app.py

import pandas as pd
import numpy as np
from sklearn.ensemble import VotingRegressor
from sklearn.base import BaseEstimator, RegressorMixin
import gradio as gr
import joblib

class FastAIWrapper(BaseEstimator, RegressorMixin):
    def __init__(self, learn):
        self.learn = learn
    
    def fit(self, X, y):
        return self
    
    def predict(self, X):
        dl = self.learn.dls.test_dl(X)
        preds, _ = self.learn.get_preds(dl=dl)
        return preds.numpy().flatten()

# Load your data and trained model
df = pd.read_csv('City_Employee_Payroll__Current__20240915.csv', low_memory=False)
ensemble = joblib.load('ensemble_model.joblib')

def predict_total_pay(gender, job_title, ethnicity):
    # Create a sample input DataFrame
    sample = pd.DataFrame({
        'GENDER': [gender],
        'JOB_TITLE': [job_title],
        'ETHNICITY': [ethnicity],
    })
    
    # Fill in other required features (you may need to adjust this based on your model's requirements)
    sample['EMPLOYMENT_TYPE'] = df['EMPLOYMENT_TYPE'].mode().iloc[0]
    sample['JOB_STATUS'] = df['JOB_STATUS'].mode().iloc[0]
    sample['MOU'] = df['MOU'].mode().iloc[0]
    sample['DEPARTMENT_NO'] = df['DEPARTMENT_NO'].mode().iloc[0]
    sample['PAY_YEAR'] = df['PAY_YEAR'].max()
    sample['REGULAR_PAY'] = df['REGULAR_PAY'].mean()
    sample['OVERTIME_PAY'] = df['OVERTIME_PAY'].mean()
    sample['ALL_OTHER_PAY'] = df['ALL_OTHER_PAY'].mean()
    
    # Calculate derived features
    sample['PAY_RATIO'] = sample['REGULAR_PAY'] / (sample['OVERTIME_PAY'] + sample['ALL_OTHER_PAY'] + 1)
    sample['TOTAL_NON_REGULAR_PAY'] = sample['OVERTIME_PAY'] + sample['ALL_OTHER_PAY']
    
    # Make prediction
    prediction = ensemble.predict(sample)[0]
    return prediction

def predict_total_pay(gender, job_title, ethnicity):
    # Function to predict total pay based on input parameters
    # Parameters:
    #   gender: str - The gender of the employee
    #   job_title: str - The job title of the employee
    #   ethnicity: str - The ethnicity of the employee
    
    # Create a sample input DataFrame with the given parameters
    # This will be used as input for the prediction model
    sample = pd.DataFrame({
        'GENDER': [gender],
        'JOB_TITLE': [job_title],
        'ETHNICITY': [ethnicity],
    })

    # Filter the main DataFrame (df) to find exact matches for the input combination
    # This creates a subset of data that matches all three input parameters
    group = df[(df['GENDER'] == gender) & (df['JOB_TITLE'] == job_title) & (df['ETHNICITY'] == ethnicity)]
    
    if len(group) > 0:
        # If exact matches are found, use their statistics to populate the sample
        # For categorical variables, use the mode (most frequent value)
        sample['EMPLOYMENT_TYPE'] = [group['EMPLOYMENT_TYPE'].mode().iloc[0]]
        sample['JOB_STATUS'] = [group['JOB_STATUS'].mode().iloc[0]]
        sample['MOU'] = [group['MOU'].mode().iloc[0]]
        sample['DEPARTMENT_NO'] = [group['DEPARTMENT_NO'].mode().iloc[0]]
        # For numerical variables, use the mean
        sample['REGULAR_PAY'] = [group['REGULAR_PAY'].mean()]
        sample['OVERTIME_PAY'] = [group['OVERTIME_PAY'].mean()]
        sample['ALL_OTHER_PAY'] = [group['ALL_OTHER_PAY'].mean()]
    else:
        # If no exact match is found, try to find a broader match based on job_title
        job_group = df[df['JOB_TITLE'] == job_title]
        if len(job_group) > 0:
            # If job title matches are found, use their statistics
            sample['EMPLOYMENT_TYPE'] = [job_group['EMPLOYMENT_TYPE'].mode().iloc[0]]
            sample['JOB_STATUS'] = [job_group['JOB_STATUS'].mode().iloc[0]]
            sample['MOU'] = [job_group['MOU'].mode().iloc[0]]
            sample['DEPARTMENT_NO'] = [job_group['DEPARTMENT_NO'].mode().iloc[0]]
            sample['REGULAR_PAY'] = [job_group['REGULAR_PAY'].mean()]
            sample['OVERTIME_PAY'] = [job_group['OVERTIME_PAY'].mean()]
            sample['ALL_OTHER_PAY'] = [job_group['ALL_OTHER_PAY'].mean()]
        else:
            # If no job title match is found, use overall statistics from the entire dataset
            sample['EMPLOYMENT_TYPE'] = [df['EMPLOYMENT_TYPE'].mode().iloc[0]]
            sample['JOB_STATUS'] = [df['JOB_STATUS'].mode().iloc[0]]
            sample['MOU'] = [df['MOU'].mode().iloc[0]]
            sample['DEPARTMENT_NO'] = [df['DEPARTMENT_NO'].mode().iloc[0]]
            sample['REGULAR_PAY'] = [df['REGULAR_PAY'].mean()]
            sample['OVERTIME_PAY'] = [df['OVERTIME_PAY'].mean()]
            sample['ALL_OTHER_PAY'] = [df['ALL_OTHER_PAY'].mean()]

    # Set PAY_YEAR to the most recent year in the dataset
    sample['PAY_YEAR'] = [df['PAY_YEAR'].max()]
    
    # Calculate derived features
    # PAY_RATIO: Ratio of regular pay to other types of pay
    sample['PAY_RATIO'] = sample['REGULAR_PAY'] / (sample['OVERTIME_PAY'] + sample['ALL_OTHER_PAY'] + 1)
    # TOTAL_NON_REGULAR_PAY: Sum of overtime pay and all other pay
    sample['TOTAL_NON_REGULAR_PAY'] = sample['OVERTIME_PAY'] + sample['ALL_OTHER_PAY']

    # Ensure all categorical columns are of type 'object' to prevent type issues with the model
    categorical_columns = ['GENDER', 'JOB_TITLE', 'ETHNICITY', 'EMPLOYMENT_TYPE', 'JOB_STATUS', 'MOU', 'DEPARTMENT_NO']
    for col in categorical_columns:
        sample[col] = sample[col].astype('object')

    # Use the ensemble model to make a prediction
    # The model takes the sample DataFrame as input and returns a predicted total pay
    prediction = ensemble.predict(sample)[0]
    
    # Return the predicted total pay
    return prediction

# Prepare dropdown options
genders = df['GENDER'].dropna().unique().tolist()
ethnicities = df['ETHNICITY'].dropna().unique().tolist()
job_titles = sorted(df['JOB_TITLE'].dropna().unique().tolist())

# Create Gradio interface
iface = gr.Interface(
    fn=predict_total_pay,
    inputs=[
        gr.Dropdown(choices=genders, label="Gender"),
        gr.Dropdown(choices=ethnicities, label="Ethnicity"),
        gr.Dropdown(choices=job_titles, label="Job Title")
    ],
    outputs=gr.Textbox(label="Predicted Total Pay"),
    title="LA City Employee Pay Predictor",
    description="Predict the total pay for LA City employees based on gender, ethnicity, and job title."
)

iface.launch()