Having difficulty get the st.button to work for my machine learning model

[ah9]

Summary

I am trying to get my model result from clicking the button on my app. but I am getting this error listed in the title. I tried checking to see if I left out a bracket or something but I can’t find the error.

Steps to reproduce

Create button to run model

if st.button(“CKD Risk Result”):
input_data = np.array([
age_float,
gender_float,
racegrp_float,
education_float,
marital_status_float,
income_float,
caresource_float,
health_insurance_float,
weight_float,
height_float,
sbp_float,
dbp_float,
hdl_float,
ldl_float,
total_chol_float,
dyslipidemia_float,
pvd_float,
activity_float,
poor_vision_float,
smoker_float,
hypertension_float,
diabetes_float,
stroke_float,
cvd_float,
chf_float,
anemia_float,
fam_hypertension_float,
fam_diabetes_float,
fam_cvd_float
])
# Reshape input_data to match the model’s expected shape if necessary
input_data = input_data.reshape(1, -1)

# Make prediction
prediction = model.predict(input_data)

# Set the diagnosis based on the prediction result
if prediction[0] == 1:
    diagnosis = "CKD"
else:
    diagnosis = "No CKD"

# Display the predicted kidney disease status
st.markdown("The predicted kidney disease status is **{}**".format(diagnosis))

If applicable, please provide the steps we should take to reproduce the error or specified behavior.

Expected behavior:

I expected once the Risk Prediction button is clicked. That it outputs. ‘The predicted kidney disease status is CKD.’ or ‘The predicted kidney disease status is No CKD.’

# Display the predicted kidney disease status
st.markdown("The predicted kidney disease status is **{}**".format(diagnosis))

Actual behavior:

I get the following error message.

ValueError: This app has encountered an error. The original error message is redacted to prevent data leaks. Full error details have been recorded in the logs (if you’re on Streamlit Cloud, click on ‘Manage app’ in the lower right of your app).

Traceback:

File "/home/adminuser/venv/lib/python3.9/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 556, in _run_script
    exec(code, module.__dict__)File "/mount/src/streamlit/HealthStreamlitApp.py", line 162, in <module>
    input_data = np.array([


### Debug info

* Streamlit version: 1.12.0
* Python version: 3.9.7
* Using Conda? =
* OS version: Windows
* Browser version: Chrome

### Requirements file

[https://github.com/AndreaHobby/streamlit/blob/main/requirements.txtpp ](https://github.com/AndreaHobby/streamlit/blob/main/requirements.txtpp).

### Links

* Link to your GitHub repo: [streamlit/HealthStreamlitApp.py at main · AndreaHobby/streamlit · GitHub ](https://github.com/AndreaHobby/streamlit/blob/main/HealthStreamlitApp.py)
* Link to your deployed app: [https://andreahobby-streamlit-healthstreamlitapp-va1lcc.streamlit.app/ ](https://andreahobby-streamlit-healthstreamlitapp-va1lcc.streamlit.app/)

[IM45145V]

I tried your code and made some changes
and getting this error
ValueError: X has 29 features, but GradientBoostingClassifier is expecting 35 features as input.
could it be that you are collecting only 29 inputs but your model requires 35 inputs
i believe thats the reason can you look into it

code i used

import streamlit as st
from PIL import Image
import numpy as np
import pickle

st.set_page_config(layout='wide', page_title='CKD App')
st.title('Chronic Kidney Disease Prediction')
image = Image.open('./image/fnalkidney-comp_1087848275.png')
st.image(image, caption='Kidney')

model = pickle.load(open('model.pkl', 'rb'))

# Define the options and their corresponding float values here

age_options = ["<40", "40-49", "50-59", "60-69", "70+"]
age_float = list(map(float, range(len(age_options))))

gender_options = ["Male", "Female"]
gender_float = list(map(lambda x: float(1.0) if x == "Male" else float(0.0), gender_options))

racegrp_options = ["White", "Black", "Hispanic", "Asian", "Other"]
racegrp_float = list(map(float, range(len(racegrp_options))))

education_options = ["<High School", "High School/GED", "Some College", "College Graduate"]
education_float = list(map(float, range(len(education_options))))

marital_status_options = ["Married", "Widowed", "Divorced", "Separated", "Never Married"]
marital_status_float = list(map(float, range(len(marital_status_options))))

income_options = ["<20k", "20-34k", "35-49k", "50-74k", "75-99k", "100k+"]
income_float = list(map(float, range(len(income_options))))
caresource_options = ["Doctor's Office/Clinic", "Hospital Inpatient", "Emergency Room"]
caresource_float = list(map(float, range(len(caresource_options))))
health_insurance_options = ["Yes", "No"]
health_insurance_float = list(map(float, range(len(health_insurance_options))))

weight_options = ["Underweight", "Normal", "Overweight", "Obese"]
weight_float = list(map(float, range(len(weight_options))))

height_options = ["<5ft", "5ft-5ft5in", "5ft5in-6ft", ">6ft"]
height_float = list(map(float, range(len(height_options))))

sbp_options = ["<120", "120-129", "130-139", "140-149", "150+"]
sbp_float = list(map(float, range(len(sbp_options))))

dbp_options = ["<80", "80-84", "85-89", "90-99", "100+"]
dbp_float = list(map(float, range(len(dbp_options))))

hdl_options = ["<40", "40-49", "50-59", "60+"]
hdl_float = list(map(float, range(len(hdl_options))))

ldl_options = ["<100", "100-129", "130-159", "160-189", "190+"]
ldl_float = list(map(float, range(len(ldl_options))))

total_chol_options = ["<200", "200-239", "240-279", "280+"]
total_chol_float = list(map(float, range(len(total_chol_options))))

dyslipidemia_options = ["Yes", "No"]
dyslipidemia_float = list(map(float, range(len(dyslipidemia_options))))

pvd_options = ["Yes", "No"]
pvd_float = list(map(float, range(len(pvd_options))))

activity_options = ["Inactive", "Moderately Active", "Active"]
activity_float = list(map(float, range(len(activity_options))))

poor_vision_options = ["Yes", "No"]
poor_vision_float = list(map(float, range(len(poor_vision_options))))
smoker_options = ["Current Smoker", "Former Smoker", "Never Smoked"]
smoker_float = list(map(float, range(len(smoker_options))))

hypertension_options = ["Yes", "No"]
hypertension_float = list(map(lambda x: float(1.0) if x == "Yes" else float(0.0), hypertension_options))

diabetes_options = ["Yes", "No"]
diabetes_float = list(map(lambda x: float(1.0) if x == "Yes" else float(0.0), diabetes_options))

stroke_options = ["Yes", "No"]
stroke_float = list(map(lambda x: float(1.0) if x == "Yes" else float(0.0), stroke_options))

cvd_options = ["Yes", "No"]
cvd_float = list(map(lambda x: float(1.0) if x == "Yes" else float(0.0), cvd_options))

chf_options = ["Yes", "No"]
chf_float = list(map(lambda x: float(1.0) if x == "Yes" else float(0.0), chf_options))

anemia_options = ["Yes", "No"]
anemia_float = list(map(lambda x: float(1.0) if x == "Yes" else float(0.0), anemia_options))

fam_hypertension_options = ["Yes", "No"]
fam_hypertension_float = list(map(lambda x: float(1.0) if x == "Yes" else float(0.0), fam_hypertension_options))

fam_diabetes_options = ["Yes", "No"]
fam_diabetes_float = list(map(lambda x: float(1.0) if x == "Yes" else float(0.0), fam_diabetes_options))

fam_cvd_options = ["Yes", "No"]
fam_cvd_float = list(map(lambda x: float(1.0) if x == "Yes" else float(0.0), fam_cvd_options))


st.subheader('Demographics')
age = st.selectbox("Age", age_options)
gender = st.selectbox("Gender", gender_options)
racegrp = st.selectbox("Race", racegrp_options)
education = st.selectbox("Education", education_options)
marital_status = st.selectbox("Marital Status", marital_status_options)
income = st.selectbox("Income", income_options)

st.subheader('Healthcare')
caresource = st.selectbox("Where do you usually receive your healthcare?", caresource_options)
health_insurance = st.selectbox('Do you have health insurance?', health_insurance_options)
weight = st.selectbox('What is your Weight?', weight_options)
height = st.selectbox('What is your Height?', height_options)
sbp = st.selectbox('What is your systolic blood pressure?', sbp_options)
dbp = st.selectbox('What is your diastolic blood pressure?', dbp_options)
HDL = st.selectbox('What is your HDL?', hdl_options)
LDL = st.selectbox('What is your LDL?', ldl_options)
Total_Cholesterol = st.selectbox('What is your Total Cholesterol?', total_chol_options)
Dyslipidemia = st.selectbox('Do you have Dyslipidemia?', dyslipidemia_options)
PVD = st.selectbox('Do you have a history Peripheral Artery Disease?', pvd_options)
Activity = st.selectbox('What is your level of physical activity every week?', activity_options)
Poor_Vision = st.selectbox('Do you have poor vision that cannot be corrected with glasses, contact lenses, or surgery?', poor_vision_options)
Smoker = st.selectbox('Are you a current or former smoker?', smoker_options)
Hypertension = st.selectbox('Do you have hypertension?', hypertension_options)
Diabetes = st.selectbox('Do you have diabetes?', diabetes_options)
Stroke = st.selectbox('Have you had a stroke before?', stroke_options)
CVD = st.selectbox('Do you have a history of cardiovascular disease?', cvd_options)
CHF = st.selectbox('Do you have a history of congestive heart failure?', chf_options)
Anemia = st.selectbox('Do you have a history of anemia?', anemia_options)

st.subheader('Family History')
Fam_Hypertension = st.selectbox('Do you have a family history of Hypertension?', fam_hypertension_options)
Fam_Diabetes = st.selectbox('Do you have a family history of Diabetes?', fam_diabetes_options)
Fam_CVD = st.selectbox('Do you have a family history of cardiovascular disease?', fam_cvd_options)

if st.button("CKD Risk Result"):
    input_data = np.array([
        age_float[age_options.index(age)],
        gender_float[gender_options.index(gender)],
        racegrp_float[racegrp_options.index(racegrp)],
        education_float[education_options.index(education)],
        marital_status_float[marital_status_options.index(marital_status)],
        income_float[income_options.index(income)],
        caresource_float[caresource_options.index(caresource)],
        health_insurance_float[health_insurance_options.index(health_insurance)],
        weight_float[weight_options.index(weight)],
        height_float[height_options.index(height)],
        sbp_float[sbp_options.index(sbp)],
        dbp_float[dbp_options.index(dbp)],
        hdl_float[hdl_options.index(HDL)],
        ldl_float[ldl_options.index(LDL)],
        total_chol_float[total_chol_options.index(Total_Cholesterol)],
        dyslipidemia_float[dyslipidemia_options.index(Dyslipidemia)],
        pvd_float[pvd_options.index(PVD)],
        activity_float[activity_options.index(Activity)],
        poor_vision_float[poor_vision_options.index(Poor_Vision)],
        smoker_float[smoker_options.index(Smoker)],
        hypertension_float[hypertension_options.index(Hypertension)],
        diabetes_float[diabetes_options.index(Diabetes)],
        stroke_float[stroke_options.index(Stroke)],
        cvd_float[cvd_options.index(CVD)],
        chf_float[chf_options.index(CHF)],
        anemia_float[anemia_options.index(Anemia)],
        fam_hypertension_float[fam_hypertension_options.index(Fam_Hypertension)],
        fam_diabetes_float[fam_diabetes_options.index(Fam_Diabetes)],
        fam_cvd_float[fam_cvd_options.index(Fam_CVD)]
    ])

    input_data = input_data.reshape(1, -1)
    prediction = model.predict(input_data)
    if prediction[0] == 1:
        diagnosis = "CKD"
    else:
        diagnosis = "No CKD"

    st.markdown("The predicted kidney disease status is **{}**".format(diagnosis))

Let me know if i made any mistakes while trying to solve this