clean up

currency.py → Dataset_prep/currency.py

@@ -1,8 +1,8 @@
 import pandas as pd
 
 # Load the CSV files
-csv1_file = 'currencyrates.csv'  # Replace with the path to your CSV1 file
-csv2_file = 'countrycurrency.csv'  # Replace with the path to your CSV2 file
+csv1_file = r'currencyrates.csv'  # Replace with the path to your CSV1 file
+csv2_file = r'countrycurrency.csv'  # Replace with the path to your CSV2 file
 
 # Read the CSVs into DataFrames
 df1 = pd.read_csv(csv1_file)  # This CSV contains just currency codes

flight_distance.py

@@ -3,7 +3,7 @@ from math import *
 
 def get_aircraft_details(aircraft_type):
     # Load the CSV file
-    csv_file = 'aircraft.csv'  # Replace with the actual path if needed
+    csv_file = r'aircraft.csv'  # Replace with the actual path if needed
     df = pd.read_csv(csv_file)
 
     # Check if the aircraft type exists in the DataFrame
@@ -36,7 +36,7 @@ def get_airport_lat_long(identifiers):
         return "Please provide at least two airport identifiers."
 
     # Load the CSV file
-    csv_file = 'airport.csv'  # Replace with the actual path if needed
+    csv_file = r'airport.csv'  # Replace with the actual path if needed
     df = pd.read_csv(csv_file)
 
     # Efficiently filter rows where the 'Name' or 'IATA' matches any of the provided identifiers

main.py

@@ -14,7 +14,7 @@ trip_distance = calculate_distances(airport_identifiers)
 print("Distance b/w Airports: \n",trip_distance)
 
 #Get onroute weather
-weather = fetch_weather_along_route(airport_identifiers,lat_long_dict)
+weather = fetch_weather_for_all_routes(airport_identifiers,lat_long_dict)
 print("On Route weather: \n", weather)
 
 # # Ensure the graph is bidirectional (undirected)