File size: 8,192 Bytes
3f70783 c4b9c36 3291fa0 3f70783 c4b9c36 3f70783 2b9bd8f 3f70783 3291fa0 3f70783 e4e6b6a 3f70783 e4e6b6a 25d0745 e4e6b6a fa897fb 11c425a 5c60915 11c425a 5c60915 11c425a 5c60915 11c425a e4e6b6a 3f70783 c2a1140 3f70783 25d0745 4873e3d c2a1140 3f70783 6e1aea4 5f463bc 3f70783 3291fa0 ac3a0cf 3f70783 7d6b4cd 3f70783 ad6f947 3f70783 e4e6b6a 3f70783 2b9bd8f 3f70783 e4e6b6a 3f70783 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 |
import gradio as gr
from model import SmokerModel
import numpy as np
MODEL = SmokerModel("ensemble_softvoting_model.joblib","min_max_scaler.joblib")
def predict(
age, height, weight,
waist, eye_L, eye_R,
hear_L, hear_R, systolic,
relaxation, fasting_blood_sugar, cholesterol,
triglyceride, HDL, LDL,
hemoglobin, urine_protein,
serum_creatinine, AST, ALT,
Gtp, dental_caries
):
'''
Predict the label for the data inputed
'''
# Combine the input data into a NumPy array
input_array = np.array([
age, height, weight,
waist, eye_L, eye_R,
hear_L, hear_R, systolic,
relaxation, fasting_blood_sugar, cholesterol,
triglyceride, HDL, LDL,
hemoglobin, urine_protein,
serum_creatinine, AST, ALT,
Gtp, dental_caries
])
#predict
label = MODEL.predict(input_array)
return label
def load_interface():
'''
Configure Gradio interface
'''
#set blocks
info_page = gr.Blocks()
model_page = gr.Blocks()
with info_page:
# set title and description
gr.Markdown( #TODO: add information about our project: github, description, model metrics, etc.
"""
# Ensemble Classifier for Predicting Smoker or Non-Smoker
**Contributors**: Matt Soria, Jake Leniart, Francisco Lozano
**University**: Depaul University
**Class**: DSC 478, Programming Machine Learning
## Overview
Our project focused on creating a classifier for a Kaggle dataset containing bio-signals and information on individuals' smoking status. The classifier aims to identify whether a patient is a smoker based on 22 provided features. You can find the dataset [here](https://www.kaggle.com/datasets/gauravduttakiit/smoker-status-prediction-using-biosignals?resource=download&select=train_dataset.csv).
We developed an Ensemble Classifier with Soft Voting, which combines KNN, SVM, and XGBoost classifiers.
## Classifier Metrics
TODO: insert the classification report and confusion matrix
## Report
For more details about our Ensemble Classifier and the individual models, please refer to our Jupyter notebooks in our project repository.
[DSC 478 Project Repo](https://github.com/msoria17/dsc478-project)
"""
)
with model_page:
# set title and description
gr.Markdown(
"""
# Interact with the Ensemble Classifier Model
Enter sample bio data to predict smoking status.
Medical Disclaimer: The predictions provided by this model are for educational purposes only and should not be considered a substitute for professional medical advice.
""")
#set inputs in rows of 3
with gr.Row():
age = gr.Number(label="Age", precision=0, minimum=0)
height = gr.Number(label="Height(cm)", precision=0, minimum=0)
weight = gr.Number(label="Weight(kg)", precision=0, minimum=0)
with gr.Row():
waist = gr.Number(label="Waist(cm)", minimum=0, info="Waist circumference length")
eye_L = gr.Number(label="Visual acuity of the left eye, measured in diopters (D)", minimum=0)
eye_R = gr.Number(label="Visual acuity of the right eye, measured in diopters (D)", minimum=0)
with gr.Row():
hear_L = gr.Radio(label="Is there any hearing ability in the left ear?",choices=[("Yes",1),("No",2)])
hear_R = gr.Radio(label="Is there any hearing ability in the right ear?",choices=[("Yes",1),("No",2)])
systolic = gr.Number(label="Systolic(mmHg)", precision=0, minimum=0, info="Blood Pressure")
with gr.Row():
relaxation = gr.Number(label="Relaxation(mmHg)", precision=0, minimum=0, info="Blood Pressure")
fasting_blood_sugar = gr.Number(label="Fasting Blood Sugar(mg/dL)", precision=0, minimum=0, info="the concentration of glucose (sugar) in the bloodstream after an extended period of fasting")
cholesterol = gr.Number(label="Total Cholesterol(mg/dL)", precision=0, minimum=0, info="Total amount of cholesterol present in the blood")
with gr.Row():
triglyceride = gr.Number(label="Triglyceride(mg/dL)", precision=0, minimum=0, info="A type of fat (lipid) found in blood")
HDL = gr.Number(label="High-Density Lipoprotein(mg/dL) ", precision=0, minimum=0, info="It is commonly referred to as 'good cholesterol'")
LDL = gr.Number(label="Low-Density Lipoprotein(mg/dL) ", precision=0, minimum=0, info="It is commonly referred to as 'bad cholesterol'")
with gr.Row():
hemoglobin = gr.Number(label="Hemoglobin(g/dL)", minimum=0, info="a protein found in red blood cells that is responsible for carrying oxygen from the lungs to the tissues and organs of the body")
urine_protein = gr.Radio(label="Does urine contain excessive traces of protein?",choices=[("Yes",2),("No",1)], info="when excessive protein is detected in the urine, it may indicate a problem with kidney function or other underlying health conditions.")
serum_creatinine = gr.Number(label="Serum creatinine(mg/dL)", minimum=0, info="Serum creatinine levels are commonly measured through a blood test and are used to assess kidney function")
with gr.Row():
AST = gr.Number(label="Aspartate Aminotransferase(IU/L)", precision=0, minimum=0, info="glutamic oxaloacetic transaminase type; AST is released into the bloodstream when cells are damaged or destroyed, such as during injury or disease affecting organs rich in AST.")
ALT = gr.Number(label="Alanine Aminotransferase(IU/L)", precision=0, minimum=0, info="glutamic oxaloacetic transaminase type; ALT is primarily found in the liver cells, and increased levels of ALT in the blood can indicate liver damage or disease")
Gtp = gr.Number(label="Gamma-glutamyl Transferase(IU/L)", precision=0, minimum=0, info="Elevated levels of GGT in the blood can indicate liver disease or bile duct obstruction. GGT levels are often measured alongside other liver function tests to assess liver health and function.")
dental_caries = gr.Radio(label="Are there any signs of dental cavities?",choices=[("Yes",1),("No",0)])
#set button row
with gr.Row():
pred_btn = gr.Button("Predict")
clear_btn = gr.Button("Clear")
#set label txt box
#TODO: change back to gr.Textbox()?
smoker_label = gr.Label(label="Predicted Label")
#set event listeners
inputs = [age, height, weight, waist, eye_L, eye_R, hear_L, hear_R, systolic, relaxation, fasting_blood_sugar, cholesterol, triglyceride, HDL, LDL, hemoglobin, urine_protein, serum_creatinine, AST, ALT, Gtp, dental_caries]
pred_btn.click(fn=predict, inputs=inputs, outputs=smoker_label)
clear_btn.click(lambda: [None]*22, outputs=inputs)
#add examples
examples= [
[20,85,135,190,30,125,53,126,0.1,9.9,0.1,9.9,1,2,1,2,79,240,40,140,55,505,72,371,16,405,4,618,1,1660,4.9,20.9,1,6,0.1,10.3,6,1311,1,2062,1,999,0,1],
[40,170,65,75.1,1.0,0.9,1,1,120,70,102,225,260,41,132,15.7,1,0.8,24,26,32,0,45,170,75,89.0,0.7,1.2,1,1,100,67,96,258,345,49,140,15.7,1,1.1,26,28,138,0,30],
[180,90,94.0,1.0,0.8,1,1,115,72,88,177,103,53,103,13.5,1,1.0,19,29,30,0,60,170,65,78.0,1.5,1.0,1,1,110,70,87,190,210,45,103,14.7,1,0.8,21,21,19,0,55],
[175,60,75.0,1.0,1.0,1,1,100,64,93,186,80,86,84,15.4,3,1.0,39,20,35,0,40,160,55,69.0,1.5,1.5,1,1,112,78,90,177,68,78,85,12.4,1,0.5,15,9,14,0,55],
[175,60,80.0,1.2,1.5,1.5,1,1,137,89,80,199,35,68,124,16.0,1,1.1,23,19,17,0,55,160,50,68.0,0.8,0.5,1,1,137,87,90,176,36,67,102,13.6,1,0.7,15,14,13,0]
]
iface = gr.TabbedInterface(
[info_page, model_page],
["Information", "Smoker Model"]
)
#TODO: include examples like here: https://www.gradio.app/docs/number
iface.launch(server_name="0.0.0.0", server_port=7860)
if __name__ == "__main__":
load_interface() |