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| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| from models.neural_network.inference import load_model_and_preprocessor | |
| # Load the pre-trained model and preprocessor | |
| nn_model, nn_preprocessor = load_model_and_preprocessor('nn_model.keras', 'nn_preprocessor.pkl') | |
| # Load the unique aircraft data and airport distances | |
| aircraft_data = pd.read_csv('aircraft_data.csv').drop_duplicates(subset='model') | |
| airport_data = pd.read_csv('airport_distances.csv') | |
| def predict_fuel_burn(model_name, origin, destination, seats, distance): | |
| # Validate the distance against seats | |
| max_seats = aircraft_dict[model_name]['seats'] | |
| if seats > max_seats: | |
| return f"The {model_name} aircraft has a maximum of {max_seats} seats." | |
| if seats <= 0: | |
| return "The number of seats must be greater than 0." | |
| if distance <= 0: | |
| return "The distance must be greater than 0." | |
| # Prepare the input data for the model | |
| data = { | |
| 'model': [model_name], | |
| 'Origin_Airport': [origin], | |
| 'Destination_Airport': [destination], | |
| 'seats': [seats], | |
| 'distance': [distance], | |
| 'J/T': [aircraft_dict[model_name]['J/T']], | |
| 'CAT': [aircraft_dict[model_name]['CAT']], | |
| '_Manufacturer': [aircraft_dict[model_name]['_Manufacturer']], | |
| 'dist': [distance] | |
| } | |
| df = pd.DataFrame(data) | |
| # Make the prediction | |
| fuel_burn_prediction_nn = nn_model.predict(nn_preprocessor.transform(df))[0] | |
| return f" {fuel_burn_prediction_nn[0]:.2f} kg" | |
| def update_fields(model_name): | |
| return { | |
| jt: gr.update(value=aircraft_dict[model_name]['J/T']), | |
| cat: gr.update(value=aircraft_dict[model_name]['CAT']), | |
| manufacturer: gr.update(value=aircraft_dict[model_name]['_Manufacturer']) | |
| } | |
| def update_destination_options(origin): | |
| destinations = airport_data[airport_data['Origin_Airport'] == origin]['Destination_Airport'].unique() | |
| return gr.update(choices=list(destinations)) | |
| def update_distance(origin, destination): | |
| distance_value = airport_dict.get((origin, destination), {}).get('distance', 'Distance not found') | |
| if distance_value == 'Distance not found': | |
| return gr.update(value=0) # Return 0 if distance is not found | |
| return gr.update(value=distance_value) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## Fuel Burn Prediction") | |
| with gr.Row(): | |
| model_name = gr.Dropdown( | |
| label="Aircraft Model", | |
| choices=list(aircraft_dict.keys()), | |
| value=list(aircraft_dict.keys())[0], | |
| ) | |
| origin = gr.Dropdown( | |
| label="Origin Airport", | |
| choices=sorted(airport_data['Origin_Airport'].unique()) | |
| ) | |
| destination = gr.Dropdown( | |
| label="Destination Airport", | |
| choices=[] | |
| ) | |
| with gr.Row(): | |
| jt = gr.Textbox(label="J/T", interactive=False) | |
| cat = gr.Textbox(label="CAT", interactive=False) | |
| manufacturer = gr.Textbox(label="Manufacturer", interactive=False) | |
| seats = gr.Number(label="Seats") | |
| distance = gr.Number(label="Distance", interactive=False) | |
| model_name.change(fn=update_fields, inputs=model_name, outputs=[jt, cat, manufacturer]) | |
| origin.change(fn=update_destination_options, inputs=origin, outputs=destination) | |
| destination.change(fn=update_distance, inputs=[origin, destination], outputs=distance) | |
| submit_btn = gr.Button("Predict Fuel Burn") | |
| result = gr.Textbox(label="Fuel Burn in Kg", interactive=False) | |
| submit_btn.click(predict_fuel_burn, inputs=[model_name, origin, destination, seats, distance], outputs=result) | |
| demo.launch() | |