Create app.py

app.py

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+import pickle
+import pandas as pd
+import shap
+from shap.plots._force_matplotlib import draw_additive_plot
+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+
+# load the model from disk
+loaded_model = pickle.load(open("h22_xgb.pkl", 'rb'))
+
+# Setup SHAP
+explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
+
+# Create the main function for server
+def main_func(ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,WorkLifeBalance):
+    new_row = pd.DataFrame.from_dict({'ValueDiversity':ValueDiversity,'AdequateResources':AdequateResources,
+              'Voice':Voice,'GrowthAdvancement':GrowthAdvancement,'Workload':Workload,
+              'WorkLifeBalance':WorkLifeBalance}, orient = 'index').transpose()
+    
+    prob = loaded_model.predict_proba(new_row)
+    
+    shap_values = explainer(new_row)
+    # plot = shap.force_plot(shap_values[0], matplotlib=True, figsize=(30,30), show=False)
+    # plot = shap.plots.waterfall(shap_values[0], max_display=6, show=False)
+    plot = shap.plots.bar(shap_values[0], max_display=6, order=shap.Explanation.abs, show_data='auto', show=False)
+
+    plt.tight_layout()
+    local_plot = plt.gcf()
+    plt.rcParams['figure.figsize'] = 6,4
+    plt.close()
+    
+    return {"Leave": float(prob[0][0]), "Stay": 1-float(prob[0][0])}, local_plot
+
+# Create the UI
+title = "**Employee Turnover Predictor & Interpreter** 🪐"
+description1 = """
+This app takes six inputs about employees' satisfaction with different aspects of their work (such as work-life balance, ...) and predicts whether the employee intends to stay with the employer or leave. There are two outputs from the app: 1- the predicted probability of stay or leave, 2- Shapley's force-plot which visualizes the extent to which each factor impacts the stay/ leave prediction.   
+"""
+
+description2 = """
+To use the app, click on one of the examples, or adjust the values of the six employee satisfaction factors, and click on Analyze. ✨ 
+""" 
+
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"## {title}")
+    # gr.Markdown("""![marketing](types-of-employee-turnover.jpg)""")
+    gr.Markdown(description1)
+    gr.Markdown("""---""")
+    gr.Markdown(description2)
+    gr.Markdown("""---""")
+    with gr.Row():        
+        with gr.Column():
+            ValueDiversity = gr.Slider(label="ValueDiversity Score", minimum=1, maximum=5, value=4, step=.1)
+            AdequateResources = gr.Slider(label="AdequateResources Score", minimum=1, maximum=5, value=4, step=.1)
+            Voice = gr.Slider(label="Voice Score", minimum=1, maximum=5, value=4, step=.1)
+            GrowthAdvancement = gr.Slider(label="GrowthAdvancement Score", minimum=1, maximum=5, value=4, step=.1)
+            Workload = gr.Slider(label="Workload Score", minimum=1, maximum=5, value=4, step=.1)
+            WorkLifeBalance = gr.Slider(label="WorkLifeBalance Score", minimum=1, maximum=5, value=4, step=.1)
+            submit_btn = gr.Button("Analyze")
+        with gr.Column(visible=True,scale=1, min_width=600) as output_col:
+            label = gr.Label(label = "Predicted Label")
+            local_plot = gr.Plot(label = 'Shap:')
+        
+            submit_btn.click(
+                main_func,
+                [ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,WorkLifeBalance],
+                [label,local_plot], api_name="Employee_Turnover"
+            )
+    
+    gr.Markdown("### Click on any of the examples below to see how it works:")
+    gr.Examples([[4,4,4,4,5,5], [5,4,5,4,4,4]], 
+                [ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,WorkLifeBalance], 
+                [label,local_plot], main_func, cache_examples=True)
+
+demo.launch()