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''' |
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cd h2o-3.42.0.2 |
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java -jar h2o.jar |
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http://localhost:54321 |
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source h20env/bin/activate |
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jupyter notebook |
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''' |
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import h2o |
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import pandas as pd |
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import matplotlib as plt |
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import gradio as gr |
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import random |
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plt.use("Agg") |
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h2o.init() |
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gbm_saved_model = h2o.load_model('/mnt/c/Users/MI/Documents/Machine learning/retention_automl/GBM_2_AutoML_1_20230812_124802') |
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def predict(*args): |
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df = pd.DataFrame([list(args)], columns=['department','promoted','review','projects','salary','tenure','satisfaction','bonus','avg_hrs_month']) |
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x = h2o.H2OFrame(df) |
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pos_pred = gbm_saved_model.predict(x) |
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return (pos_pred.as_data_frame()) |
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unique_department = ['IT', |
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'admin', |
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'engineering', |
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'finance', |
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'logistics', |
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'marketing', |
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'operations', |
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'retail', |
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'sales', |
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'support'] |
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unique_salary = ['high', 'low', 'medium'] |
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with gr.Blocks() as demo: |
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gr.Markdown(""" |
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**Employee leave probability prediction using H2O AutoML demo app**. |
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Data set used - https://www.kaggle.com/datasets/marikastewart/employee-turnover . |
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Jupyter Notebook is available at *Files* tab |
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""") |
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with gr.Row(): |
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with gr.Column(): |
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department = gr.Dropdown( |
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label="Department", |
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choices=unique_department, |
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value=lambda: random.choice(unique_department), |
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) |
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promoted = gr.Number( |
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label="Promoted", |
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minimum=0.0, |
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maximum=1.0 |
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) |
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review = gr.Slider(label="Review", minimum=0, maximum=1, step=0.01, randomize=True) |
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projects = gr.Number( |
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label="Projects", |
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minimum=0.0, |
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maximum=30.0 |
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) |
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salary = gr.Dropdown( |
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label="salary", |
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choices=unique_salary, |
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value=lambda: random.choice(unique_salary), |
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) |
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tenure = gr.Number( |
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label="Tenure", |
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minimum=0.0, |
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maximum=50.0 |
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) |
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satisfaction = gr.Slider(label="Satisfaction", minimum=0, maximum=1, step=0.01, randomize=True) |
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bonus = gr.Number( |
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label="Bonus", |
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minimum=0.0, |
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maximum=1.0 |
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) |
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avg_hrs_month = gr.Number( |
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label="Avg_hrs_month", |
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minimum=0.0, |
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maximum=500.0 |
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) |
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with gr.Column(): |
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label = gr.Dataframe() |
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with gr.Row(): |
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predict_btn = gr.Button(value="Predict") |
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predict_btn.click( |
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predict, |
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inputs=[ |
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department, |
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promoted, |
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review, |
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projects, |
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salary, |
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tenure, |
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satisfaction, |
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bonus, |
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avg_hrs_month |
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], |
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outputs=[label], |
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) |
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demo.launch() |
