trial

app.py

@@ -40,7 +40,7 @@ result = prediction()
 st.write(result)
 
 # download the result
-csv = convert_df(result)
-if st.download_button('Download Prediction', csv, 'prediction.csv'):
-   st.write('Thanks for downloading!')
+# csv = convert_df(result)
+# if st.download_button('Download Prediction', csv, 'prediction.csv'):
+#    st.write('Thanks for downloading!')
 

logs.log

@@ -211,3 +211,30 @@ Feature names unseen at fit time:
 2023-08-04 07:23:22,157:INFO:Preloading libraries
 2023-08-04 07:23:22,159:INFO:Set up data.
 2023-08-04 07:23:22,167:INFO:Set up index.
+2023-08-04 07:30:25,503:WARNING:
+'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
+2023-08-04 07:30:25,503:WARNING:
+'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
+2023-08-04 07:30:25,503:WARNING:
+'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
+2023-08-04 07:30:25,503:WARNING:
+'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
+2023-08-04 07:30:33,393:INFO:Initializing predict_model()
+2023-08-04 07:30:33,393:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x2847f71c0>, 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 0x2847c2550>)
+2023-08-04 07:30:33,393:INFO:Checking exceptions
+2023-08-04 07:30:33,393:INFO:Preloading libraries
+2023-08-04 07:30:33,395:INFO:Set up data.
+2023-08-04 07:30:33,403:INFO:Set up index.