File size: 3,288 Bytes
5a4e33e | 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 | import gradio as gr
import pickle
import numpy as np
from sklearn.preprocessing import StandardScaler
# Load the trained models
models = {
"Logistic": pickle.load(open("logistic_model_best.pkl", "rb")),
"SVC": pickle.load(open("svc_model_best.pkl", "rb")),
"Decision Trees": pickle.load(open("decisiontree_model_best.pkl", "rb")),
"Random Forest": pickle.load(open("rfc_model_best.pkl", "rb")),
"KNN": pickle.load(open("knn_model_best.pkl", "rb")),
"LDA": pickle.load(open("lda_model_best.pkl", "rb"))
}
# Define the input and output dropdowns
age_dropdown = gr.inputs.Number(label='Age')
sex_dropdown = gr.inputs.Dropdown(["Male", "Female"], label="Sex")
cp_dropdown = gr.inputs.Dropdown(["1", "2", "3", "4"], label="Chest Pain Type")
restbp_dropdown = gr.inputs.Number(label='Resting Blood pressure')
chol_dropdown = gr.inputs.Number(label='Serum cholestoral')
fbs_dropdown = gr.inputs.Dropdown(["0", "1"], label="Fasting Blood Sugar")
ecg_dropdown = gr.inputs.Dropdown(["0", "1", "2"], label="Electrocardiographic Results")
max_rt_dropdown = gr.inputs.Number(label="Maximum Heart Rate")
exang_dropdown = gr.inputs.Dropdown(["0", "1"], label="Exercise-Induced Angina")
oldpeak_dropdown = gr.inputs.Number(label="oldpeak =ST Depression Induced by Exercise")
slope_dropdown = gr.inputs.Dropdown(["0", "1", "2"], label="Slope of the ST Segment")
ca_dropdown = gr.inputs.Dropdown(["0", "1", "2", "3"], label="Number of Major Vessels")
thal_dropdown = gr.inputs.Dropdown(["0", "1", "2"], label="Thalassemia")
model_dropdown = gr.inputs.Dropdown(
["Logistic", "SVC", "Decision Trees", "Random Forest", "KNN", "LDA"],
label="Model"
)
xoutputs = [
gr.outputs.Textbox(label="Probability of Heart Disease"),
]
# Define the function to make predictions
import numpy as np
def predict(age, sex, cp, restbp, chol, fbs, ecg, max_rt, exang, oldpeak, slope, ca, thal, model):
# Convert input values to their respective data types
age = int(age)
sex = 1 if sex == "Male" else 0
cp = int(cp)
restbp = int(restbp)
chol = int(chol)
fbs = int(fbs)
ecg = int(ecg)
max_rt = int(max_rt)
exang = int(exang)
oldpeak = float(oldpeak)
slope = int(slope)
ca = int(ca)
thal = int(thal)
# Create the input array
input_array = np.array([age, sex, cp, restbp, chol, fbs, ecg, max_rt, exang, oldpeak, slope, ca, thal]).reshape(1,-1)
# Get the selected model
selected_model = models[model]
scaler = StandardScaler()
normalized_input_array = scaler.fit_transform(input_array)
predi = selected_model.predict(normalized_input_array)
# Convert the prediction to a string
if predi[0] == 1:
return "Absent"
elif predi[0] == 2:
return "Present"
else:
return "Unknown"
# Create the interface
interface = gr.Interface(
fn=predict,
inputs=[
age_dropdown, sex_dropdown, cp_dropdown, restbp_dropdown, chol_dropdown,
fbs_dropdown, ecg_dropdown,max_rt_dropdown, exang_dropdown, oldpeak_dropdown,
slope_dropdown, ca_dropdown, thal_dropdown, model_dropdown
],
outputs = xoutputs,
examples=[[70, 1.0,4.0,130.0,322.0,0.0,2.0,109.0,0.0,2.4,2.0,3.0,2.0,"SVC"]],
title="Heart Disease Prediction",
description="ML HomeWork 04"
)
interface.launch() |