adding model2

app.py

@@ -1,61 +1,141 @@
+# import gradio as gr
+# import pickle
+# import pandas as pd 
+# import numpy as np
+# import lightgbm as lgb
+# from autogluon.tabular import TabularPredictor
+
+# loaded_model = pickle.load(open('Autogluon/models/XGBoost_BAG_L1/model.pkl', 'rb'))
+# # pred_proba_radio = loaded_model.predict_proba(X_test_radio)
+# # pred_proba_radio
+
+
+# def relapse(age, pathology, B_symptoms):
+    
+#     X_test_radio = pd.DataFrame.from_dict(
+#         {
+#             "Age": [age],
+#             "pathology_Nodular Sclerosis cHL": [1 if pathology == 'Nodular' else 0],
+#             "pathology_others": [1 if pathology == 'Others' else 0],
+#             "B_symptoms_Yes" : [1 if B_symptoms else 0],
+#             'radio_Yes': [1]
+#         }
+#     )
+    
+#     X_test_no_radio = pd.DataFrame.from_dict(
+#         {
+#             "Age": [age],
+#             "pathology_Nodular Sclerosis cHL": [1 if pathology == 'Nodular' else 0],
+#             "pathology_others": [1 if pathology == 'Others' else 0],
+#             "B_symptoms_Yes" : [1 if B_symptoms else 0],
+#             'radio_Yes': [0]
+#         }
+#     )    
+
+#     pred_proba_radio = loaded_model.predict_proba(X_test_radio)
+    
+#     pred_proba_radio =  round(np.ndarray.item(pred_proba_radio),2)
+
+#     pred_radio = loaded_model.predict(X_test_radio)
+    
+#     pred_proba_no_radio = loaded_model.predict_proba(X_test_no_radio)  
+    
+#     pred_proba_no_radio =  round(np.ndarray.item(pred_proba_no_radio),2)
+
+#     pred_no_radio = loaded_model.predict(X_test_no_radio)
+    
+#     return {"Radio": pred_proba_radio, "No Radio": pred_proba_no_radio}
+
+# iface = gr.Interface(
+    
+#     title = 'Should we omit radiotherapy?', 
+#                      description = 'This model predicts relapse according to risk factors.', 
+#                     fn=relapse, 
+#                      inputs= [
+#                          gr.Number(label = 'Age', show_label = True),
+#         gr.Radio( choices = ['Mixed cellularity', 'Nodular', 'Others'], label = 'Pathology', show_label = True),
+#                         gr.Checkbox(label = 'B_symptoms', show_label = True)],
+#     outputs = gr.Label(label = 'Has a higher probability of relapse', show_label = True)
+#     ,live = True,
+#     interpretation="default",
+# )
+
+# iface.launch()
+
+
+
+
+
+
 import gradio as gr
-import pickle
 import pandas as pd 
 import numpy as np
-import lightgbm as lgb
 from autogluon.tabular import TabularPredictor
 
-loaded_model = pickle.load(open('Autogluon/models/XGBoost_BAG_L1/model.pkl', 'rb'))
-# pred_proba_radio = loaded_model.predict_proba(X_test_radio)
-# pred_proba_radio
+predictor_1 = TabularPredictor.load("no_path_PET_1/")
+predictor_2 = TabularPredictor.load("no_path_PET_2/")
+predictor_3 = TabularPredictor.load("no_path_PET_3/")
+predictor_4 = TabularPredictor.load("no_path_PET_4/")
+predictor_5 = TabularPredictor.load("no_path_PET_5/")
+
+## Import mapping dataset
+
+stage_mapping = pd.read_csv('stage_mapping.csv', index_col = 0)
+
+# stage_mapping
 
+def get_encoding(stage):
+    return stage_mapping.loc[stage, 'stage_encoded']   # this is the dataframe for mappings that will be created from the training set
 
-def relapse(age, pathology, B_symptoms):
+def relapse(age_group, stage):
     
     X_test_radio = pd.DataFrame.from_dict(
         {
-            "Age": [age],
-            "pathology_Nodular Sclerosis cHL": [1 if pathology == 'Nodular' else 0],
-            "pathology_others": [1 if pathology == 'Others' else 0],
-            "B_symptoms_Yes" : [1 if B_symptoms else 0],
-            'radio_Yes': [1]
+            "age_group": ["<=15" if age_group == "<=15" else ">15"],
+            'radio': ["Yes"], 
+            'stage_encoded' :  get_encoding(stage)
+            
         }
     )
-    
+        
     X_test_no_radio = pd.DataFrame.from_dict(
         {
-            "Age": [age],
-            "pathology_Nodular Sclerosis cHL": [1 if pathology == 'Nodular' else 0],
-            "pathology_others": [1 if pathology == 'Others' else 0],
-            "B_symptoms_Yes" : [1 if B_symptoms else 0],
-            'radio_Yes': [0]
+            "age_group": ["<=15" if age_group == "Age <=15" else ">15"],
+            'radio': ["No"],
+            'stage_encoded' :  get_encoding(stage)
+
         }
     )    
-
-    pred_proba_radio = loaded_model.predict_proba(X_test_radio)
     
-    pred_proba_radio =  round(np.ndarray.item(pred_proba_radio),2)
+    rad_1 = float(round(predictor_1.predict_proba(X_test_radio).iloc[0,1],3))
+    rad_2 = float(round(predictor_2.predict_proba(X_test_radio).iloc[0,1],3))
+    rad_3 = float(round(predictor_3.predict_proba(X_test_radio).iloc[0,1],3))
+    rad_4 = float(round(predictor_4.predict_proba(X_test_radio).iloc[0,1],3))
+    
+    pred_proba_radio = float(round(np.mean([rad_1, rad_2, rad_3, rad_4]),3))
 
-    pred_radio = loaded_model.predict(X_test_radio)
+    no_rad1 = float(round(predictor_1.predict_proba(X_test_no_radio).iloc[0,1],3)) 
+    no_rad2 = float(round(predictor_2.predict_proba(X_test_no_radio).iloc[0,1],3)) 
+    no_rad3 = float(round(predictor_3.predict_proba(X_test_no_radio).iloc[0,1],3)) 
+    no_rad4 = float(round(predictor_4.predict_proba(X_test_no_radio).iloc[0,1],3)) 
     
-    pred_proba_no_radio = loaded_model.predict_proba(X_test_no_radio)  
     
-    pred_proba_no_radio =  round(np.ndarray.item(pred_proba_no_radio),2)
-
-    pred_no_radio = loaded_model.predict(X_test_no_radio)
+    pred_proba_no_radio = float(round(np.mean([no_rad1, no_rad2, no_rad3, no_rad4]),3))
+    
     
     return {"Radio": pred_proba_radio, "No Radio": pred_proba_no_radio}
 
+
 iface = gr.Interface(
     
     title = 'Should we omit radiotherapy?', 
                      description = 'This model predicts relapse according to risk factors.', 
                     fn=relapse, 
                      inputs= [
-                         gr.Number(label = 'Age', show_label = True),
-        gr.Radio( choices = ['Mixed cellularity', 'Nodular', 'Others'], label = 'Pathology', show_label = True),
-                        gr.Checkbox(label = 'B_symptoms', show_label = True)],
-    outputs = gr.Label(label = 'Has a higher probability of relapse', show_label = True)
+                     gr.Checkbox(label = 'Age <= 15', show_label = True),
+                     gr.Dropdown(choices = ['1A', '1B', '2A', '2B', '3A', '3B', '4A', '4B'], 
+                                    label = 'stage', show_label = True)                     ]
+    ,outputs = gr.Label(label = 'Has a higher probability of relapse', show_label = True)
     ,live = True,
     interpretation="default",
 )

no_path_PET_2/__version__

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