rasmodev commited on
Commit
1cfdd33
1 Parent(s): 43bb729

Update app.py

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Files changed (1) hide show
  1. app.py +23 -17
app.py CHANGED
@@ -63,23 +63,29 @@ def main():
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  years_since_last_promotion = str(years_since_last_promotion)
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  years_with_curr_manager = str(years_with_curr_manager)
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- # Create a DataFrame to hold the user input data
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- input_data = pd.DataFrame({
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- 'Age': [age],
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- 'Department': [department],
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- 'EnvironmentSatisfaction': [environment_satisfaction],
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- 'JobRole': [job_role],
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- 'JobSatisfaction': [job_satisfaction],
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- 'MonthlyIncome': [monthly_income],
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- 'NumCompaniesWorked': [num_companies_worked],
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- 'OverTime': [over_time],
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- 'PercentSalaryHike': [percent_salary_hike],
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- 'RelationshipSatisfaction': [relationship_satisfaction],
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- 'TrainingTimesLastYear': [training_times_last_year],
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- 'WorkLifeBalance': [work_life_balance],
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- 'YearsSinceLastPromotion': [years_since_last_promotion],
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- 'YearsWithCurrManager': [years_with_curr_manager]
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- })
 
 
 
 
 
 
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  # Make predictions
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  prediction = model.predict(input_data)
 
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  years_since_last_promotion = str(years_since_last_promotion)
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  years_with_curr_manager = str(years_with_curr_manager)
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+ # Create a DataFrame to hold the user input data
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+ input_data = pd.DataFrame({
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+ 'Age': [age],
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+ 'Department': [department],
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+ 'EnvironmentSatisfaction': [environment_satisfaction],
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+ 'JobRole': [job_role],
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+ 'JobSatisfaction': [job_satisfaction],
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+ 'MonthlyIncome': [monthly_income],
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+ 'NumCompaniesWorked': [num_companies_worked],
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+ 'OverTime': [over_time],
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+ 'PercentSalaryHike': [percent_salary_hike],
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+ 'RelationshipSatisfaction': [relationship_satisfaction],
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+ 'TrainingTimesLastYear': [training_times_last_year],
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+ 'WorkLifeBalance': [work_life_balance],
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+ 'YearsSinceLastPromotion': [years_since_last_promotion],
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+ 'YearsWithCurrManager': [years_with_curr_manager]
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+ })
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+
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+ # Reorder columns to match the expected order
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+ input_data = input_data[['Age', 'Department', 'EnvironmentSatisfaction', 'JobRole', 'JobSatisfaction',
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+ 'MonthlyIncome', 'NumCompaniesWorked', 'OverTime', 'PercentSalaryHike',
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+ 'RelationshipSatisfaction', 'TrainingTimesLastYear', 'WorkLifeBalance',
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+ 'YearsSinceLastPromotion', 'YearsWithCurrManager']]
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  # Make predictions
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  prediction = model.predict(input_data)