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| # Import the required Libraries | |
| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| import os, joblib | |
| import re | |
| current_directory = os.path.dirname(os.path.realpath(__file__)) | |
| pipeline_path = os.path.join(current_directory, 'toolkit', 'pipeline.joblib') | |
| # pipeline = joblib.load(r'toolkit\pipeline.joblib') | |
| pipeline = joblib.load(pipeline_path) | |
| inputs = ['Origin_lat', 'Origin_lon', 'Destination_lat', 'Destination_lon', | |
| 'Trip_distance', 'Speed', 'dewpoint_2m_temperature', | |
| 'mean_2m_air_temperature', 'pickup_weekday', 'pickup_weekofyear', | |
| 'pickup_hour', 'pickup_minute', 'pickup_week_hour', 'month', | |
| 'day_of_week', 'cluster_id', 'target_transformed', 'temperature_range', | |
| 'wind_speed', 'rain'] | |
| def predict(*args, pipeline=pipeline, inputs=inputs): | |
| # Check if inputs is provided | |
| if inputs is None: | |
| raise ValueError("Please provide the 'inputs' parameter.") | |
| # Creating a DataFrame of inputs | |
| input_data = pd.DataFrame([args], columns=inputs) | |
| print(input_data) | |
| # Modeling | |
| try: | |
| model_output = abs(int(pipeline.predict(input_data))) | |
| except Exception as e: | |
| print(f"Error during prediction: {str(e)}") | |
| model_output = 0 | |
| output_str = 'Hey there, Your ETA is' | |
| dist = 'seconds' | |
| return f"{output_str} {model_output} {dist}" | |
| with gr.Blocks(theme=gr.themes.Monochrome()) as app: | |
| gr.Markdown("# 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(2.806,3.381,step = 0.01,interactive=True, value=2.806, label = 'origin_lat') | |
| origin_lon = gr.Slider(36.589,36.820,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(36.592,36.819,step = 0.1,interactive=True, value=36.596,label ='Destination_lon') | |
| Trip_distance = gr.Slider(61,958,step =10,interactive=True, value=20,label = 'Trip_distance') | |
| Speed = gr.Slider(0.300,85.000, step=0.01, interactive=True, value=20, label = 'Speed') | |
| with gr.Column(): | |
| dewpoint_2m_temperature =gr.Slider(280.000, 288.000, step = 0.01,interactive=True, value=282.201,label ='dewpoint_2m_temperature') | |
| mean_2m_air_temperature =gr.Slider(285.203, 291.110,step = 0.1,interactive=True, value=287.203,label ='mean_2m_air_temperature') | |
| pickup_weekday = gr.Dropdown([0,1,2,3,4,5,6],label ='pickup_weekday', value=3) | |
| pickup_weekofyear = gr.Dropdown([47,48,49,50],label="pickup_weekofyear",value=3) | |
| pickup_hour = gr.Dropdown([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23] | |
| ,label="pickup_hour",value=13) | |
| pickup_minute = gr.Slider(0, 59, step = 1,interactive=True, value=5,label ='pickup_minute') | |
| pickup_week_hour = gr.Slider(0, 167, step = 1,interactive=True, value=5,label ='pickup_week_hour') | |
| month = gr.Dropdown([11,12],label = 'month') | |
| day_of_week = gr.Dropdown([0,1,2,3,4,5,6],label ='day_of_week', value=3) | |
| cluster_id = gr.Dropdown([1,2,3,4,5,6,7],label="Cluster ID", value=4) | |
| with gr.Column(): | |
| target_transformed = gr.Slider(0, 336.426, step = 0.01,interactive=True, value=100,label ='target_transformed') | |
| temperature_range = gr.Slider(2.317, 9.166, step = 0.01,interactive=True, value=5,label='temperature_range') | |
| wind_speed = gr.Slider(0.803, 9.887, step = 0.01,interactive=True, value=5,label='wind_speed') | |
| rain = gr.Dropdown([0,1],label='rain') | |
| with gr.Row(): | |
| btn = gr.Button("Predict") | |
| 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_lon, | |
| Trip_distance, Speed, dewpoint_2m_temperature, | |
| mean_2m_air_temperature, pickup_weekday, pickup_weekofyear, | |
| pickup_hour, pickup_minute, pickup_week_hour, month, | |
| day_of_week, cluster_id, target_transformed, temperature_range, | |
| wind_speed, rain], outputs = output) | |
| app.launch(share = True, debug =True) |