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iimport pickle
import gradio as gr
# Define the function to read the pickled model
def read_pickle(path, saved_model_name):
with open(path + saved_model_name + '.pickle', 'rb') as to_read:
model = pickle.load(to_read)
return model
# Load the pickled model
path = './' # Assuming the model file is in the current directory
model = read_pickle(path, "Automatidata_gui")
# 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, 2]"),
gr.Number(0, 6, label="Passenger Count"),
gr.Number(label="Distance"),
gr.Number(label="Duration"),
gr.Number(0, 1, label="Rush Hour")
],
outputs="text", title="Taxi Fares Estimator",
description="Predicting Taxi Fare Amount Using Machine Learning."
)
# Launch the interface
if __name__ == "__main__":
automatidata_ga.launch(share=True)
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