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import gradio as gr
import pickle
def read_pickle(path, saved_model_name:str):
with open(path + saved_model_name + '.pickle', 'rb') as to_read:
model = pickle.load(to_read)
return model
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)} $"
path = 'F:/Case study/Interview preparation/01.Project/1. Automatidata/Final/'
model = read_pickle(path,'Automatidata_gui')
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 Estimater",
description="Predicting Taxi Fare Amount Using Machine Learning.",
)
if __name__ == "__main__":
automatidata_ga.launch(share=True)
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