in ["That's a reason why I travel alone...", "I prefer not to say"]]
+
+    # Select only flight during the same departure hour
+    df['departure_hour'] = df['departure_time'].str.split(':').str[0].astype(int)
+    df = df[df['departure_hour'] == departure_hour].drop(columns=['departure_hour'])
 
+    # Convert time columns to datetime objects
+    df['ontime'] = pd.to_datetime(df['ontime'], format='%H:%M')
+    df['delayed'] = pd.to_datetime(df['delayed'], format='%H:%M')
 
-io2 = gr.Interface(
+    # 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']]
+ 
+    # Convert the string to datetime, then the datetime column to HH:MM
+    filtered_df['ontime'] = pd.to_datetime(filtered_df['ontime'])
+    filtered_df['ontime'] = filtered_df['ontime'].dt.strftime('%H:%M')
+    filtered_df['delayed'] = pd.to_datetime(filtered_df['delayed'])
+    filtered_df['delayed'] = filtered_df['delayed'].dt.strftime('%H:%M')
     
+    return filtered_df
+
+
+
+flights = gr.Interface(
+    get_specific_flights,
     [
-        gr.Dataframe(
-            headers       = ["FlightNumber", "Destination", "ScheduleDep", "ActualDep"],
-            datatype      = ["str", "str", "str", "str"],
-            row_count     = get_num_rows(tomorrow_path),
-            col_count     = (4,"fixed"),
-            interactive   = False,
-            column_widths = '20%',
-            show_label    = True,
-            label         = 'Tomorrow Flight Delays Prediction',
-            values        = download_data(tomorrow_path)
-        ),
+        gr.Radio(["today", "tomorrow"], type="value", label="Day", info="When do you have the plane?"),
+        gr.Slider(0, 50, value=20, label="Possible Delay", info="How unfortunate do you wanna be?"),
+        gr.Row(gr.Number(precision=0, minimum=0, maximum=23, label="Departure Time"), gr.Radio(["am", "pm"], type="index", info="It's the same, no worries!")),
+        gr.CheckboxGroup(["Yes, it's cloudy", "I am not in Stockholm"], label="Weather", info="Is it a typical Stockholm day?"),
+        gr.Dropdown(get_possible_destinations() + ["That's a reason why I travel alone...", "I prefer not to say"], 
+                    type = "value", multiselect=True, label="Destination", value=["That's a reason why I travel alone..."],
+                    info="Are you just curious or you are actually going somewhere? Where? With who?"),
+        gr.Radio(["Yes", "Yes", "Yes"], type="index", label="Let's guess?", info="We know that you'll say yes!"),
     ],
     "dataframe",
-    description="Here you can see how much delayed your flight will be!",
 )
 
-
-
-io2 = gr.Interface()
-
-gr.TabbedInterface(
-    [io1, io2], {"Today's Flight", "Tomorrow's Flight"}
-).launch(share = True)
+flights.launch()