| 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) | |