app.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt 
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score 
from sklearn.linear_model import LogisticRegression
import warnings 
warnings.filterwarnings('ignore')
import joblib
import gradio as gr


loaded_model = joblib.load('heart.pkl')



def heart_disease(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca,thal):
#turning the arguments into a numpy array 

 x = np.array([age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca,thal])

 prediction = loaded_model.predict(x.reshape(1, -1))

 if(prediction[0]==0):
 return("The person does not have any heart diseases")
 else:
 return('The person has a heart disease')

outputs = gr.outputs.Textbox()

# Define some example inputs for the interface
examples = [
 [59, 1, 1, 140, 221, 0, 1, 164, 1, 0.0, 2, 0, 2],
 [45, 0, 2, 125, 212, 1, 0, 168, 0, 1.6, 1, 0, 3],
 [72, 1, 3, 160, 114, 0, 0, 115, 0, 1.1, 2, 0, 7],
]

app = gr.Interface(fn=heart_disease, inputs=[
 gr.inputs.Number(label="Age"), 
 gr.inputs.Radio(choices=[("Male", 1), ("Female", 0)], label="Sex"), 
 gr.inputs.Radio(choices=[
 ("Typical Angina", 0), 
 ("Atypical Angina", 1), 
 ("Non-Anginal Pain", 2), 
 ("Asymptomatic", 3)
 ], label="Chest Pain Type"), 
 gr.inputs.Number(label="Resting Blood Pressure (mm Hg)"), 
 gr.inputs.Number(label="Serum Cholesterol Level (mg/dL)"), 
 gr.inputs.Radio(choices=[("Fasting Blood Sugar > 120 mg/dL", 1), ("Fasting Blood Sugar <= 120 mg/dL", 0)], label="Fasting Blood Sugar Level"), 
 gr.inputs.Radio(choices=[
 ("Normal", 0), 
 ("ST-T Wave Abnormality", 1), 
 ("Probable or Definite Left Ventricular Hypertrophy", 2)
 ], label="Resting Electrocardiographic Results"), 
 gr.inputs.Number(label="Maximum Heart Rate Achieved"), 
 gr.inputs.Radio(choices=[("Exercise-Induced Angina", 1), ("No Exercise-Induced Angina", 0)], label="Exercise-Induced Angina"), 
 gr.inputs.Number(label="ST Depression Induced by Exercise Relative to Rest"), 
 gr.inputs.Radio(choices=[("Upsloping", 0), ("Flat", 1), ("Downsloping", 2)], label="Slope of the Peak Exercise ST Segment"), 
 gr.inputs.Number(label="Number of Major Vessels (0-3) Colored by Fluoroscopy"), 
 gr.inputs.Radio(choices=[
 ("Normal", 3), 
 ("Fixed Defect", 6), 
 ("Reversible Defect", 7)
 ], label="Thalassemia")
 ], outputs=outputs, examples=examples,title="Heart Disease Prediction",description=''' 
 This model predicts the presence of heart disease based on various input parameters. Please enter the values for the following inputs:

Description about the inputs. age: The age of the patient in years.
sex: The patient's gender (1 = male, 0 = female).
cp: Chest pain type (0 = typical angina, 1 = atypical angina, 2 = non-anginal pain, 3 = asymptomatic).
trestbps: Resting blood pressure (in mm Hg) on admission to the hospital.
chol: Serum cholesterol level (in mg/dL).
fbs: Fasting blood sugar level (> 120 mg/dL = 1, <= 120 mg/dL = 0).
restecg: Resting electrocardiographic results (0 = normal, 1 = having ST-T wave abnormality, 2 = showing probable or definite left ventricular hypertrophy).
thalach: Maximum heart rate achieved.
exang: Exercise-induced angina (1 = yes, 0 = no).
oldpeak: ST depression induced by exercise relative to rest.
slope: The slope of the peak exercise ST segment (0 = upsloping, 1 = flat, 2 = downsloping).
ca: Number of major vessels (0-3) colored by fluoroscopy.
thal: A blood disorder called thalassemia (3 = normal, 6 = fixed defect, 7 = reversible defect). ''')


app.launch(share=True)