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 predict_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],dtype=float)

  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=predict_heart_disease,   inputs=[
        gr.inputs.Number(label="Age"), 
        gr.inputs.Number(label="Sex (0 for Female, 1 for Male)"), 
        gr.inputs.Number(label="Chest Pain Type (0 for Typical Angina, 1 for Atypical Angina, 2 for Non-Anginal Pain, 3 for Asymptomatic)"), 
        gr.inputs.Number(label="Resting Blood Pressure (mm Hg)"), 
        gr.inputs.Number(label="Serum Cholesterol Level (mg/dL)"), 
        gr.inputs.Number(label="Fasting Blood Sugar Level (0 for <= 120 mg/dL, 1 for > 120 mg/dL)"), 
        gr.inputs.Number(label="Resting Electrocardiographic Results (0 for Normal, 1 for ST-T Wave Abnormality, 2 for Probable or Definite Left Ventricular Hypertrophy)"), 
        gr.inputs.Number(label="Maximum Heart Rate Achieved"), 
        gr.inputs.Number(label="Exercise-Induced Angina (0 for No, 1 for Yes)"), 
        gr.inputs.Number(label="ST Depression Induced by Exercise Relative to Rest"), 
        gr.inputs.Number(label="Slope of the Peak Exercise ST Segment (0 for Upsloping, 1 for Flat, 2 for Downsloping)"), 
        gr.inputs.Number(label="Number of Major Vessels (0-3) Colored by Fluoroscopy"), 
        gr.inputs.Number(label="Thalassemia (3 for Normal, 6 for Fixed Defect, 7 for Reversible Defect)")
    ],  outputs=outputs, examples=examples,examples_output = [predict_heart_disease(*example) for example in 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()