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app.py

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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}.')

requirements.txt

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+streamlit
+joblib
+scikit-learn
+pandas

thyroid_cancer_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fc40b8b8cb4350f247eb66ac812dba01ed162d9019c2fee420c9b0ec4c182af0
+size 9150