Upload app.py

app.py

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