```
import numpy as np
import pickle
import streamlit as st
# loading the saved model
loaded_model = pickle.load(open('trained_model.sav', 'rb'))
# creating a function for Prediction
def diabetes_prediction(input_data):
# changing the input_data to numpy array
input_data_as_numpy_array = np.asarray(input_data)
# reshape the array as we are predicting for one instance
input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)
prediction = loaded_model.predict(input_data_reshaped)
# print(prediction)
if (prediction[0] == 1):
return 'The person is diabetic'
else:
return 'The person is not diabetic'
def main():
# giving a title
st.title('Diabetes Prediction Web App')
# getting the input data from the user
Pregnancies = st.text_input('Number of Pregnancies')
Glucose = st.text_input('Glucose Level')
BloodPressure = st.text_input('Blood Pressure value')
SkinThickness = st.text_input('Skin Thickness value')
Insulin = st.text_input('Insulin Level')
BMI = st.text_input('BMI value')
DiabetesPedigreeFunction = st.text_input('Diabetes Pedigree Function value')
Age = st.text_input('Age of the Person')
# code for Prediction
diagnosis = ''
# creating a button for Prediction
if st.button('Diabetes Test Result'):
diagnosis = diabetes_prediction([Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age])
st.success(diagnosis)
if __name__ == '__main__':
main()
```

Hi @Selvin_Macwan, welcome to the Streamlit community!

Though I’m not entirely sure what you are asking, I think the problem may be here:

```
if (prediction[0] == 1):
return 'The person is diabetic'
else:
return 'The person is not diabetic'
```

For any classification model, the values can generally take the value of 0 to 1, but it’s unlikely that you’ll get exactly a value of 1. Usually you want to test if the model value exceeds some threshold, like 0.5 or whatever the expected rate of ‘success’ is in your sample.

If this is not your issue, please provide more details about the error you are experiencing.

Best,

Randy