Update app.py

app.py

@@ -5,43 +5,35 @@ 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", 'rb'))
-
 # Setup SHAP
 explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
-
 # Create the main function for server
-def main_func(age, sex,cp, trtbps, chol, fbs, restecg, thalachh, exng, oldpeak, slp,caa,thall):
+def main_func(age, sex, cp, trtbps, chol, fbs, restecg, thalachh, exng, oldpeak, slp, caa, thall):
     new_row = pd.DataFrame.from_dict({'age':age,'sex':sex,
-              'cp':cp,'trtbps':trtbps,'chol':chol,
-              'fbs':fbs,'restecg': restecg, 'thalachh':thalachh, 'exng':exng, 'oldpeak':oldpeak, 'slp':slp, 'caa':caa,'thall':thall}, 
-                                     orient = 'index').transpose()
-    
+              'cp':cp,'trtbps':trtbps,'chol':chol, 'fbs':fbs, 'restecg':restecg,
+              'thalachh':thalachh, 'exng':exng, 'oldpeak':oldpeak, 'slp':slp, 'caa':caa, 'thall':thall}, 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 {"low chance": float(prob[0][0]), "high chance": 1-float(prob[0][0])}, local_plot
-
+   
+    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 takes info from subjects and predicts their heart attack likelihood. Do not use for medical purposes .✨   
+This app takes info from subjects and predicts their heart attack likelihood. Do not use for medical diagnosis.  
 """
-
 description2 = """
- 🤞
+To use the app, click on one of the examples, or adjust the values of the factors, and click on Analyze. 🤞
 """ 
-
 with gr.Blocks(title=title) as demo:
     gr.Markdown(f"## {title}")
 #    gr.Markdown("""![marketing](file/marketing.jpg)""")
@@ -49,35 +41,29 @@ with gr.Blocks(title=title) as demo:
     gr.Markdown("""---""")
     gr.Markdown(description2)
     gr.Markdown("""---""")
-    age = gr.Slider(label="age score", minimum=15, maximum=90, value=40, step=1)
+    age = gr.Slider(label="age score", minimum=15, maximum=90, value=40, step=5)
     sex = gr.Slider(label="sex score", minimum=0, maximum=1, value=1, step=1)
     cp = gr.Slider(label="cp score", minimum=1, maximum=5, value=4, step=1)
-    trtbps = gr.Slider(label="trtbps score", minimum=1, maximum=5, value=4, step=1)
-    chol = gr.Slider(label="chol score", minimum=1, maximum=5, value=4, step=1)
-    fbs = gr.Slider(label="fbs score", minimum=1, maximum=5, value=4, step=1)
-    restecg = gr.Slider(label="restecg score", minimum=1, maximum=5, value=4, step=1)
-    thalachh = gr.Slider(label="thalachh score", minimum=1, maximum=5, value=4, step=1)
-    exng = gr.Slider(label="exng score", minimum=1, maximum=5, value=4, step=1)
-    oldpeak = gr.Slider(label="oldpeak score", minimum=1, maximum=5, value=4, step=1)
-    slp = gr.Slider(label="slp score", minimum=1, maximum=5, value=4, step=1)
-    caa = gr.Slider(label="caa score", minimum=1, maximum=5, value=4, step=1)
-    thall = gr.Slider(label="thall score", minimum=1, maximum=5, value=4, step=1)
-
-
-    
+    trtbps = gr.Slider(label="trtbps Score", minimum=1, maximum=5, value=4, step=1)
+    chol = gr.Slider(label="chol Score", minimum=1, maximum=5, value=4, step=1)
+    fbs = gr.Slider(label="fbs Score", minimum=1, maximum=5, value=4, step=1)
+    restecg = gr.Slider(label="restecg Score", minimum=1, maximum=5, value=4, step=1)
+    thalachh = gr.Slider(label="thalachh Score", minimum=1, maximum=5, value=4, step=1)
+    exng = gr.Slider(label="exng Score", minimum=1, maximum=5, value=4, step=1)
+    oldpeak = gr.Slider(label="oldpeak Score", minimum=1, maximum=5, value=4, step=1)
+    slp = gr.Slider(label="slp Score", minimum=1, maximum=5, value=4, step=1)
+    caa = gr.Slider(label="caa Score", minimum=1, maximum=5, value=4, step=1)
+    thall = gr.Slider(label="thall 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,
-        [age, sex,cp, trtbps, chol, fbs, restecg, thalachh, exng, oldpeak, slp,caa,thall],
-        [label,local_plot], api_name="Heart_Predictor"
+        [age, sex, cp, trtbps, chol, fbs, restecg, thalachh, exng, oldpeak, slp, caa, thall],
+        [label,local_plot], api_name="Employee_Turnover"
     )
-    
+   
     gr.Markdown("### Click on any of the examples below to see how it works:")
-    gr.Examples([[24,0,4,4,5,5,4,4,5,5,1,2,3], [20,0,4,4,5,5,4,3,5,5,1,2,3]], [age, sex,cp, trtbps, chol, fbs, restecg, thalachh, exng, oldpeak, slp,caa,thall], [label,local_plot], main_func, cache_examples=True)
-
+    gr.Examples([[24,0,4,4,5,4,4,5,5,1,2,3,4], [20,0,3,4,5,4,4,5,5,1,2,3,3]], [age, sex, cp, trtbps, chol, fbs, restecg, thalachh, exng, oldpeak, slp, caa, thall], [label,local_plot], main_func, cache_examples=True)
 demo.launch()