ML model

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+import os, joblib
+import re
+
+# load model pipeline
+file_path = os.path.abspath('toolkit/pipeline.joblib')
+pipeline = joblib.load(file_path)
+
+
+#function to calculate week hour from weekday and hour
+def calculate_pickup_week_hour(pickup_hour, pickup_weekday):
+    return pickup_weekday * 24 + pickup_hour
+
+def predict(origin_lat, origin_lon, Destination_lat, Destination_lon,
+            Trip_distance, dewpoint_2m_temperature,
+            minimum_2m_air_temperature, pickup_weekday, pickup_hour,
+            cluster_id, temperature_range, rain):
+    
+    # Calculate pickup_week_hour
+    pickup_week_hour = calculate_pickup_week_hour(pickup_hour, pickup_weekday)
+
+    # Modeling
+    try:
+        model_output = abs(int(pipeline.predict(pd.DataFrame([[origin_lat, origin_lon, Destination_lat, Destination_lon,
+                                                               Trip_distance, dewpoint_2m_temperature,
+                                                               minimum_2m_air_temperature, pickup_weekday, pickup_hour,
+                                                               pickup_week_hour, cluster_id, temperature_range,
+                                                               rain]], columns=['Origin_lat', 'Origin_lon', 'Destination_lat',
+                                                                                'Destination_lon', 'Trip_distance',
+                                                                                'dewpoint_2m_temperature',
+                                                                                'minimum_2m_air_temperature',
+                                                                                'pickup_weekday', 'pickup_hour',
+                                                                                'pickup_week_hour', 'cluster_id',
+                                                                                'temperature_range', 'rain']))))
+    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}"
+
+# UI layout
+with gr.Blocks(theme=gr.themes.Soft()) 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, 3.373, step=0.001, interactive=True, value=2.806, label='Origin latitude')
+        origin_lon = gr.Slider(36.589, 36.820, step=0.001, interactive=True, value=36.589, label='Origin longitude')
+        Destination_lat = gr.Slider(2.807, 3.381, step=0.001, interactive=True, value=2.810, label='Destination latitude')
+        Destination_lon = gr.Slider(36.592, 36.819, step=0.001, interactive=True, value=36.596, label='Destination longitude')
+        Trip_distance = gr.Slider(0, 62028, step=1, interactive=True, value=1000, label='Trip distance (M)')
+        cluster_id = gr.Dropdown([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], label="Cluster ID", value=4)
+
+    with gr.Column():
+        pickup_weekday = gr.Dropdown([0, 1, 2, 3, 4, 5, 6], value=3, label='Pickup weekday')
+        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],
+                                   value=13, label='Pickup hour')
+
+    with gr.Column():
+        dewpoint_2m_temperature = gr.Slider(279.129, 286.327, step=0.001, interactive=True, value=282.201,
+                                             label='dewpoint_2m_temperature')
+        minimum_2m_air_temperature = gr.Slider(282.037, 292.543, step=0.01, interactive=True, value=285.203,
+                                               label='minimum_2m_air_temperature')
+        temperature_range = gr.Slider(1.663, 10.022, step=0.01, interactive=True, value=5, label='temperature_range')
+        rain = gr.Dropdown([0, 1], label='Is it raining (0=No, 1=Yes)')
+
+    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 latitude: Origin in degree latitude)
+                    - Origin longitude:  Origin in degree longitude
+                    - Destination latitude: Destination latitude
+                    - Destination longitude: Destination logitude
+                    - Trip distance (M): Distance in meters on a driving route
+                    - Cluster ID: Select the cluster within which you started your trip
+                    - Pickup weekday: Day of the week
+                        Monday=0, Tuesday=1, Wednesday=2, Thursday=3, Friday=4, Saturday=5, Sunday=6
+                    - Pickup hour: The hour of the day (24hr clock)
+                    - dewpoint_2m_temperature: The temperature at 2 meters above the ground where the air temperature would be 
+                      low enough for dew to form. It gives an indication of humidity.
+                    - minimum_2m_air_temperature: The lowest air temperature recorded at 2 meters above the ground during the specified date.
+                    - temperature_range: The air temperature range recorded at 2 meters above the ground on the day
+                    - rain: Is it raining? yes=1, no=2
+                    """)
+
+    btn.click(fn=predict, inputs=[origin_lat, origin_lon, Destination_lat, Destination_lon,
+                                  Trip_distance, dewpoint_2m_temperature,
+                                  minimum_2m_air_temperature, pickup_weekday, pickup_hour,
+                                  cluster_id, temperature_range,
+                                  rain], outputs=output)
+
+    app.launch(share=True, debug=True)