Calling predict on an example from an already trained logistic regression model

I trained a logistic regression model for multi classification on text data. I wanted to generate a sample prediction from the model but I am getting this error

ValueError: X has 30 features per sample; expecting 100000  

Here is the code that vectorizes the text data

tfidf_pipeline = Pipeline([      ('tfidf' ,TfidfVectorizer(max_features=50000, ngram_range=(1, 3), stop_words = 'english', strip_accents= 'ascii',))])    preprocessor_pipeline = ColumnTransformer(      transformers=[      ('short_description', tfidf_pipeline,'short_description'),      ('details', tfidf_pipeline,'details'),  ])  

Here is the code I am trying to run but getting the latter above error

d = {'short_description' : ['[mitigated]  [ubl5] ssd slam station not working'],      'details' : ['ssd slam station not working, unable to  take slam from the station.']}  df_test = pd.DataFrame(data=d)  X = df_test[['short_description', 'details']]  X_prep = preprocessor_pipeline.fit_transform(X)  y_p = lr.predict(X_prep)  

https://stackoverflow.com/questions/67223461/calling-predict-on-an-example-from-an-already-trained-logistic-regression-model April 23, 2021 at 11:08AM