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f3686e0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | # -*- 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)
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