update

app.py

@@ -34,7 +34,7 @@ def run():
       st.write(prediction)
 
       # download the result
-      csv = convert_df(result)
+      csv = convert_df(prediction)
       if st.download_button('Download Prediction', csv, 'prediction.csv'):
          st.write('Thanks for downloading!')
 

logs.log

@@ -284,3 +284,57 @@ Feature names unseen at fit time:
 2023-08-08 22:27:05,992:INFO:Preloading libraries
 2023-08-08 22:27:05,992:INFO:Set up data.
 2023-08-08 22:27:05,998:INFO:Set up index.
+2023-08-08 22:45:35,556:WARNING:
+'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
+2023-08-08 22:45:35,556:WARNING:
+'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
+2023-08-08 22:45:35,556:WARNING:
+'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
+2023-08-08 22:45:35,556:WARNING:
+'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
+2023-08-08 22:45:36,129:INFO:Initializing load_model()
+2023-08-08 22:45:36,129:INFO:load_model(model_name=model, platform=None, authentication=None, verbose=True)
+2023-08-08 22:45:39,904:INFO:Initializing load_model()
+2023-08-08 22:45:39,905:INFO:load_model(model_name=model, platform=None, authentication=None, verbose=True)
+2023-08-08 22:45:39,951:INFO:Initializing predict_model()
+2023-08-08 22:45:39,951:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x281b923d0>, estimator=Pipeline(memory=FastMemory(location=/var/folders/vh/81ldn_315vdf1b2_lnntkb080000gn/T/joblib),
+         steps=[('combine',
+                 TransformerWrapper(transformer=TransformerWrapper(include=['ed'],
+                                                                   transformer=Combine()))),
+                ('remove outlier',
+                 TransformerWrapper(transformer=TransformerWrapper(transformer=RemoveOutliers(random_state=42)))),
+                ('normalize',
+                 TransformerWrapper(transformer=TransformerWrapper(exclude=['ed',
+                                                                            'age'],
+                                                                   transformer=RobustScaler()))),
+                ('actual_estimator',
+                 LogisticRegression(C=8.956999999999999,
+                                    class_weight='balanced', max_iter=1000,
+                                    random_state=42))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x281bb7f70>)
+2023-08-08 22:45:39,952:INFO:Checking exceptions
+2023-08-08 22:45:39,952:INFO:Preloading libraries
+2023-08-08 22:45:39,954:INFO:Set up data.
+2023-08-08 22:45:39,960:INFO:Set up index.
+2023-08-08 22:46:02,637:INFO:Initializing load_model()
+2023-08-08 22:46:02,638:INFO:load_model(model_name=model, platform=None, authentication=None, verbose=True)
+2023-08-08 22:46:05,250:INFO:Initializing load_model()
+2023-08-08 22:46:05,250:INFO:load_model(model_name=model, platform=None, authentication=None, verbose=True)
+2023-08-08 22:46:05,281:INFO:Initializing predict_model()
+2023-08-08 22:46:05,281:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x133e7d250>, estimator=Pipeline(memory=FastMemory(location=/var/folders/vh/81ldn_315vdf1b2_lnntkb080000gn/T/joblib),
+         steps=[('combine',
+                 TransformerWrapper(transformer=TransformerWrapper(include=['ed'],
+                                                                   transformer=Combine()))),
+                ('remove outlier',
+                 TransformerWrapper(transformer=TransformerWrapper(transformer=RemoveOutliers(random_state=42)))),
+                ('normalize',
+                 TransformerWrapper(transformer=TransformerWrapper(exclude=['ed',
+                                                                            'age'],
+                                                                   transformer=RobustScaler()))),
+                ('actual_estimator',
+                 LogisticRegression(C=8.956999999999999,
+                                    class_weight='balanced', max_iter=1000,
+                                    random_state=42))]), probability_threshold=None, encoded_labels=False, raw_score=False, round=4, verbose=True, ml_usecase=None, preprocess=True, encode_labels=<function _SupervisedExperiment.predict_model.<locals>.encode_labels at 0x133e57ca0>)
+2023-08-08 22:46:05,281:INFO:Checking exceptions
+2023-08-08 22:46:05,281:INFO:Preloading libraries
+2023-08-08 22:46:05,282:INFO:Set up data.
+2023-08-08 22:46:05,286:INFO:Set up index.