app.py

import pickle
import gradio as gr

# Load the pickled model
with open('./Automatidata_gui.pickle', 'rb') as file:
    model = pickle.load(file)

# Define the function for making predictions
def automatidata(VendorID, passenger_count, Distance, Duration, rush_hour):
    inputs = [[VendorID, passenger_count, Distance, Duration, rush_hour]]
    prediction = model.predict(inputs) 
    prediction_value = prediction[0][0]
    return f"Fare amount(approx.) = {round(prediction_value, 2)} $"

# Create the Gradio interface
automatidata_ga = gr.Interface(fn=automatidata, 
                               inputs=[
                                   gr.Number(1, 2, label="VendorID - [1 or 2]"),
                                   gr.Number(0, 6, label="Passenger Count - [1 to 6]"),
                                   gr.Number(label="Distance in miles"),
                                   gr.Number(label="Duration in mins"),
                                   gr.Number(0, 1, label="Rush Hour - [0 or 1]")
                               ],
                               outputs="text", title="New York City Taxi and Limousine Commission (TLC) - Taxi Fares Estimator",
                               examples = [
                                            [2,1,2.33,15.09,0],
                                            [1,2,4.22,24.29,0],
                                            [1,1,0.71,6.66,0],
                                            [2,1,0.97,8.37,0],
                                            [2,3,1.48,8.92,0],
                                    ],                                                                                 
                               description="Predicting Taxi Fare Amount Using Machine Learning.",
                               theme='dark'
                               )

automatidata_ga.launch(share=True)