Upload app.py

app.py

@@ -9,7 +9,7 @@ from math import sqrt
 from scipy import stats as st
 from matplotlib import pyplot as plt
 
-from sklearn.linear_model import LogisticRegression
+from sklearn.calibration import CalibratedClassifierCV
 
 import shap
 import gradio as gr
@@ -21,24 +21,39 @@ from datasets import load_dataset
 
 #Read data training data.
 
-x1 = pd.read_csv("6m_data_train.csv", index_col = 0, low_memory = False)
+x1 = load_dataset("mertkarabacak/NCDB-GBM", data_files="6m_data_train.csv", use_auth_token = HF_TOKEN)
+x1 = pd.DataFrame(x1['train'])
+x1 = x1.iloc[:, 1:]
 
-x2 = pd.read_csv("12m_data_train.csv", index_col = 0, low_memory = False)
+x2 = load_dataset("mertkarabacak/NCDB-GBM", data_files="12m_data_train.csv", use_auth_token = HF_TOKEN)
+x2 = pd.DataFrame(x2['train'])
+x2 = x2.iloc[:, 1:]
 
-x3 = pd.read_csv("24m_data_train.csv", index_col = 0, low_memory = False)
-
-x4 = pd.read_csv("36m_data_train.csv", index_col = 0, low_memory = False)
+x3 = load_dataset("mertkarabacak/NCDB-GBM", data_files="18m_data_train.csv", use_auth_token = HF_TOKEN)
+x3 = pd.DataFrame(x3['train'])
+x3 = x3.iloc[:, 1:]
 
+x4 = load_dataset("mertkarabacak/NCDB-GBM", data_files="24m_data_train.csv", use_auth_token = HF_TOKEN)
+x4 = pd.DataFrame(x4['train'])
+x4 = x4.iloc[:, 1:]
 
 #Read validation data.
 
-x1_valid = pd.read_csv("6m_data_valid.csv", index_col = 0, low_memory = False)
+x1_valid = load_dataset("mertkarabacak/NCDB-GBM", data_files="6m_data_valid.csv", use_auth_token = HF_TOKEN)
+x1_valid = pd.DataFrame(x1_valid['train'])
+x1_valid = x1_valid.iloc[:, 1:]
 
-x2_valid = pd.read_csv("12m_data_valid.csv", index_col = 0, low_memory = False)
+x2_valid = load_dataset("mertkarabacak/NCDB-GBM", data_files="12m_data_valid.csv", use_auth_token = HF_TOKEN)
+x2_valid = pd.DataFrame(x2_valid['train'])
+x2_valid = x2_valid.iloc[:, 1:]
 
-x3_valid = pd.read_csv("24m_data_valid.csv", index_col = 0, low_memory = False)
+x3_valid = load_dataset("mertkarabacak/NCDB-GBM", data_files="18m_data_valid.csv", use_auth_token = HF_TOKEN)
+x3_valid = pd.DataFrame(x3_valid['train'])
+x3_valid = x3_valid.iloc[:, 1:]
 
-x4_valid = pd.read_csv("36m_data_valid.csv", index_col = 0, low_memory = False)
+x4_valid = load_dataset("mertkarabacak/NCDB-GBM", data_files="24m_data_valid.csv", use_auth_token = HF_TOKEN)
+x4_valid = pd.DataFrame(x4_valid['train'])
+x4_valid = x4_valid.iloc[:, 1:]
 
 
 #Define feature names.
@@ -59,82 +74,67 @@ f4_names = [f4.replace('__', ' - ') for f4 in f4_names]
 f4_names = [f4.replace('_', ' ') for f4 in f4_names]
 
 
-#Prepare training data for the outcome 1 (prolonged LOS).
+#Prepare training data for the outcome 1.
 y1 = x1.pop('OUTCOME')
 
-#Prepare validation data for the outcome 1 (prolonged LOS).
-y1_valid = x1_valid.pop('OUTCOME')
-
-#Prepare training data for the outcome 2 (non-home discharges).
+#Prepare training data for the outcome 2.
 y2 = x2.pop('OUTCOME')
 
-#Prepare validation data for the outcome 2 (non-home discharges).
-y2_valid = x2_valid.pop('OUTCOME')
-
-#Prepare training data for the outcome 3 (30-day readmissions).
+#Prepare training data for the outcome 3.
 y3 = x3.pop('OUTCOME')
 
-#Prepare validation data for the outcome 3 (30-day readmissions).
-y3_valid = x3_valid.pop('OUTCOME')
-
-#Prepare training data for the outcome 4 (unplanned reoperations).
+#Prepare training data for the outcome 3.
 y4 = x4.pop('OUTCOME')
 
-#Prepare validation data for the outcome 4 (unplanned reoperations).
-y4_valid = x4_valid.pop('OUTCOME')
-
 
-#Assign hyperparameters.
+#Training models.
 
-y1_params =  {'objective': 'binary', 'boosting_type': 'gbdt', 'lambda_l1': 2.874728678068222e-05, 'lambda_l2': 0.002100238688192627, 'num_leaves': 39, 'feature_fraction': 0.4504130718946593, 'bagging_fraction': 0.8916461477863318, 'bagging_freq': 7, 'min_child_samples': 45, 'metric': 'binary_logloss', 'verbosity': -1, 'random_state': 31}
-y2_params =  {'objective': 'binary', 'boosting_type': 'gbdt', 'lambda_l1': 0.0002837317278662907, 'lambda_l2': 5.412618023120056e-06, 'num_leaves': 78, 'feature_fraction': 0.4044321534682025, 'bagging_fraction': 0.747678020066352, 'bagging_freq': 6, 'min_child_samples': 44, 'metric': 'binary_logloss', 'verbosity': -1, 'random_state': 31}
-y3_params =  {'objective': 'binary', 'boosting_type': 'gbdt', 'lambda_l1': 0.00016354134178989566, 'lambda_l2': 0.005110516449291205, 'num_leaves': 4, 'feature_fraction': 0.525789668995701, 'bagging_fraction': 0.4203858842031528, 'bagging_freq': 3, 'min_child_samples': 66, 'metric': 'binary_logloss', 'verbosity': -1, 'random_state': 31}
-y4_params =  {'objective': 'binary', 'boosting_type': 'gbdt', 'lambda_l1': 0.00014329772210712767, 'lambda_l2': 0.001638738946438707, 'num_leaves': 2, 'feature_fraction': 0.565882308738563, 'bagging_fraction': 0.47701769327658605, 'bagging_freq': 5, 'min_child_samples': 59, 'metric': 'binary_logloss', 'verbosity': -1, 'random_state': 31}
+from tabpfn import TabPFNClassifier
+tabpfn = TabPFNClassifier(device='cuda', N_ensemble_configurations=1)
 
-#Training models.
+y1_model = tabpfn
+y1_model = y1_model.fit(x1, y1, overwrite_warning=True)
 
-from lightgbm import LGBMClassifier
-lgb = LGBMClassifier(**y1_params)
-y1_model = lgb
+y1_calib_model = CalibratedClassifierCV(y1_model, method='sigmoid', cv='prefit')
+y1_calib_model = y1_calib_model.fit(x1, y1)
 
-y1_model = y1_model.fit(x1, y1)
 y1_explainer = shap.Explainer(y1_model.predict, x1)
-y1_calib_probs = y1_model.predict_proba(x1_valid)
-y1_calib_model = LogisticRegression()
-y1_calib_model = y1_calib_model.fit(y1_calib_probs, y1_valid)
 
 
-from lightgbm import LGBMClassifier
-lgb = LGBMClassifier(**y2_params)
-y2_model = lgb
+from tabpfn import TabPFNClassifier
+tabpfn = TabPFNClassifier(device='cuda', N_ensemble_configurations=1)
+
+y2_model = tabpfn
+y2_model = y2_model.fit(x2, y2, overwrite_warning=True)
+
+y2_calib_model = CalibratedClassifierCV(y2_model, method='sigmoid', cv='prefit')
+y2_calib_model = y2_calib_model.fit(x2, y2)
 
-y2_model = y2_model.fit(x2, y2)
 y2_explainer = shap.Explainer(y2_model.predict, x2)
-y2_calib_probs = y2_model.predict_proba(x2_valid)
-y2_calib_model = LogisticRegression()
-y2_calib_model = y2_calib_model.fit(y2_calib_probs, y2_valid)
 
 
-from lightgbm import LGBMClassifier
-lgb = LGBMClassifier(**y3_params)
-y3_model = lgb
+from tabpfn import TabPFNClassifier
+tabpfn = TabPFNClassifier(device='cuda', N_ensemble_configurations=1)
+
+y3_model = tabpfn
+y3_model = y3_model.fit(x3, y3, overwrite_warning=True)
+
+y3_calib_model = CalibratedClassifierCV(y3_model, method='sigmoid', cv='prefit')
+y3_calib_model = y3_calib_model.fit(x3, y3)
 
-y3_model = y3_model.fit(x3, y3)
 y3_explainer = shap.Explainer(y3_model.predict, x3)
-y3_calib_probs = y3_model.predict_proba(x3_valid)
-y3_calib_model = LogisticRegression()
-y3_calib_model = y3_calib_model.fit(y3_calib_probs, y3_valid)
 
 
-from lightgbm import LGBMClassifier
-lgb = LGBMClassifier(**y4_params)
-y4_model = lgb
+from tabpfn import TabPFNClassifier
+tabpfn = TabPFNClassifier(device='cuda', N_ensemble_configurations=1)
+
+y4_model = tabpfn
+y4_model = y4_model.fit(x4, y4, overwrite_warning=True)
+
+y4_calib_model = CalibratedClassifierCV(y4_model, method='sigmoid', cv='prefit')
+y4_calib_model = y4_calib_model.fit(x4, y4)
 
-y4_model = y4_model.fit(x4, y4)
 y4_explainer = shap.Explainer(y4_model.predict, x4)
-y4_calib_probs = y4_model.predict_proba(x4_valid)
-y4_calib_model = LogisticRegression()
-y4_calib_model = y4_calib_model.fit(y4_calib_probs, y4_valid)
 
 
 output_y1 = (
@@ -158,7 +158,7 @@ output_y2 = (
 output_y3 = (
     """          
         <br/>        
-        <center>The probability of 24-month survival:</center>
+        <center>The probability of 18-month survival:</center>
         <br/>        
         <center><h1>{:.2f}%</h1></center>
 """
@@ -167,7 +167,7 @@ output_y3 = (
 output_y4 = (
     """          
         <br/>        
-        <center>The probability of 36-month survival:</center>
+        <center>The probability of 24-month survival:</center>
         <br/>        
         <center><h1>{:.2f}%</h1></center>
 """
@@ -177,8 +177,7 @@ output_y4 = (
 #Define predict for y1.
 def y1_predict(*args):
     df1 = pd.DataFrame([args], columns=x1.columns)
-    pos_pred = y1_model.predict_proba(df1)
-    pos_pred = y1_calib_model.predict_proba(pos_pred)
+    pos_pred = y1_calib_model.predict_proba(df1)
     prob = pos_pred[0][1]
     prob = 1-prob
     output = output_y1.format(prob * 100)
@@ -187,8 +186,7 @@ def y1_predict(*args):
 #Define predict for y2.
 def y2_predict(*args):
     df2 = pd.DataFrame([args], columns=x2.columns)
-    pos_pred = y2_model.predict_proba(df2)
-    pos_pred = y2_calib_model.predict_proba(pos_pred)        
+    pos_pred = y2_calib_model.predict_proba(df2)
     prob = pos_pred[0][1]
     prob = 1-prob
     output = output_y2.format(prob * 100)
@@ -197,8 +195,7 @@ def y2_predict(*args):
 #Define predict for y3.
 def y3_predict(*args):
     df3 = pd.DataFrame([args], columns=x3.columns)
-    pos_pred = y3_model.predict_proba(df3)
-    pos_pred = y3_calib_model.predict_proba(pos_pred)            
+    pos_pred = y3_calib_model.predict_proba(df3)
     prob = pos_pred[0][1]
     prob = 1-prob    
     output = output_y3.format(prob * 100)
@@ -207,10 +204,9 @@ def y3_predict(*args):
 #Define predict for y4.
 def y4_predict(*args):
     df4 = pd.DataFrame([args], columns=x4.columns)
-    pos_pred = y4_model.predict_proba(df4)
-    pos_pred = y4_calib_model.predict_proba(pos_pred)            
+    pos_pred = y4_calib_model.predict_proba(df4)
     prob = pos_pred[0][1]
-    prob = 1-prob
+    prob = 1-prob    
     output = output_y4.format(prob * 100)
     return output
 
@@ -297,14 +293,14 @@ def y4_interpret(*args):
     return fig
 
 
-with gr.Blocks(title = "NCDB-GBM") as demo:
+with gr.Blocks(title = "NCDB-Meningioma") as demo:
         
     gr.Markdown(
         """
     <br/>
     <center><h2>NOT FOR CLINICAL USE</h2><center>    
     <br/>    
-    <center><h1>GBM Survival Outcomes</h1></center>
+    <center><h1>IDH-wt Glioblastoma Survival Outcomes</h1></center>
     <center><h2>Prediction Tool</h2></center>
     <br/>
     <center><h3>This web application should not be used to guide any clinical decisions.</h3><center>
@@ -330,44 +326,44 @@ with gr.Blocks(title = "NCDB-GBM") as demo:
           </tr>
           <tr>
             <td>6-Month Mortality</td>
-            <td>LightGBM</td>
-            <td>0.694 (0.686 - 0.702)</td>
-            <td>0.810 (0.803 - 0.817)</td>
-            <td>0.772 (0.765 - 0.779)</td>
-            <td>0.719 (0.711 - 0.727)</td>
-            <td>0.831 (0.824 - 0.838)</td>
-            <td>0.152 (0.146 - 0.158)</td>             
+            <td>TabPFN</td>
+            <td>0.755 (0.733 - 0.777)</td>
+            <td>0.767 (0.745 - 0.789)</td>
+            <td>0.764 (0.742 - 0.786)</td>
+            <td>0.654 (0.630 - 0.678)</td>
+            <td>0.840 (0.811 - 0.857)</td>
+            <td>0.135 (0.117 - 0.153)</td>             
           </tr>
           <tr>
             <td>12-Month Mortality</td>
-            <td>LightGBM</td>
-            <td>0.700 (0.692 - 0.708)</td>
-            <td>0.742 (0.735 - 0.749)</td>
-            <td>0.720 (0.712 - 0.728)</td>
-            <td>0.821 (0.815 - 0.827)</td>
-            <td>0.808 (0.792 - 0.807)</td>
-            <td>0.183 (0.176 - 0.190)</td>             
+            <td>TabPFN</td>
+            <td>0.685 (0.661 - 0.709)</td>
+            <td>0.728 (0.705 - 0.751)</td>
+            <td>0.707 (0.683 - 0.731)</td>
+            <td>0.746 (0.723 - 0.769)</td>
+            <td>0.783 (0.752 - 0.800)</td>
+            <td>0.203 (0.182 - 0.224)</td>             
           </tr>
           <tr>
-            <td>24-Month Mortality</td>
-            <td>LightGBM</td>
-            <td>0.742 (0.735 - 0.749)</td>
-            <td>0.555 (0.547 - 0.563)</td>
-            <td>0.702 (0.694 - 0.710)</td>
-            <td>0.897 (0.892 - 0.902)</td>
-            <td>0.716 (0.706 - 0.727)</td>
-            <td>0.153 (0.147 - 0.159)</td>             
-          </tr>                        
+            <td>18-Month Mortality</td>
+            <td>TabPFN</td>
+            <td>0.706 (0.682 - 0.730)</td>
+            <td>0.659 (0.634 - 0.684)</td>
+            <td>0.689 (0.665 - 0.713)</td>
+            <td>0.832 (0.812 - 0.852)</td>
+            <td>0.749 (0.717 - 0.768)</td>
+            <td>0.193 (0.172 - 0.214)</td>             
+          </tr>
           <tr>
-            <td>36-Month Mortality</td>
-            <td>LightGBM</td>
-            <td>0.705 (0.697 - 0.713)</td>
-            <td>0.576 (0.568 - 0.584)</td>
-            <td>0.689 (0.681 - 0.697)</td>
-            <td>0.937 (0.933 - 0.941)</td>
-            <td>0.707 (0.687 - 0.713)</td>
-            <td>0.103 (0.098 - 0.108)</td>             
-          </tr>        
+            <td>24-Month Mortality</td>
+            <td>TabPFN</td>
+            <td>0.732 (0.708 - 0.756)</td>
+            <td>0.716 (0.691 - 0.741)</td>
+            <td>0.728 (0.704 - 0.752)</td>
+            <td>0.925 (0.911 - 0.939)</td>
+            <td>0.780 (0.755 - 0.813)</td>
+            <td>0.141 (0.122 - 0.160)</td>             
+          </tr>
         </table>
         </div>
         """
@@ -381,45 +377,29 @@ with gr.Blocks(title = "NCDB-GBM") as demo:
 
             Sex = gr.Dropdown(label = "Sex", choices = ['Male', 'Female'], type = 'index', value = 'Male')
             
-            Race = gr.Dropdown(label = "Race", choices = ['White', 'Black', 'Asian Indian or Pakistani', 'Chinese', 'Filipino', 'American Indian, Aleutian, or Eskimo', 'Vietnamese', 'Korean', 'Other or Unknown'], type = 'index', value = 'White')
+            Race = gr.Dropdown(label = "Race", choices = ['White', 'Black', 'Other'], type = 'index', value = 'White')
 
             Hispanic_Ethnicity = gr.Dropdown(label = "Hispanic Ethnicity", choices = ['No', 'Yes', 'Unknown'], type = 'index', value = 'No')
             
-            Primary_Payor = gr.Dropdown(label = "Primary Payor", choices = ['Private insurance', 'Medicare', 'Medicaid', 'Other government', 'Not insured', 'Unknown'], type = 'index', value = 'Private insurance')
+            Insurance_Status = gr.Dropdown(label = "Insurance Status", choices = ['Private insurance', 'Medicare', 'Medicaid', 'Other government', 'Not insured', 'Unknown'], type = 'index', value = 'Private insurance')
             
-            Facility_Type = gr.Dropdown(label = "Facility Type", choices = ['Academic/Research Program', 'Comprehensive Community Cancer Program', 'Integrated Network Cancer Program', 'Community Cancer Program', 'Other or Unknown'], type = 'index', value = 'Academic/Research Program')
+            Facility_Type = gr.Dropdown(label = "Facility Type", choices = ['Academic/Research Program', 'Community Cancer Program', 'Integrated Network Cancer Program'], type = 'index', value = 'Academic/Research Program')
             
-            Facility_Location = gr.Dropdown(label = "Facility Location", choices = ['South Atlantic', 'East North Central', 'Middle Atlantic', 'East North Central', 'Middle Atlantic', 'Pacific', 'West South Central', 'West North Central', 'East South Central', 'New England', 'Mountain', 'Unknown or Other'], type = 'index', value = 'South Atlantic')
+            Facility_Location = gr.Dropdown(label = "Facility Location", choices = ['Central', 'Atlantic', 'Pacific', 'Mountain', 'New England'], type = 'index', value = 'Central')
 
-            CharlsonDeyo_Score = gr.Dropdown(label = "Charlson-Deyo Score", choices = ['0', '1', '2', 'Greater than 3'], type = 'index', value = '0')
-            
-            Karnofsky_Performance_Scale = gr.Dropdown(label = "Karnofsky Performance Scale", choices = ['KPS 0-20', 'KPS 21-40', 'KPS 41-60', 'KPS 61-80', 'KPS 81-100', 'Unknown'], type = 'index', value = 'KPS 81-100')
-            
-            Laterality = gr.Dropdown(label = "Laterality", choices = ['Right', 'Left', 'Bilateral', 'Midline', 'Unknown'], type = 'index', value = 'Right')
-            
-            Tumor_Localization = gr.Dropdown(label = "Tumor Localization", choices = ['Frontal lobe', 'Temporal lobe', 'Parietal lobe', 'Occipital lobe', 'Overlapping', 'Intraventricular', 'Cerebellum', 'Brain stem', 'Unknown'], type = 'index', value = 'Frontal lobe')
-            
-            Focality = gr.Dropdown(label = "Focality", choices = ['Unifocal', 'Multifocal', 'Unknown'], type = 'index', value = 'Unifocal')
-            
-            Diagnostic_Biopsy = gr.Dropdown(label = "Diagnostic Biopsy", choices = ['No', 'Yes', 'Unknown'], type = 'index', value = 'No')
-            
-            Tumor_Size = gr.Dropdown(label = "Tumor Size", choices = ['< 2 cm', '2 - 3.9 cm', '4 - 5.9 cm', '6 - 7.9 cm', '8 - 9.9 cm', '10 - 11.9 cm', '12 - 13.9 cm', '14 - 15.9 cm', '16 - 17.9 cm', '18 - 19.9 cm', '> 20 cm', 'Unknown'], type = 'index', value = '< 2 cm')
+            CharlsonDeyo_Score = gr.Dropdown(label = "Charlson-Deyo Score", choices = ['0', '1', '>2'], type = 'index', value = '0')
             
-            CoDeletion_1p19q = gr.Dropdown(label = "1p19q Co-Deletion", choices = ['No', 'Yes', 'Unknown'], type = 'index', value = 'No')
+            MGMT_Methylation = gr.Dropdown(label = "MGMT Methylation", choices = ['Unmethylated', 'Methylated'], type = 'index', value = 'Unmethylated')
             
-            MGMT_Methylation = gr.Dropdown(label = "MGMT Methylation", choices = ['Unmethylated', 'Methylated', 'Unknown'], type = 'index', value = 'Unmethylated')
-                        
-            Ki67_Labeling_Index = gr.Dropdown(label = 'Ki-67 Labeling Index', choices = ['0-20%', '21-40%', '41-60%', '61-80%', '81-100%', 'Normal (no percentage available)', 'Slightly elevated (no percentage available)', 'Elevated (no percentage available)', 'Unknown'], type = 'index', value = '0-20%')
+            Tumor_Size = gr.Dropdown(label = "Tumor Size (mm)", minimum = 1, maximum = 300, step = 1, value = 30)
             
-            Resective_Surgery = gr.Dropdown(label = "Resective Surgery", choices = ['No', 'Yes', 'Unknown'], type = 'index', value = 'Yes')
+            Extent_of_Resection = gr.Dropdown(label = 'Extent of Resection', choices = ['No resective surgery was performed', 'Gross total resection'], type = 'index', value = 'Gross total resection')
             
-            Extent_of_Resection = gr.Dropdown(label = "Extent of Resection", choices = ['No resective surgery was performed', 'Gross total resection', 'Subtotal resection', 'Unknown'], type = 'index', value = 'Gross total resection')
+            Radiotherapy = gr.Dropdown(label = 'Radiotherapy', choices = ['No', 'Yes'], type = 'index', value = 'Yes')
             
-            Radiation_Treatment = gr.Dropdown(label = "Radiation Treatment", choices = ['No', 'Yes', 'Unknown'], type = 'index', value = 'Yes')
+            Chemotherapy = gr.Dropdown(label = "Chemotherapy", choices = ['No', 'Yes'], type = 'index', value = 'Yes')
             
-            Chemotherapy = gr.Dropdown(label = "Chemotherapy", choices = ['No', 'Yes (single-agent chemotherapy)', 'Yes (multi-agent chemotherapy)', 'Yes (details unknown)', 'Unknown'], type = 'index', value = 'No')
-
-            Immunotherapy = gr.Dropdown(label = "Immunotherapy", choices = ['No', 'Yes', 'Unknown'], type = 'index', value = 'No')
+            Immunotherapy = gr.Dropdown(label = "Immunotherapy", choices = ['No', 'Yes'], type = 'index', value = 'No')
             
         with gr.Column():
             
@@ -429,7 +409,7 @@ with gr.Blocks(title = "NCDB-GBM") as demo:
                     """
                     <center> <h2>6-Month Survival</h2> </center>
                     <br/>
-                    <center> This model uses the LightGBM algorithm.</center>
+                    <center> This model uses the Random Forest algorithm.</center>
                     <br/>
                     """
                     )
@@ -473,7 +453,7 @@ with gr.Blocks(title = "NCDB-GBM") as demo:
                     """
                     <center> <h2>12-Month Survival</h2> </center>
                     <br/>
-                    <center> This model uses the LightGBM algorithm.</center>
+                    <center> This model uses the Random Forest algorithm.</center>
                     <br/>
                     """
                     )
@@ -516,9 +496,9 @@ with gr.Blocks(title = "NCDB-GBM") as demo:
                 
                 gr.Markdown(
                     """
-                    <center> <h2>24-Month Survival</h2> </center>
+                    <center> <h2> 18-Month Survival</h2> </center>
                     <br/>
-                    <center> This model uses the LightGBM algorithm.</center>
+                    <center> This model uses the TabPFN algorithm.</center>
                     <br/>
                     """
                     )
@@ -561,9 +541,9 @@ with gr.Blocks(title = "NCDB-GBM") as demo:
                 
                 gr.Markdown(
                     """
-                    <center> <h2>36-Month Survival</h2> </center>
+                    <center> <h2> 24-Month Survival</h2> </center>
                     <br/>
-                    <center> This model uses the LightGBM algorithm.</center>
+                    <center> This model uses the TabPFN algorithm.</center>
                     <br/>
                     """
                     )
@@ -600,56 +580,55 @@ with gr.Blocks(title = "NCDB-GBM") as demo:
                     """
                     <br/>
                     """
-                    )  
-
+                    )                
            
                 y1_predict_btn.click(
                     y1_predict,
-                    inputs = [Facility_Type,Facility_Location,Age,Sex,Race,Hispanic_Ethnicity,Primary_Payor,CharlsonDeyo_Score,Tumor_Localization,Laterality,Diagnostic_Biopsy,Ki67_Labeling_Index,Karnofsky_Performance_Scale,MGMT_Methylation,Focality,Tumor_Size,Chemotherapy,Immunotherapy,CoDeletion_1p19q,Resective_Surgery,Extent_of_Resection,Radiation_Treatment],
+                    inputs = [Age, Sex, Race, Hispanic_Ethnicity, Insurance_Status, Facility_Type, Facility_Location, CharlsonDeyo_Score, Tumor_Size, MGMT_Methylation, Extent_of_Resection, Radiotherapy, Chemotherapy, Immunotherapy],
                     outputs = [label1]
                 )
 
                 y2_predict_btn.click(
                     y2_predict,
-                    inputs = [Facility_Type,Facility_Location,Age,Sex,Race,Hispanic_Ethnicity,Primary_Payor,CharlsonDeyo_Score,Tumor_Localization,Laterality,Diagnostic_Biopsy,Ki67_Labeling_Index,Karnofsky_Performance_Scale,MGMT_Methylation,Focality,Tumor_Size,Chemotherapy,Immunotherapy,CoDeletion_1p19q,Resective_Surgery,Extent_of_Resection,Radiation_Treatment],
+                    inputs = [Age, Sex, Race, Hispanic_Ethnicity, Insurance_Status, Facility_Type, Facility_Location, CharlsonDeyo_Score, Tumor_Size, MGMT_Methylation, Extent_of_Resection, Radiotherapy, Chemotherapy, Immunotherapy],
                     outputs = [label2]
                 )
                 
                 y3_predict_btn.click(
                     y3_predict,
-                    inputs = [Facility_Type,Facility_Location,Age,Sex,Race,Hispanic_Ethnicity,Primary_Payor,CharlsonDeyo_Score,Tumor_Localization,Laterality,Diagnostic_Biopsy,Ki67_Labeling_Index,Karnofsky_Performance_Scale,MGMT_Methylation,Focality,Tumor_Size,Chemotherapy,Immunotherapy,CoDeletion_1p19q,Resective_Surgery,Extent_of_Resection,Radiation_Treatment],
+                    inputs = [Age, Sex, Race, Hispanic_Ethnicity, Insurance_Status, Facility_Type, Facility_Location, CharlsonDeyo_Score, Tumor_Size, MGMT_Methylation, Extent_of_Resection, Radiotherapy, Chemotherapy, Immunotherapy],
                     outputs = [label3]
                 )
-
+                
                 y4_predict_btn.click(
                     y4_predict,
-                    inputs = [Facility_Type,Facility_Location,Age,Sex,Race,Hispanic_Ethnicity,Primary_Payor,CharlsonDeyo_Score,Tumor_Localization,Laterality,Diagnostic_Biopsy,Ki67_Labeling_Index,Karnofsky_Performance_Scale,MGMT_Methylation,Focality,Tumor_Size,Chemotherapy,Immunotherapy,CoDeletion_1p19q,Resective_Surgery,Extent_of_Resection,Radiation_Treatment],
+                    inputs = [Age, Sex, Race, Hispanic_Ethnicity, Insurance_Status, Facility_Type, Facility_Location, CharlsonDeyo_Score, Tumor_Size, MGMT_Methylation, Extent_of_Resection, Radiotherapy, Chemotherapy, Immunotherapy],
                     outputs = [label4]
-                )
+                )                
 
                 y1_interpret_btn.click(
                     y1_interpret,
-                    inputs = [Facility_Type,Facility_Location,Age,Sex,Race,Hispanic_Ethnicity,Primary_Payor,CharlsonDeyo_Score,Tumor_Localization,Laterality,Diagnostic_Biopsy,Ki67_Labeling_Index,Karnofsky_Performance_Scale,MGMT_Methylation,Focality,Tumor_Size,Chemotherapy,Immunotherapy,CoDeletion_1p19q,Resective_Surgery,Extent_of_Resection,Radiation_Treatment],
+                    inputs = [Age, Sex, Race, Hispanic_Ethnicity, Insurance_Status, Facility_Type, Facility_Location, CharlsonDeyo_Score, Tumor_Size, MGMT_Methylation, Extent_of_Resection, Radiotherapy, Chemotherapy, Immunotherapy],
                     outputs = [plot1],
                 )
                 
                 y2_interpret_btn.click(
                     y2_interpret,
-                    inputs = [Facility_Type,Facility_Location,Age,Sex,Race,Hispanic_Ethnicity,Primary_Payor,CharlsonDeyo_Score,Tumor_Localization,Laterality,Diagnostic_Biopsy,Ki67_Labeling_Index,Karnofsky_Performance_Scale,MGMT_Methylation,Focality,Tumor_Size,Chemotherapy,Immunotherapy,CoDeletion_1p19q,Resective_Surgery,Extent_of_Resection,Radiation_Treatment],
+                    inputs = [Age, Sex, Race, Hispanic_Ethnicity, Insurance_Status, Facility_Type, Facility_Location, CharlsonDeyo_Score, Tumor_Size, MGMT_Methylation, Extent_of_Resection, Radiotherapy, Chemotherapy, Immunotherapy],
                     outputs = [plot2],
                 )
 
                 y3_interpret_btn.click(
                     y3_interpret,
-                    inputs = [Facility_Type,Facility_Location,Age,Sex,Race,Hispanic_Ethnicity,Primary_Payor,CharlsonDeyo_Score,Tumor_Localization,Laterality,Diagnostic_Biopsy,Ki67_Labeling_Index,Karnofsky_Performance_Scale,MGMT_Methylation,Focality,Tumor_Size,Chemotherapy,Immunotherapy,CoDeletion_1p19q,Resective_Surgery,Extent_of_Resection,Radiation_Treatment],
+                    inputs = [Age, Sex, Race, Hispanic_Ethnicity, Insurance_Status, Facility_Type, Facility_Location, CharlsonDeyo_Score, Tumor_Size, MGMT_Methylation, Extent_of_Resection, Radiotherapy, Chemotherapy, Immunotherapy],
                   outputs = [plot3],
                 )
-
+                
                 y4_interpret_btn.click(
                     y4_interpret,
-                    inputs = [Facility_Type,Facility_Location,Age,Sex,Race,Hispanic_Ethnicity,Primary_Payor,CharlsonDeyo_Score,Tumor_Localization,Laterality,Diagnostic_Biopsy,Ki67_Labeling_Index,Karnofsky_Performance_Scale,MGMT_Methylation,Focality,Tumor_Size,Chemotherapy,Immunotherapy,CoDeletion_1p19q,Resective_Surgery,Extent_of_Resection,Radiation_Treatment],
+                    inputs = [Age, Sex, Race, Hispanic_Ethnicity, Insurance_Status, Facility_Type, Facility_Location, CharlsonDeyo_Score, Tumor_Size, MGMT_Methylation, Extent_of_Resection, Radiotherapy, Chemotherapy, Immunotherapy],
                   outputs = [plot4],
-                )
+                )                
                 
     gr.Markdown(
                 """