I am getting this error"AttributeError: Can't get attribute '_RemainderColsList' on <module 'sklearn.compose._column_transformer' from 'C:\Users\USER\kenyan-crop-yield-prediction\venv\Lib\site-packages\sklearn\compose\_column_transformer.py'>" when trying to test my app with Streamlit Cloud. I have attempted to upgrade sklearn, even setting a reminder, but the error persists. here is some part of the code

Step 0: Imports

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os

from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import ElasticNet
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import r2_score, mean_absolute_error, root_mean_squared_error, make_scorer
from sklearn.inspection import permutation_importance
import joblib

import warnings
warnings.filterwarnings("ignore")

Numeric transformer: imputation and scaling

numeric_transformer = Pipeline(steps=[
("imputer", SimpleImputer(strategy="median")),
("scaler", StandardScaler())
])

Categorical transformer: imputation and one-hot encoding

categorical_transformer = Pipeline(steps=[
("imputer", SimpleImputer(strategy="constant", fill_value="missing")),
("onehot", OneHotEncoder(handle_unknown="ignore"))
])

Combine preprocessing

preprocessor = ColumnTransformer(
transformers=[
("num", numeric_transformer, num_features),
("cat", categorical_transformer, cat_features)
],
remainder="drop"
)

Define Models

model_rf = RandomForestRegressor(n_estimators=250, random_state=42, n_jobs=-1)
model_enet = ElasticNet(alpha=0.001, l1_ratio=0.1, random_state=42)

Pipelines

pipe_rf = Pipeline(steps=[
("preprocessor", preprocessor),
("model", model_rf)
])

pipe_enet = Pipeline(steps=[
("preprocessor", preprocessor),
("model", model_enet)
])