# -*- coding: utf-8 -*-
"""Heart_Disease_Prediction.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1cqMvzxYU8QVM8IdfByIymr-3YryFBU5p

Importing the Dependencies
"""

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

"""Data Collection and Processing"""

# loading the csv data to a Pandas DataFrame
heart_data = pd.read_csv('/content/heart_disease_data.csv')

# print first 5 rows of the dataset
heart_data.head()

# print last 5 rows of the dataset
heart_data.tail()

# number of rows and columns in the dataset
heart_data.shape

# getting some info about the data
heart_data.info()

# checking for missing values
heart_data.isnull().sum()

# statistical measures about the data
heart_data.describe()

# checking the distribution of Target Variable
heart_data['target'].value_counts()

"""1 --> Defective Heart

0 --> Healthy Heart

Splitting the Features and Target
"""

X = heart_data.drop(columns='target', axis=1)
Y = heart_data['target']

print(X)

print(Y)

"""Splitting the Data into Training data & Test Data"""

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)

print(X.shape, X_train.shape, X_test.shape)

"""Model Training

Logistic Regression
"""

model = LogisticRegression()

# training the LogisticRegression model with Training data
model.fit(X_train, Y_train)

"""Model Evaluation

Accuracy Score
"""

# accuracy on training data
X_train_prediction = model.predict(X_train)
training_data_accuracy = accuracy_score(X_train_prediction, Y_train)

print('Accuracy on Training data : ', training_data_accuracy)

# accuracy on test data
X_test_prediction = model.predict(X_test)
test_data_accuracy = accuracy_score(X_test_prediction, Y_test)

print('Accuracy on Test data : ', test_data_accuracy)

"""Building a Predictive System"""

def heart_predict(age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal):
  #input_data = (62,0,0,140,268,0,0,160,0,3.6,0,2,2)
  sex=""
  if sex == "male":
    sex = 0
  else:
    sex = 1

  input_data = (age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal)
  # change the input data to a numpy array
  input_data_as_numpy_array= np.asarray(input_data)

  # reshape the numpy array as we are predicting for only on instance
  input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)

  prediction = model.predict(input_data_reshaped)
  print(prediction)

  if (prediction[0]== 0):
    print('The Person does not have a Heart Disease')
    return 'The Person does not have a Heart Disease'
  else:
    print('The Person has Heart Disease')
    return 'The Person has Heart Disease'

heart_predict(62,"female",0,140,268,0,0,160,0,3.6,0,2,2)

#!pip install gradio

import gradio as gr


def heart_predict(age,sex_R,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal):
  #input_data = (62,0,0,140,268,0,0,160,0,3.6,0,2,2)
  sex=""
  if sex == "male":
    sex = 0
  else:
    sex = 1


  input_data = (age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal)
  # change the input data to a numpy array
  input_data_as_numpy_array= np.asarray(input_data)

  # reshape the numpy array as we are predicting for only on instance
  input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)

  prediction = model.predict(input_data_reshaped)
  print(prediction)

  if (prediction[0]== 0):
    print('The Person does not have a Heart Disease')
    return 'The Person does not have a Heart Disease'
  else:
    print('The Person has Heart Disease')
    return 'The Person has Heart Disease'



demo = gr.Interface(
    fn=heart_predict,
    inputs = [
        gr.Slider(1, 100, value=62, label="age", info="Choose between 1 and 20"),
        gr.Radio(["male","female"], value="male", label="sex", info="select"),
        gr.Slider(1, 100, value=0, label="cp", info="Choose between 1 and 100"),
        gr.Slider(1, 50, value=140, label="trestbps", info="Choose between 1 and 50"),
        gr.Slider(1, 200, value=268, label="chol", info="Choose between 1 and 200"),
        gr.Slider(1, 100, value=0, label="fbs", info="Choose between 1 and 100"),
        gr.Slider(0.0, 2.0, value=0, label="restecg", info="Choose between 0.0 and 1.0"),
        gr.Slider(1, 100, value=160, label="thalach", info="Choose between 1 and 100"),
        gr.Slider(1, 100, value=0, label="exang", info="Choose between 1 and 100"),
        gr.Slider(1, 100, value=3.6, label="oldpeak", info="Choose between 1 and 100"),
        gr.Slider(1, 100, value=0, label="slope", info="Choose between 1 and 100"),
        gr.Slider(1, 100, value=2, label="ca", info="Choose between 1 and 100"),
        gr.Slider(1, 100, value=2, label="thal", info="Choose between 1 and 100"),
    ],
    outputs = "text",
)

if __name__ == "__main__":
    demo.launch()