Refactor flight route UI and main script: Update airport options and add note about dataset limitations

README.md

@@ -18,8 +18,9 @@ license: mit
 
 This project focuses on optimizing flight routes to minimize travel time and costs using advanced algorithms and data analysis techniques.
 
-## Features
+> **Note:** The actual flight time and performance may vary since the dataset used is very rudimentary. In the real world, the same aircraft can have different internal configurations, leading to variations in flight time and fuel consumption.
 
+## Features
 - Efficient route calculation
 - Cost optimization
 

app.py

@@ -8,7 +8,7 @@ from weather import *
 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()]
+airport_options = [f"{row['IATA']} - {row['Airport_Name']} - {row['Country']}" 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
@@ -75,6 +75,8 @@ with gr.Blocks(theme=gr.themes.Default()) as demo:
         inputs=[airport_selector, aircraft_selector], 
         outputs=[result_output, map_output]
     )
+    
+    gr.Markdown("**Note:** The actual flight time and performance may vary since the dataset used is very rudimentary. In the real world, the same aircraft can have different internal configurations, leading to variations in flight time and fuel consumption.")
 
 # Launch the Gradio app
 demo.launch()