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| from sklearn.ensemble import BaggingClassifier | |
| import pickle | |
| model = pickle.load(open('model_diabetes.pkl','rb')) | |
| def classify(num): | |
| if num<1: | |
| return 'negative' | |
| else: | |
| return 'positive' | |
| import gradio as gr | |
| import numpy as np | |
| def predict_diabetes(preg,glu,bp,st,ins,bmi,dpf,age): | |
| input_array=np.array([[preg,glu,bp,st,ins,bmi,dpf,age]]) | |
| pred=model.predict(input_array) | |
| output=classify(pred[0]) | |
| if output=='negative': | |
| return [(0,output)] | |
| else: | |
| return [(1,output)] | |
| preg = gr.inputs.Slider(minimum=0, maximum=17, default=2, label="Pregnancy") | |
| glu = gr.inputs.Slider(minimum=0, maximum=199, default=2, label="glucose") | |
| bp = gr.inputs.Slider(minimum=0, maximum=122, default=2, label="blood prussure") | |
| st = gr.inputs.Slider(minimum=0, maximum=99, default=2, label="skin thickness") | |
| ins = gr.inputs.Slider(minimum=0, maximum=846, default=2, label="insulin") | |
| bmi = gr.inputs.Slider(minimum=0, maximum=67.1, default=2, label="bmi") | |
| dpf = gr.inputs.Slider(minimum=0, maximum=2.5, default=2, label="diabetes pedigree function") | |
| age = gr.inputs.Slider(minimum=20, maximum=100, default=2, label="age") | |
| op=gr.outputs.HighlightedText(color_map={ "negative": "green", | |
| "positive": "red",}) | |
| gr.Interface(predict_diabetes, inputs=[preg,glu,bp,st,ins,bmi,dpf,age], outputs=op,live=True).launch() |