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from statistics import mode | |
from cProfile import label | |
from joblib import load | |
import matplotlib.pyplot as plt | |
import gradio as gr | |
import numpy as np | |
def getdata(Age,Sex,CP,Trtbps,Chol,Fbs,Restecg,Thalachh,Oldpeak,Slp,Caa,Thall,Exng): | |
if Sex == "Male": | |
Sex = 1 | |
else: | |
Sex = 0 | |
if CP == "Typical Angina": | |
CP = 0 | |
elif CP == "Atypical Angina": | |
CP = 1 | |
elif CP == "Non-anginal Pain": | |
CP = 2 | |
else: | |
CP = 3 | |
if Fbs == "True": | |
Fbs = 1 | |
else: | |
Fbs = 0 | |
if Restecg == "Normal": | |
Restecg = 0 | |
elif Restecg == "ST-T wave normality": | |
Restecg = 1 | |
else: | |
Restecg = 2 | |
if Exng == "Yes": | |
Exng = 1 | |
else: | |
Exng = 0 | |
a = [Age,Sex,CP,Trtbps,Chol,Fbs,Restecg,Thalachh,Exng,Oldpeak,Slp,Caa,Thall] | |
arr = np.array([a]) | |
return arr | |
def getfig(X_test): | |
X_pca = load('X_pca.data') | |
y = load('y.data') | |
pca = load('pca.dim') | |
u_pca = pca.transform(X_test) | |
fig = plt.figure(figsize=(5,4)) | |
plt.scatter(X_pca[:, 0], X_pca[:, 1], c = y, cmap = plt.cm.Spectral, s = 10) | |
plt.scatter(u_pca[:, 0], u_pca[:, 1], c = 'g', cmap = plt.cm.Spectral, s = 40) | |
plt.title(f"PCA, Exp. Variance: {np.round(np.sum(pca.explained_variance_ratio_), 4)}") | |
plt.xlabel("PC 1") | |
plt.ylabel("PC 2") | |
return fig | |
def greet(Age,Sex,CP,Trtbps,Chol,Fbs,Restecg,Thalachh,Oldpeak,Slp,Caa,Thall,Exng): | |
X_test = getdata(Age,Sex,CP,Trtbps,Chol,Fbs,Restecg,Thalachh,Oldpeak,Slp,Caa,Thall,Exng) | |
scaler = load('stdscaler.model') | |
x_std = scaler.transform(X_test) | |
log_reg = load('log_reg.model') | |
y_lr = log_reg.predict(x_std) | |
kmeans = load('kmeans.model') | |
y_km = kmeans.predict(x_std) | |
tree = load('tree.model') | |
y_tree = tree.predict(x_std) | |
nb = load('nb.model') | |
y_bayes = nb.predict(X_test) | |
forest = load('forest.model') | |
y_forest = forest.predict(X_test) | |
r = [y_lr[0], y_km[0], y_tree[0], y_bayes[0], y_forest[0]] | |
f = mode(r) | |
if f == 0: | |
x = "You have less chance of heart attack" | |
else: | |
x = "You have more chance of heart attack" | |
fig = load('dime.fig') | |
fig2 = getfig(X_test) | |
return x, fig, fig2 | |
interface = gr.Interface( | |
title = "HeartAttack prediction - UMG <br> Project Coeur ❤", | |
description = "<h3>The idea is to classify between 0 = less chance of heart attack and 1 = more chance of heart attack, according to the data provided by the user.</h3>"+ | |
"<b>Models:</b> Logistic Regression, K-means, Decision Trees, Naive Bayes and Random Forest"+ | |
"<br><b>Metrics:</b> Accuracy: 0.82, Precision: 0.775, Recall: 0.939, F1 Score: 0.849 <br> <br><b>Please provide the requested data:</b>", | |
article='Step-by-step on GitHub <a href="https://github.com/Adrian8aS/Machine-Learning-App-Gradio/blob/21246d9ba87859e9068369b89d48b4c6ee13dfe5/Proyecto%20integrador.ipynb"> notebook </a> '+ | |
'<br>Dashboard of our train data <a href="https://1drv.ms/x/s!At7E16oDTBiKktUagvJHHpF5CCoITA?e=fOLjUq"> here! </a> '+ | |
'<br>Privacy Policy <a href="https://raw.githubusercontent.com/rulasvrdz/DataMining/main/Texto.txt"> here! </a> '+ | |
"<br><br> ~ Project Coeur", | |
allow_flagging = "never", | |
fn = greet, | |
inputs = [ | |
gr.Number(label="Age of the patient"), | |
gr.Radio(["Male", "Female"], label="Sex of the patient"), | |
gr.Radio(["Typical Angina", "Atypical Angina", "Non-anginal Pain", "Asymptomatic"], label="Chest pain type"), | |
gr.Number(label="Resting blood pressure (in mm Hg)"), | |
gr.Number(label="Cholestoral in mg/dl fetched via BMI sensor"), | |
gr.Radio(["True", "False"], label="Fasting blood sugar > 120 mg/dl"), | |
gr.Radio(["Normal", "ST-T wave normality", "Left ventricular hypertrophy"], label="Resting electrocardiographic results"), | |
gr.Number(label="Maximum heart rate achieved"), | |
gr.Number(label="Previous peak"), | |
gr.Radio([0, 1, 2], label="Slope"), | |
gr.Radio([0, 1, 2, 3, 4], label="Number of major vessels"), | |
gr.Radio([0, 1, 2, 3], label="Thalium Stress Test result"), | |
gr.Radio(["Yes", "No"], label="Exercise induced angina") | |
], | |
outputs = [gr.Text(label="Prediction"), 'plot', 'plot'], | |
examples = [[41,"Female","Typical Angina",130,204,"False","Normal",172,1.4,2,0,2,"No"], | |
[45,"Male","Non-anginal Pain",110,264,"False","ST-T wave normality",132,0.2,1,0,3,"No"]] | |
) | |
interface.launch(share = False) |