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import streamlit as st | |
import joblib | |
import pandas as pd | |
# Modeli yükle | |
model = joblib.load('thyroid_cancer_model.pkl') | |
# Uygulama başlığı | |
st.title('Thyroid Cancer Recurrence Prediction') | |
# Giriş verilerini al | |
age = st.number_input('Age', min_value=0, max_value=100, step=1) | |
gender = st.selectbox('Gender', ['M', 'F']) | |
smoking = st.selectbox('Smoking', ['Yes', 'No']) | |
hx_smoking = st.selectbox('Hx Smoking', ['Yes', 'No']) | |
hx_radiotherapy = st.selectbox('Hx Radiotherapy', ['Yes', 'No']) | |
thyroid_function = st.selectbox('Thyroid Function', ['Euthyroid', 'Clinical Hyperthyroidism', 'Subclinical Hyperthyroidism']) | |
physical_examination = st.selectbox('Physical Examination', ['Single nodular goiter-left', 'Multinodular goiter', 'Single nodular goiter-right']) | |
adenopathy = st.selectbox('Adenopathy', ['No', 'Right', 'Left', 'Bilateral', 'Extensive']) | |
pathology = st.selectbox('Pathology', ['Micropapillary', 'Papillary', 'Follicular', 'Hurthel cell']) | |
focality = st.selectbox('Focality', ['Uni-Focal', 'Multi-Focal']) | |
risk = st.selectbox('Risk', ['Low', 'Intermediate', 'High']) | |
t = st.selectbox('T', ['T1a', 'T1b', 'T2', 'T3', 'T4a', 'T4b']) | |
n = st.selectbox('N', ['N0', 'N1a', 'N1b']) | |
m = st.selectbox('M', ['M0', 'M1']) | |
stage = st.selectbox('Stage', ['I', 'II', 'III', 'IVA', 'IVB']) | |
response = st.selectbox('Response', ['Excellent', 'Indeterminate', 'Biochemical Incomplete', 'Structural Incomplete']) | |
# Giriş verilerini bir dataframe'e dönüştür | |
input_data = pd.DataFrame({ | |
'Age': [age], | |
'Gender': [gender], | |
'Smoking': [smoking], | |
'Hx Smoking': [hx_smoking], | |
'Hx Radiothreapy': [hx_radiotherapy], | |
'Thyroid Function': [thyroid_function], | |
'Physical Examination': [physical_examination], | |
'Adenopathy': [adenopathy], | |
'Pathology': [pathology], | |
'Focality': [focality], | |
'Risk': [risk], | |
'T': [t], | |
'N': [n], | |
'M': [m], | |
'Stage': [stage], | |
'Response': [response] | |
}) | |
# Tahmin yap butonu | |
if st.button('Predict Recurrence'): | |
prediction = model.predict(input_data) | |
result = 'likely to recur' if prediction[0] == 1 else 'not likely to recur' | |
st.write(f'The model predicts that the cancer is {result}.') | |