Hello, I am newer to streamlit, please forgive me this is also my first time posting on a technical forum, however this one is driving me crazy.
So created a streamlit app for League of Legends as a capstone for a bootcamp, I am using a MinMaxScaler on collected data from the Riot API, prior to predicting the outcome with a Decision Tree. In my local environment it works flawlessly (after many hours of pulling my hair out). However, once I deployed the code I began getting errors with my MinMaxScaler. This scaler was built and fit in a Jupyter Notebook and saved as a .gz utilizing the joblib package. I load this into my streamlit code utilizing joblib as well. Once my main function runs it throws an error for the scaling function:
AttributeError: 'MinMaxScaler' object has no attribute 'clip'
Traceback:
File "/usr/local/lib/python3.7/site-packages/streamlit/script_runner.py", line 332, in _run_script
exec(code, module.__dict__)
File "/app/league_capstone/Streamlit_App.py", line 23, in <module>
summoner_data_x, summoner_data_y, summoner_data_raw= full_process(user_input, scaler, games)
File "/app/league_capstone/Util.py", line 212, in full_process
team_data_x = scale_data(team_data_summoners, scaler, summonerName)
File "/app/league_capstone/Util.py", line 200, in scale_data
X_num_scaled = scaler.transform(X_num)
File "/home/appuser/.local/lib/python3.7/site-packages/sklearn/preprocessing/_data.py", line 439, in transform
if self.clip:
What I find strange is that this runs smoothly locally, but once it was deployed there are errors. Any help would be appreciated. I will leave the link to the app, and github repo below.
requirements.txt:
matplotlib==3.3.3
seaborn==0.11.0
plotly==4.14.1
pandas==1.1.5
xgboost==1.3.0.post0
riotwatcher==3.1.1
numpy==1.19.3
streamlit==0.73.0
joblib==1.0.0
imbalanced_learn==0.7.0
imblearn==0.0
scikit_learn==0.24.0
https://share.streamlit.io/luxorth1/league_capstone/main/Streamlit_App.py
