delete app,py

app.py

@@ -1,71 +0,0 @@
-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("heart_xgb.pkl"))
-
-
-# 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.close()
-    
-    return {"Leave": float(prob[0][0]), "Stay": 1-float(prob[0][0])}, local_plot
-
-# Create the UI
-title = "Heart Attack Predictor & Interpreter ❤️"
-description1 = "This app should not be used for medical purposes"
-
-
-description2 = "Use the app to predict risk for heart attacks 🤞"
-
-with gr.Blocks(title=title) as demo:
-    gr.Markdown(f"## {title}")
-#    gr.Markdown("""![marketing](file/marketing.jpg)""")
-    gr.Markdown(description1)
-    gr.Markdown("""---""")
-    gr.Markdown(description2)
-    gr.Markdown("""---""")
-    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) 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()