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| # Import the required Libraries | |
| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| import os, pickle | |
| import re | |
| def loaded_object(filepath= r'ml_items' ): | |
| with open(filepath,'rb') as file: | |
| loaded_object = pickle.load(file) | |
| return loaded_object | |
| ###### SETUP | |
| loaded_object = loaded_object() | |
| ###### instantiating loaded objects | |
| mls = loaded_object['model'] | |
| ml_scaler = loaded_object['scaler'] | |
| ####loading model | |
| inputs = ['Origin_lat','Origin_lon','Destination_lat','Destination_lon','Trip_distance','maximum_2m_air_temperature', | |
| 'mean_2m_air_temperature','minimum_2m_air_temperature','times_encoded','cluster_id'] | |
| #Defining the predict function | |
| def predict(*args,scaler = ml_scaler, model =mls): | |
| # Creating a dataframe of inputs | |
| input_data = pd.DataFrame([args], columns=inputs) | |
| print(input_data) | |
| input_data= scaler.transform(input_data) | |
| # Modeling | |
| #with gr.Row(): | |
| output_str = 'Hey there,Your ETA is' | |
| output_str = 'Hey there,Your ETA is' | |
| dist = 'seconds' | |
| model_output = abs(int(model.predict(input_data))) | |
| return f"{output_str} {model_output} {dist}" | |
| # Function to process inputs and return prediction | |
| # Creating a dataframe of inputs | |
| with gr.Blocks(theme=gr.themes.Monochrome()) as app: | |
| gr.Markdown("# YASSIR ETA PREDICTION") | |
| gr.Markdown("""This app uses a machine learning model to predict the ETA of trips on the Yassir Hailing App.Refer to the expander at the bottom for more information on the inputs.""") | |
| with gr.Row(): | |
| origin_lat= gr.Slider(2.807,3.381,step = 0.01,interactive=True, value=2.807, label = 'origin_lat') | |
| origin_lon = gr.Slider(36.589,36.82,step =0.01,interactive=True, value=36.589,label = 'origin_lon') | |
| Destination_lat =gr.Slider(2.807,3.381,step = 0.1,interactive=True, value=2.81,label ='Destination_lat') | |
| Destination_lon =gr.Slider(36.596,36.819,step = 0.1,interactive=True, value=36.596,label ='Destination_lon') | |
| Trip_distance = gr.Slider(1,62028,step =100,interactive=True, value= 200,label = 'Trip_distance') | |
| with gr.Column(): | |
| maximum_2m_air_temperature =gr.Slider(288.201, 294.411, step = 0.1,interactive=True, value=288.201,label ='maximum_2m_air_temperature') | |
| mean_2m_air_temperature =gr.Slider(285.203, 291.593,step = 0.1,interactive=True, value=285.203,label ='mean_2m_air_temperature') | |
| minimum_2m_air_temperature = gr.Slider( 282.348, 287.693,step = 0.1,interactive=True,value=282.348, label ='minimum_2m_air_temperature') | |
| times_encoded = gr.Dropdown([1,2,3],label="Time of the day",value= 3) | |
| cluster_id = gr.Dropdown([1,2,3,4,5,6,7],label="Cluster ID", value=4) | |
| with gr.Row(): | |
| btn = gr.Button("Predict").style(full_width=True) | |
| output = gr.Textbox(label="Prediction") | |
| # Expander for more info on columns | |
| with gr.Accordion("Information on inputs"): | |
| gr.Markdown("""These are information on the inputs the app takes for predicting a rides ETA. | |
| - origin_lat: Origin in degree latitude) | |
| - origin_lon: Origin in degree longitude | |
| - Destination_lat: Destination latitude | |
| - Destination_lon: Destination logitude | |
| - Trip Distance : Distance in meters on a driving route | |
| - Cluster ID : Select the cluster within which you started your trip | |
| - Time of the day: What time in the day did your trip start, 1- morning(or daytime),2 - evening 3- midnight | |
| """) | |
| btn.click(fn = predict,inputs= [origin_lat,origin_lon, Destination_lat, Destination_lat,Trip_distance, | |
| maximum_2m_air_temperature,mean_2m_air_temperature, minimum_2m_air_temperature,times_encoded, | |
| cluster_id], outputs = output) | |
| app.launch(debug =True) |