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Update app.py
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app.py
CHANGED
@@ -2,16 +2,20 @@ import gradio as gr
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import pandas as pd
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import joblib
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import numpy as np
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# Load the pre-trained model
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# Load the unique aircraft data
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aircraft_data = pd.read_csv('datasets/aircraft_data.csv').drop_duplicates(subset='model')
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aircraft_dict = aircraft_data.set_index('model').to_dict(orient='index')
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# Load the airport distances data
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airport_data = pd.read_csv('datasets/airport_distances.csv')
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airport_dict = airport_data.set_index(['Origin_Airport', 'Destination_Airport']).to_dict(orient='index')
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@@ -41,8 +45,10 @@ def predict_fuel_burn(model_name, origin, destination, seats, distance):
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df = pd.DataFrame(data)
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# Make the prediction
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fuel_burn_prediction_xgboost = xgboost_model.predict(df)
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def update_fields(model_name):
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@@ -101,3 +107,5 @@ with gr.Blocks() as demo:
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submit_btn.click(predict_fuel_burn, inputs=[model_name, origin, destination, seats, distance], outputs=result)
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demo.launch()
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import pandas as pd
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import joblib
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import numpy as np
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from models.neural_network.inference import load_model_and_preprocessor
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# Load the pre-trained model
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nn_model, nn_preprocessor = load_model_and_preprocessor('/Users/ashishpoudel/Downloads/AircraftFuelPrediction-main/saved_models/nn_model.keras',
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'/Users/ashishpoudel/Downloads/AircraftFuelPrediction-main/saved_models/nn_preprocessor.pkl')
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xgboost_model = joblib.load('/Users/ashishpoudel/Downloads/AircraftFuelPrediction-main/saved_models/xgboost_model.joblib')
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# Load the unique aircraft data
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aircraft_data = pd.read_csv('/Users/ashishpoudel/Downloads/AircraftFuelPrediction-main/datasets/aircraft_data.csv').drop_duplicates(subset='model')
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aircraft_dict = aircraft_data.set_index('model').to_dict(orient='index')
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# Load the airport distances data
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airport_data = pd.read_csv('/Users/ashishpoudel/Downloads/AircraftFuelPrediction-main/datasets/airport_distances.csv')
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airport_dict = airport_data.set_index(['Origin_Airport', 'Destination_Airport']).to_dict(orient='index')
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df = pd.DataFrame(data)
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# Make the prediction
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fuel_burn_prediction_nn = nn_model.predict(nn_preprocessor.transform(df))[0]
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fuel_burn_prediction_xgboost = xgboost_model.predict(df)
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return f"Neural Network: {fuel_burn_prediction_nn[0]:.2f} kg, XGBoost: {fuel_burn_prediction_xgboost[0]:.2f} kg"
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def update_fields(model_name):
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submit_btn.click(predict_fuel_burn, inputs=[model_name, origin, destination, seats, distance], outputs=result)
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demo.launch()
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