# -*- coding: utf-8 -*-
import gradio as gr
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn.metrics import accuracy_score
kkk_diabetes_dataset = pd.read_csv('diabetes.csv') 
X = kkk_diabetes_dataset.drop(columns = 'Outcome', axis=1)
Y = kkk_diabetes_dataset['Outcome']
scaler = StandardScaler()
scaler.fit(X)
standardized_data = scaler.transform(X)

X = standardized_data
Y = kkk_diabetes_dataset['Outcome']
from sklearn.neural_network import MLPClassifier
classifier =  MLPClassifier(max_iter=1000,  alpha=1)
X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.2, stratify=Y, random_state=2)
classifier.fit(X_train, Y_train)

def diabetes(Pregnancies, Glucose_levels_in_millimoles_per_litres, Blood_Pressure_in_millimetres_of_mercury, Skin_Thickness_in_millimetres, Insulin_levels, BMI_in_kilogram_per_square_metres, Diabetes_Pedigree, Age_in_years):
    x = np.array([Pregnancies, Glucose_levels_in_millimoles_per_litres, Blood_Pressure_in_millimetres_of_mercury, Skin_Thickness_in_millimetres, Insulin_levels, BMI_in_kilogram_per_square_metres, Diabetes_Pedigree, Age_in_years])
    prediction = classifier.predict(x.reshape(1, -1))
    if prediction == 0:
        return "Patient is NOT DIABETIC"
    elif prediction == 1:
        return "Patient is DIABETIC"

outputs = gr.outputs.Textbox()
app = gr.Interface(fn=diabetes, 
                   inputs=[gr.inputs.Slider(0,9,step=1,label= 'How many times has the patient been pregnant, 0 if unapplicable'),
                           'number',
                           'number',
                           'number',
                           'number',
                           'number',
                           gr.inputs.Slider(0,1,label= 'Diabetes Pedigree function'),
                           'number'], 
                   outputs='text',
                   theme="grass",
                   title="kkk's Machine Learning App",
                   description="This is a diabetes model")

app.launch(inline = False)