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| import gradio as gr | |
| import joblib | |
| import numpy as np | |
| def preprocessing(gender): | |
| if gender == "Male": | |
| return 0 | |
| elif gender == "Female": | |
| return 1 | |
| def predict(Gender,Haemoglobin,MCH,MCHC,MCV): | |
| model = joblib.load("models/svm.pkl") | |
| gender = preprocessing(Gender) | |
| data = np.array([[gender,Haemoglobin,MCH,MCHC,MCV]]) | |
| pred = model.predict(data) | |
| if pred == 0: | |
| return "Not Anaemic" | |
| else: | |
| return "Anaemic" | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs=[gr.Radio(choices=["Male","Female"],value="str",label="Gender",info="Choose Gender"), | |
| gr.Number(label="Haemoglobin"), | |
| gr.Number(label="MCH"), | |
| gr.Number(label="MCHC"), | |
| gr.Number(label="MCV"), | |
| ], | |
| outputs=["text"], | |
| ) | |
| demo.launch() | |