Create app.py

app.py

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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()
+