Delete app.py

app.py

@@ -1,127 +0,0 @@
-import pandas as pd
-import numpy as np
-from sklearn.preprocessing import StandardScaler, OneHotEncoder
-from sklearn.model_selection import train_test_split
-from fastai.tabular.all import *
-from sklearn.ensemble import VotingRegressor
-from sklearn.linear_model import LinearRegression
-from sklearn.tree import DecisionTreeRegressor
-from sklearn.base import BaseEstimator, RegressorMixin
-from sklearn.compose import ColumnTransformer
-from sklearn.pipeline import Pipeline
-import gradio as gr
-
-df = pd.read_csv('City_Employee_Payroll__Current__20240915.csv', low_memory=False)
-df = df.replace([np.inf, -np.inf], np.nan)
-
-cat_names = ['EMPLOYMENT_TYPE', 'JOB_STATUS', 'MOU', 'GENDER', 'ETHNICITY', 'JOB_TITLE', 'DEPARTMENT_NO']
-cont_names = ['PAY_YEAR', 'REGULAR_PAY', 'OVERTIME_PAY', 'ALL_OTHER_PAY']
-
-df['PAY_RATIO'] = df['REGULAR_PAY'] / (df['OVERTIME_PAY'] + df['ALL_OTHER_PAY'] + 1)
-df['TOTAL_NON_REGULAR_PAY'] = df['OVERTIME_PAY'] + df['ALL_OTHER_PAY']
-cont_names.extend(['PAY_RATIO', 'TOTAL_NON_REGULAR_PAY'])
-
-X = df[cat_names + cont_names].copy()
-y = df['TOTAL_PAY'].copy()
-for col in cat_names:
-    X[col] = X[col].fillna('Unknown')
-
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-X_train_sample, _, y_train_sample, _ = train_test_split(X_train, y_train, train_size=0.3, random_state=42)
-
-to = TabularPandas(df, procs=[Categorify, FillMissing, Normalize], cat_names=cat_names, cont_names=cont_names, y_names='TOTAL_PAY', splits=RandomSplitter(valid_pct=0.2)(range_of(df)))
-dls = to.dataloaders(bs=64)
-
-learn = tabular_learner(dls, layers=[200, 100, 50], metrics=rmse)
-learn.fit_one_cycle(9)
-
-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()
-
-preprocessor = ColumnTransformer(
-    transformers=[
-        ('num', StandardScaler(), cont_names),
-        ('cat', OneHotEncoder(drop='first', sparse=False, handle_unknown='ignore'), cat_names)
-    ])
-
-model1 = FastAIWrapper(learn)
-model2 = Pipeline([('preprocessor', preprocessor), ('regressor', LinearRegression())])
-model3 = Pipeline([('preprocessor', preprocessor), ('regressor', DecisionTreeRegressor())])
-
-ensemble = VotingRegressor(
-    estimators=[('fastai', model1), ('lr', model2), ('dt', model3)],
-    weights=[2, 1, 1]
-)
-
-ensemble.fit(X_train_sample, y_train_sample)
-
-def predict_total_pay(gender, job_title, ethnicity):
-    sample = pd.DataFrame({
-        'GENDER': [gender],
-        'JOB_TITLE': [job_title],
-        'ETHNICITY': [ethnicity],
-    })
-    group = df[(df['GENDER'] == gender) & (df['JOB_TITLE'] == job_title) & (df['ETHNICITY'] == ethnicity)]
-    if len(group) > 0:
-        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]]
-        sample['REGULAR_PAY'] = [group['REGULAR_PAY'].mean()]
-        sample['OVERTIME_PAY'] = [group['OVERTIME_PAY'].mean()]
-        sample['ALL_OTHER_PAY'] = [group['ALL_OTHER_PAY'].mean()]
-    else:
-        job_group = df[df['JOB_TITLE'] == job_title]
-        if len(job_group) > 0:
-            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:
-            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()]
-    sample['PAY_YEAR'] = [df['PAY_YEAR'].max()]
-    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']
-    categorical_columns = ['GENDER', 'JOB_TITLE', 'ETHNICITY', 'EMPLOYMENT_TYPE', 'JOB_STATUS', 'MOU', 'DEPARTMENT_NO']
-    for col in categorical_columns:
-        sample[col] = sample[col].astype('object')
-    prediction = ensemble.predict(sample)[0]
-    return prediction
-
-def gradio_predict(gender, ethnicity, job_title):
-    predicted_pay = predict_total_pay(gender, job_title, ethnicity)
-    return f"${predicted_pay:.2f}"
-
-genders = df['GENDER'].dropna().unique().tolist()
-ethnicities = df['ETHNICITY'].dropna().unique().tolist()
-job_titles = sorted(df['JOB_TITLE'].dropna().unique().tolist())
-
-iface = gr.Interface(
-    fn=gradio_predict,
-    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()