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cap.py

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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'Gradio_App_toolkit' ):
+  with open(filepath,'rb') as file:
+    loaded_object = pickle.load(file)
+  return loaded_object
+###### SETUP
+
+###### instantiating loaded objects
+loaded_object = loaded_object()
+ml_model = loaded_object['model']
+ml_scaler = loaded_object['scaler']
+print(ml_model)
+print(ml_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 =ml_model):
+    # 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' 
+    dist = 'meters'
+    model_output = model.predict(input_data)
+    return(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(share = True, debug =True,server_port= 6006)

requirements.txt

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+pandas==1.4.4
+numpy==1.21.5
+seaborn==0.11.2
+scikit-learn==1.1.2