Spaces:
Sleeping
Sleeping
File size: 5,074 Bytes
7b17a2d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 |
import gradio as gr
import pandas as pd
from flight_distance import *
from optimize import *
from weather import *
# Load airport data and aircraft data from CSV files
airport_df = pd.read_csv(r'airport.csv') # Adjust the path to your CSV file
aircraft_df = pd.read_csv(r'aircraft.csv') # Adjust the path to your CSV file
# Create a combined option list with both IATA codes and airport names
airport_options = [f"{row['IATA']} - {row['Airport_Name']}" for _, row in airport_df.iterrows()]
# Ensure the correct column is used for aircraft types
aircraft_type_column = 'Aircraft'
aircraft_options = aircraft_df[aircraft_type_column].tolist()
# Gradio function to determine if a route can be flown
def check_route(airport_selections, aircraft_type):
# Extract IATA codes from the selected options
airports = [selection.split(" - ")[0] for selection in airport_selections]
# Step 1: Get Airport Coordinates
lat_long_dict = get_airport_lat_long(airports)
# Step 2: Calculate Distances between each node (airports)
trip_distance = calculate_distances(airports)
# Step 3: Get on-route weather
raw_weather = fetch_weather_for_all_routes(airports, lat_long_dict)
route_factors = extract_route_factors(raw_weather)
# Step 4: Ensure the graph is bidirectional (undirected)
for (a, b), dist in list(trip_distance.items()):
trip_distance[(b, a)] = dist
# Step 5: Find the optimal route based on weather, temperature, and distance
optimal_route, optimal_distance = find_optimal_route(airports, trip_distance, route_factors)
# Step 6: Fetch Aircraft Details
aircraft_specs = get_aircraft_details(aircraft_type)
# Check if aircraft details were retrieved successfully
if isinstance(aircraft_specs, str):
return {"Error": aircraft_specs} # Return error message if aircraft not found
# Prepare sector-wise details for flight time, fuel required, and refueling need
sector_details = []
refuel_required = False # Flag to track if refueling is required
for i in range(len(optimal_route) - 1):
segment = (optimal_route[i], optimal_route[i + 1])
segment_distance = trip_distance.get(segment) or trip_distance.get((segment[1], segment[0]))
# Calculate fuel and time for this sector
fuel, time = calculate_fuel_and_time_for_segment(segment_distance, aircraft_specs)
sector_info = {
"Sector": f"{optimal_route[i]} -> {optimal_route[i+1]}",
"Fuel Required (kg)": round(fuel, 2),
"Flight Time (hrs)": round(time, 2)
}
# Check if refueling is required for this sector
if fuel > aircraft_specs['Max_Fuel_Capacity_kg']:
sector_info["Refuel Required"] = "Yes"
refuel_required = True
else:
sector_info["Refuel Required"] = "No"
sector_details.append(sector_info)
# Check the final leg (return to the starting point)
last_segment = (optimal_route[-1], optimal_route[0])
last_segment_distance = trip_distance.get(last_segment) or trip_distance.get((last_segment[1], last_segment[0]))
fuel, time = calculate_fuel_and_time_for_segment(last_segment_distance, aircraft_specs)
# Add final leg details
final_leg_info = {
"Sector": f"{optimal_route[-1]} -> {optimal_route[0]}",
"Fuel Required (kg)": round(fuel, 2),
"Flight Time (hrs)": round(time, 2)
}
if fuel > aircraft_specs['Max_Fuel_Capacity_kg']:
final_leg_info["Refuel Required"] = "Yes"
refuel_required = True
else:
final_leg_info["Refuel Required"] = "No"
sector_details.append(final_leg_info)
# Step 7: Prepare and return result
if refuel_required:
result = {
"Optimal Route": " -> ".join(optimal_route) + f" -> {optimal_route[0]}",
"Total Round Trip Distance": str(optimal_distance) + " km",
"Can Fly Entire Route": "No, refueling required in one or more sectors.",
"Sector Details": sector_details
}
else:
result = {
"Optimal Route": " -> ".join(optimal_route) + f" -> {optimal_route[0]}",
"Total Round Trip Distance": str(optimal_distance) + " km",
"Can Fly Entire Route": "Yes, no refueling required.",
"Sector Details": sector_details
}
return result
# Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("## Airport Route Feasibility Checker")
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="Result")
# Connect the button click to the check_route function
check_button.click(fn=check_route, inputs=[airport_selector, aircraft_selector], outputs=result_output)
# Launch the Gradio app
demo.launch()
|