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app.py

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+import gradio as gr
+import joblib
+import numpy as np
+import pandas as pd
+
+# Load the model and unique brand values
+model = joblib.load('model.joblib')
+unique_values = joblib.load('unique_values.joblib')
+edu = unique_values['Education_Level']
+occ = unique_values['Occupation']
+loc = unique_values['Location']
+emp = unique_values['Employment_Status']
+hom = unique_values['Homeownership_Status']
+typ = unique_values['Type_of_Housing']
+gen = unique_values['Gender']
+pri = unique_values['Primary_Mode_of_Transportation']
+
+
+
+# Define the prediction function
+def predict(edu, occ, loc, emp, hom, typ, gen, pri, age, num, wor,hou):
+    # Convert inputs to appropriate types
+    age = int(age)
+    num = int(num)
+    wor = float(wor)
+    hou - int(hou)
+    
+    # Prepare the input array for prediction
+    input_data = pd.DataFrame({
+        'Age': [age],
+        'Education_Level': [edu],
+        'Occupation': [occ],
+        'Number_of_Dependents': [num],
+        'Location': [loc],
+        'Work_Experience': [wor],
+        'Marital_Status': [mar],
+        'Employment_Status': [emp],
+        'Household_Size': [hou],
+        'Type_of_Housing': [typ],
+        'Gender': [gen],
+        'Primary_Mode_of_Transportation': [pri]
+    })
+    
+    # Perform the prediction
+    prediction = model.predict(input_data)
+    
+    return prediction[0]
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Dropdown(choices=list(edu), label='Education_Level'),
+        gr.Dropdown(choices=list(occ), label='Occupation'),
+        gr.Dropdown(choices=list(loc), label='Location'),
+        gr.Dropdown(choices=list(emp), label='Employment_Status'),
+        gr.Dropdown(choices=list(hom), label='Homeownership_Status'),
+        gr.Dropdown(choices=list(typ), label='Type_of_Housing'),
+        gr.Dropdown(choices=list(gen), label='Gender'),
+        gr.Dropdown(choices=list(pri), label='Primary_Mode_of_Transportation'),
+        gr.Textbox(label='Age'),
+        gr.Textbox(label='Number_of_Dependents'),
+        gr.Textbox(label='Work_Experience'),
+        gr.Textbox(label='Household_Size')
+
+    ],
+    outputs="text",
+    title="Household Income Predictor",
+    description="Enter your information to predict your household income."
+)
+
+# Launch the app
+interface.launch()

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0e862da8368f3ba6dcdf52ed748b31d27b2d1b0882e56fc26801206c514c7d17
+size 488685

requirements.txt

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+joblib
+pandas
+scikit-learn==1.3.2
+xgboost==2.1.1

unique_values.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:21057c1b75b14016577d9b531190b5f6b13eb7c514112b50b5da1b219d97d0b4
+size 2921