Hugging Face's logo

Spaces:
runaksh
/
Patientsurvival-model
Runtime error

App Files Community
Patientsurvival-model
File size: 4,185 Bytes 
4c2f6d7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
21daefd
4c2f6d7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
 
from fastapi import FastAPI, Request, Response

import gradio
import joblib
from xgboost import XGBClassifier
import pandas as pd
import numpy as np

from sklearn.metrics import f1_score, precision_score, recall_score
import prometheus_client as prom

app = FastAPI()

username = "runaksh"
repo_name = "Patientsurvival-model"
repo_path = username+ '/' + repo_name
xgb_model_loaded = joblib.load("xgboost-model.pkl")

import pandas as pd

test_data = pd.read_csv("test_data.csv")

f1_metric = prom.Gauge('sentiment_f1_score', 'F1 score for random 100 test samples')
precision_metric = prom.Gauge('sentiment_precision_score', 'Precision score for random 100 test samples')
recall_metric = prom.Gauge('sentiment_recall_score', 'Recall score for random 100 test samples')


# Function for response generation
# Load your trained model

def bol_to_int(bol):
  if bol==True:
    return 1
  else:
    return 0

# Function for prediction
def predict_death_event(feature1, feature2, feature3,feature4, feature5, feature6, feature7, feature8, feature9, feature10, feature11, feature12):
    data = {'age' : [feature1], 'anaemia' : [bol_to_int(feature2)],
            'creatinine_phosphokinase' : [feature3],
            'diabetes' : [bol_to_int(feature4)],
            'ejection_fraction' : [feature5],
            'high_blood_pressure' : [bol_to_int(feature6)],
            'platelets' : [feature7],
            'serum_creatinine' : [feature8],
            'serum_sodium' : [feature9],
            'sex' : [bol_to_int(feature10)],
            'smoking' : [bol_to_int(feature11)],
            'time' : [feature12]}
    df = pd.DataFrame(data)
    y_pred = xgb_model_loaded.predict(df)[0]
    return y_pred

# Function for updating metrics
def update_metrics():
    test = test_data.sample(100)
    test_text = test['Text'].values
    test_pred = sentiment_model(list(test_text))
    pred_labels = [int(pred['label'].split("_")[1]) for pred in test_pred]
    f1 = f1_score(test['labels'], pred_labels).round(3)
    precision = precision_score(test['labels'], pred_labels).round(3)
    recall = recall_score(test['labels'], pred_labels).round(3)
    
    f1_metric.set(f1)
    precision_metric.set(precision)
    recall_metric.set(recall)


@app.get("/metrics")
async def get_metrics():
    update_metrics()
    return Response(media_type="text/plain", content= prom.generate_latest())

# Gradio interface to generate UI link
title = "Patient Survival Prediction"
description = "Predict survival of patient with heart failure, given their clinical record"

iface = gradio.Interface(fn = predict_death_event,
                         inputs=[
                            gradio.components.Slider(30, 100, step=1, label= 'age'),
                            gradio.components.Radio(["0","1"], label= 'anaemia'),
                            gradio.components.Slider(1, 10000, step=1, label= 'creatinine_phosphokinase'),
                            gradio.components.Radio(["0","1"], label= 'diabetes'),
                            gradio.components.Slider(1, 100, step=1, label= 'ejection_fraction'),
                            gradio.components.Radio(["0","1"], label= 'high_blood_pressure'),
                            gradio.components.Number(label= 'platelets'),
                            gradio.components.Slider(0.1, 10.0, step=0.1, label= 'serum_creatinine'),
                            gradio.components.Slider(100, 150, step=1, label= 'serum_sodium'),
                            gradio.components.Radio(["0","1"], label= 'sex'),
                            gradio.components.Radio(["0","1"], label= 'smoking'),
                            gradio.components.Slider(1, 300, step=1, label= 'time')],
                         outputs = [gradio.components.Textbox (label ='DeathEvent')],
                         title = title,
                         description = description)

app = gradio.mount_gradio_app(app, iface, path="/")

#iface.launch(server_name = "0.0.0.0", server_port = 8001) # Ref. for parameters: https://www.gradio.app/docs/interface

if __name__ == "__main__":
    # Use this for debugging purposes only
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8001)