paragon-analytics commited on
Commit
9ae1c78
·
1 Parent(s): 72889cf

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +25 -16
app.py CHANGED
@@ -7,16 +7,18 @@ import numpy as np
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  import matplotlib.pyplot as plt
8
 
9
  # load the model from disk
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- loaded_model = pickle.load(open("h22_xgb.pkl", 'rb'))
11
 
12
  # Setup SHAP
13
  explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
14
 
15
  # Create the main function for server
16
- def main_func(ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,WorkLifeBalance):
17
- new_row = pd.DataFrame.from_dict({'ValueDiversity':ValueDiversity,'AdequateResources':AdequateResources,
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- 'Voice':Voice,'GrowthAdvancement':GrowthAdvancement,'Workload':Workload,
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- 'WorkLifeBalance':WorkLifeBalance}, orient = 'index').transpose()
 
 
20
 
21
  prob = loaded_model.predict_proba(new_row)
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@@ -29,7 +31,7 @@ def main_func(ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,
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  local_plot = plt.gcf()
30
  plt.close()
31
 
32
- return {"Leave": float(prob[0][0]), "Stay": 1-float(prob[0][0])}, local_plot
33
 
34
  # Create the UI
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  title = "**Heart Attack Predictor & Interpreter** 🪐"
@@ -41,17 +43,24 @@ To use the app, click on one of the examples, or adjust the values of the factor
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42
  with gr.Blocks(title=title) as demo:
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  gr.Markdown(f"## {title}")
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- # gr.Markdown("""![marketing](file/marketing.jpg)""")
45
  gr.Markdown(description1)
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  gr.Markdown("""---""")
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  gr.Markdown(description2)
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  gr.Markdown("""---""")
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- ValueDiversity = gr.Slider(label="ValueDiversity Score", minimum=1, maximum=5, value=4, step=1)
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- AdequateResources = gr.Slider(label="AdequateResources Score", minimum=1, maximum=5, value=4, step=1)
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- Voice = gr.Slider(label="Voice Score", minimum=1, maximum=5, value=4, step=1)
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- GrowthAdvancement = gr.Slider(label="GrowthAdvancement Score", minimum=1, maximum=5, value=4, step=1)
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- Workload = gr.Slider(label="Workload Score", minimum=1, maximum=5, value=4, step=1)
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- WorkLifeBalance = gr.Slider(label="WorkLifeBalance Score", minimum=1, maximum=5, value=4, step=1)
 
 
 
 
 
 
 
 
55
  submit_btn = gr.Button("Analyze")
56
 
57
  with gr.Column(visible=True) as output_col:
@@ -60,11 +69,11 @@ with gr.Blocks(title=title) as demo:
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61
  submit_btn.click(
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  main_func,
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- [ValueDiversity,AdequateResources,Voice,GrowthAdvancement,Workload,WorkLifeBalance],
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- [label,local_plot], api_name="Employee_Turnover"
65
  )
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67
  gr.Markdown("### Click on any of the examples below to see how it works:")
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- 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)
69
 
70
  demo.launch()
 
7
  import matplotlib.pyplot as plt
8
 
9
  # load the model from disk
10
+ loaded_model = pickle.load(open("heart_xgb.pkl", 'rb'))
11
 
12
  # Setup SHAP
13
  explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.
14
 
15
  # Create the main function for server
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+ def main_func(age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall):
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+ new_row = pd.DataFrame.from_dict({'age':age,'sex':sex,
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+ 'cp':cp,'trtbps':trtbps,'chol':chol,
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+ 'fbs':fbs, 'restecg':restecg,'thalachh':thalachh,'exng':exng,
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+ 'oldpeak':oldpeak,'slp':slp,'caa':caa,'thall':thall},
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+ orient = 'index').transpose()
22
 
23
  prob = loaded_model.predict_proba(new_row)
24
 
 
31
  local_plot = plt.gcf()
32
  plt.close()
33
 
34
+ return {"Low Chance": float(prob[0][0]), "High Chance": 1-float(prob[0][0])}, local_plot
35
 
36
  # Create the UI
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  title = "**Heart Attack Predictor & Interpreter** 🪐"
 
43
 
44
  with gr.Blocks(title=title) as demo:
45
  gr.Markdown(f"## {title}")
 
46
  gr.Markdown(description1)
47
  gr.Markdown("""---""")
48
  gr.Markdown(description2)
49
  gr.Markdown("""---""")
50
+ age = gr.Slider(label="age Score", minimum=15, maximum=90, value=40, step=5)
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+ sex = gr.Slider(label="sex Score", minimum=0, maximum=1, value=1, step=1)
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+ cp = gr.Slider(label="cp Score", minimum=1, maximum=5, value=4, step=1)
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+ trtbps = gr.Slider(label="trtbps Score", minimum=1, maximum=5, value=4, step=1)
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+ chol = gr.Slider(label="chol Score", minimum=1, maximum=5, value=4, step=1)
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+ fbs = gr.Slider(label="fbs Score", minimum=1, maximum=5, value=4, step=1)
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+ restecg = gr.Slider(label="restecg Score", minimum=1, maximum=5, value=4, step=1)
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+ thalachh = gr.Slider(label="thalachh Score", minimum=1, maximum=5, value=4, step=1)
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+ exng = gr.Slider(label="exng Score", minimum=1, maximum=5, value=4, step=1)
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+ oldpeak = gr.Slider(label="oldpeak Score", minimum=1, maximum=5, value=4, step=1)
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+ slp = gr.Slider(label="slp Score", minimum=1, maximum=5, value=4, step=1)
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+ caa = gr.Slider(label="caa Score", minimum=1, maximum=5, value=4, step=1)
62
+ thall = gr.Slider(label="thall Score", minimum=1, maximum=5, value=4, step=1)
63
+
64
  submit_btn = gr.Button("Analyze")
65
 
66
  with gr.Column(visible=True) as output_col:
 
69
 
70
  submit_btn.click(
71
  main_func,
72
+ [age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall],
73
+ [label,local_plot], api_name="Heart_Predictor"
74
  )
75
 
76
  gr.Markdown("### Click on any of the examples below to see how it works:")
77
+ gr.Examples([[24,0,4,4,5,5,4,4,5,5,1,2,3], [24,0,4,4,5,3,3,2,1,1,1,2,3]], [age, sex, cp, trtbps, chol, fbs, restecg, thalachh,exng,oldpeak,slp,caa,thall], [label,local_plot], main_func, cache_examples=True)
78
 
79
  demo.launch()