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| import gradio as gr | |
| import pandas as pd | |
| from map_generator import * | |
| from flight_distance import * | |
| from optimize import * | |
| from weather import * | |
| # Load airport data and aircraft data from Parquet and CSV files | |
| airport_df = pd.read_parquet(r'airport.parquet') # Adjust the path to your Parquet file | |
| aircraft_df = pd.read_csv(r'aircraft.csv') # Adjust the path to your CSV file | |
| airport_options = [f"{row['IATA']} - {row['Airport_Name']}" for _, row in airport_df.iterrows()] | |
| airports_dict = {row['IATA']: row['Airport_Name'] for _, row in airport_df.iterrows()} # For map display | |
| # Ensure the correct column is used for aircraft types | |
| aircraft_type_column = 'Aircraft' | |
| aircraft_options = aircraft_df[aircraft_type_column].tolist() | |
| def check_route(airport_selections, aircraft_type): | |
| airports = [selection.split(" - ")[0] for selection in airport_selections] | |
| lat_long_dict = get_airport_lat_long(airports) | |
| trip_distance = calculate_distances(airports) | |
| raw_weather = fetch_weather_for_all_routes(airports, lat_long_dict) | |
| route_factors = extract_route_factors(raw_weather) | |
| for (a, b), dist in list(trip_distance.items()): | |
| trip_distance[(b, a)] = dist | |
| optimal_route, optimal_distance = find_optimal_route(airports, trip_distance, route_factors) | |
| aircraft_specs = get_aircraft_details(aircraft_type) | |
| if isinstance(aircraft_specs, str): | |
| return {"Error": aircraft_specs}, "" | |
| feasibility_result = check_route_feasibility(optimal_route, trip_distance, aircraft_specs) | |
| map_html = create_route_map(airports_dict, lat_long_dict, optimal_route, feasibility_result["Refuel Sectors"]) | |
| if feasibility_result["Can Fly Entire Route"]: | |
| result = { | |
| "Optimal Route": " -> ".join(optimal_route) + f" -> {optimal_route[0]}", | |
| "Total Round Trip Distance": f"{optimal_distance} km", | |
| "Total Fuel Required": feasibility_result["Total Fuel Required (kg)"], | |
| "Total Flight Time": feasibility_result["Total Flight Time (hrs)"], | |
| "Can Fly Entire Route": "Yes", | |
| "Sector Details": feasibility_result["Sector Details"] | |
| } | |
| else: | |
| result = { | |
| "Optimal Route": " -> ".join(optimal_route) + f" -> {optimal_route[0]}", | |
| "Total Round Trip Distance": f"{optimal_distance} km", | |
| "Can Fly Entire Route": "No, refueling required in one or more sectors.", | |
| "Sector Details": feasibility_result["Sector Details"] | |
| } | |
| return result, map_html | |
| # Gradio Interface | |
| with gr.Blocks(theme=gr.themes.Default()) as demo: | |
| gr.Markdown("## Airport Route Feasibility Checker") | |
| # Place components in two columns for results and map | |
| with gr.Row(): | |
| with gr.Column(): | |
| airport_selector = gr.Dropdown(airport_options, multiselect=True, label="Select Airports (IATA - Name)") | |
| aircraft_selector = gr.Dropdown(aircraft_options, label="Select Aircraft Type") | |
| check_button = gr.Button("Check Route Feasibility") | |
| result_output = gr.JSON(label="Feasibility Result (Route, Fuel, Refueling Info)") | |
| with gr.Column(): | |
| gr.Markdown("## Route Map") | |
| map_output = gr.HTML(label="Interactive Route Map with Refueling Sectors") | |
| # Connect the button click to the check_route function | |
| check_button.click( | |
| fn=check_route, | |
| inputs=[airport_selector, aircraft_selector], | |
| outputs=[result_output, map_output] | |
| ) | |
| # Launch the Gradio app | |
| demo.launch() |