app.py

import numpy
import gradio as gr
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
from sklearn.preprocessing import MinMaxScaler
def heart(Age,Sex,ChestPainType,RestingBP,Cholesterol,FastingBS,RestingECG,MaxHR,ExerciseAngina,Oldpeak,ST_Slope):
  d=pd.read_csv("https://raw.githubusercontent.com/akarshsnair/Dataset-cart/main/heart.csv")
  for col in d.columns:
      if d[col].dtype == 'object':
          d[col] = LabelEncoder().fit_transform(d[col])
  svm=SVC(probability=True)
  xdf=d.drop("HeartDisease",axis=1)
  ydf=d["HeartDisease"]
  x_train,x_test,y_train,y_test=train_test_split(xdf,ydf,test_size=0.35,random_state=20)
  mms=MinMaxScaler(feature_range=(0,1))
  x_train=mms.fit_transform(x_train)
  x_test=mms.fit_transform(x_test)
  x_train=pd.DataFrame(x_train)
  x_test=pd.DataFrame(x_test)
  svm=SVC(probability=True)
  svm.fit(x_train,y_train)
  predictions=svm.predict(x_test)
  data = {'Age':Age,'Sex':Sex,'Chest Pain Type':ChestPainType,'Resting Blood Pressure':RestingBP,'Cholesterol level ':Cholesterol,'Fasting Blood Sugar':FastingBS,'Resting E.C.G':RestingECG,'Maximum Heart Rate achieved ':MaxHR,'Exercise induced Angina ':ExerciseAngina,'Old peak':Oldpeak,'ST_Slope':ST_Slope}
  index = [0]
  cust_df = pd.DataFrame(data, index)
  costpredLog = svm.predict(cust_df)
  if costpredLog ==0:
    Prediction = "There is less chance for the patient to catch with heart disease"
  else:
    Prediction = "There is more of a chance for the patient to catch heart disease."
  return  Prediction
  
  
iface = gr.Interface(fn = heart,
                     
inputs =['number','number','number','number','number','number','number','number','number','number','number'],

outputs =['text'],

title="Onset of heart disease failure prediction",

description ='''    Description
Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide. 
Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure.
Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, 
physical inactivity and harmful use of alcohol using population-wide strategies. People with cardiovascular disease or who are at high cardiovascular risk 
(due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) 
need early detection and management wherein a machine learning model can be of great help.

More details about the Inputs taken and how they needed to be taken are given below:

     * Age (Your Age)
     * Sex (Male (1) or Female (0))
     * Chest pain 
        1) ASY - 0
        2) ATA - 1
        3) NAP - 2
        4) TA - 3
     * RestingBP - Resting Blood pressure
     * Cholesterol - Cholesterol level as of now
     * FastingBS - Fasting blood sugar 
        1) Above 120,Type 1
        2) Below 120,Type 0
     * RestingECG 
        1) LVH - 0
        2) NORMAL - 1
        3) ST - 2
     * MaxHR - Is the maximum heart rate recorded
     * ExerciseAngina - Do you have Angine while you exercise 
        1) No - 0
        2) Yes - 1
     * Oldpeak -  ST depression induced by exercise relative to rest.
     * ST_Slope - The ST segment shift relative to exercise-induced increments in heart rate
        1) Down - 0
        2) Flat - 1
        3) UP - 2

''',

article=''' This application is made for Hackathon'22

Through this project, we are looking forward to providing insight into the health of each and every person.
The dataset is taken from Kaggle and the link for the dataset is provided here:

Dataset link as Raw form: https://raw.githubusercontent.com/ADITHYASNAIR2021/Dataset-cart/main/heart.csv

''')

iface.launch(debug = True)