repair

app.py

@@ -1,13 +1,11 @@
-import os, stat
+import os
 import streamlit as st
 import pandas as pd
 from pycaret.classification import *
 
-os.environ['TRANSFORMERS_CACHE'] = '/home/user/'
 os.environ['MLFLOW_TRACKING_USERNAME'] = 'fandanabil1379'
 os.environ['MLFLOW_TRACKING_PASSWORD'] = 'dadc32f6246f307c2fe4928f3074068f628b79ba'
 
-@st.cache_data
 def convert_df(df):
    return df.to_csv(index=False).encode('utf-8')
 
@@ -30,12 +28,7 @@ if uploaded_file is not None:
    # do prediction
    df = pd.read_csv(uploaded_file)
    model = load_model()
-
-   try:
-      prediction = predict_model(model, df).drop(columns='Unnamed: 0')
-   except:
-      os.chmod("/home/user/app/", stat.S_IRWXO)
-      prediction = predict_model(model, df).drop(columns='Unnamed: 0')
+   prediction = predict_model(model, df).drop(columns='Unnamed: 0')
 
    # show the result
    st.write(prediction)

logs.log

@@ -51,3 +51,68 @@ Feature names unseen at fit time:
 2023-08-02 14:14:55,425:INFO:Preloading libraries
 2023-08-02 14:14:55,425:INFO:Set up data.
 2023-08-02 14:14:55,430:INFO:Set up index.
+2023-08-03 07:20:31,034:WARNING:
+'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
+2023-08-03 07:20:31,035:WARNING:
+'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
+2023-08-03 07:20:31,035:WARNING:
+'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
+2023-08-03 07:20:31,035:WARNING:
+'cuml' is a soft dependency and not included in the pycaret installation. Please run: `pip install cuml` to install.
+2023-08-03 07:20:41,626:INFO:Initializing predict_model()
+2023-08-03 07:20:41,626:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x16da13e20>, 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 0x17e0d9160>)
+2023-08-03 07:20:41,626:INFO:Checking exceptions
+2023-08-03 07:20:41,626:INFO:Preloading libraries
+2023-08-03 07:20:41,629:INFO:Set up data.
+2023-08-03 07:20:41,637:INFO:Set up index.
+2023-08-03 07:21:17,448:INFO:Initializing predict_model()
+2023-08-03 07:21:17,449:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x16dab19d0>, 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 0x17e0d9dc0>)
+2023-08-03 07:21:17,449:INFO:Checking exceptions
+2023-08-03 07:21:17,449:INFO:Preloading libraries
+2023-08-03 07:21:17,449:INFO:Set up data.
+2023-08-03 07:21:17,455:INFO:Set up index.
+2023-08-03 07:22:14,797:INFO:Initializing predict_model()
+2023-08-03 07:22:14,797:INFO:predict_model(self=<pycaret.classification.oop.ClassificationExperiment object at 0x16da4d670>, 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 0x16e275a60>)
+2023-08-03 07:22:14,797:INFO:Checking exceptions
+2023-08-03 07:22:14,797:INFO:Preloading libraries
+2023-08-03 07:22:14,798:INFO:Set up data.
+2023-08-03 07:22:14,801:INFO:Set up index.