ModuleNotFoundError: No module named 'pycaret.internal' - heroku

Question

I am trying to deploy a ML model using Streamlit and Pycaret on Heroku.
When I try deploying the app, I get the following error:
ModuleNotFoundError: No module named 'pycaret.internal'
Traceback:
File "/app/.heroku/python/lib/python3.6/site-packages/streamlit/ScriptRunner.py", line 322, in _run_script
exec(code, module.__dict__)
File "/app/Final.py", line 11, in <module>
tuned_cat=joblib.load('Cat.pkl')
File "/app/.heroku/python/lib/python3.6/site-packages/joblib/numpy_pickle.py", line 585, in load
obj = _unpickle(fobj, filename, mmap_mode)
File "/app/.heroku/python/lib/python3.6/site-packages/joblib/numpy_pickle.py", line 504, in _unpickle
obj = unpickler.load()
File "/app/.heroku/python/lib/python3.6/pickle.py", line 1050, in load
dispatch[key[0]](self)
File "/app/.heroku/python/lib/python3.6/pickle.py", line 1338, in load_global
klass = self.find_class(module, name)
File "/app/.heroku/python/lib/python3.6/pickle.py", line 1388, in find_class
__import__(module, level=0)
I have the following dependencies in the requirements.txt file:
pycaret==1.0.0
streamlit==0.58.0
I had to go with the pycaret version 1.0 because when I chose the latest version the slug size on Heroku would go beyond 500M and it would not deploy. Compiled slug size: 552M is too large (max is 500M).
Link to Github
Final.py:
import streamlit as st
import joblib
from pycaret.classification import *
tuned_cat=joblib.load('Cat.pkl')
import tensorflow as tf
physical_devices = tf.config.list_physical_devices('CPU')
def run():
add_selectbox = st.sidebar.selectbox(
"What would you like to do?",
("Online Prediction","Batch Prediction"))
st.sidebar.info('This app is created to predict if the applicant should be granted a loan or not.')
st.title("Loan Prediction App")
if add_selectbox == 'Online Prediction':
gender = st.selectbox('Gender',['Female','Male'])
married = st.selectbox('Married',['No','Yes'])
depend = st.selectbox('Dependents',['0','1','2','3+'])
edu = st.selectbox('Education',['Graduate','Not Graduate'])
self = st.selectbox('Self Employed',['No','Yes'])
app_inc = st.number_input ('Applicant Income')
co_inc = st.number_input ('Coapplicant Income')
amt = st.number_input ('Loan Amount')
term = st.number_input ('Loan Amount Term')
credit = st.selectbox('Credit History',['0','1'])
prop_are = st.selectbox('Property Area',['Rural','Semiurban','Urban'])
output=""
test_df = pd.DataFrame()
test_df['Gender']= [gender]
test_df['Married']=[married]
test_df['Dependents']=[depend]
test_df['Education']=[edu]
test_df['Self_Employed']=[self]
test_df['ApplicantIncome']=[app_inc]
test_df['CoapplicantIncome']=[co_inc]
test_df['LoanAmount']=[amt]
test_df['Loan_Amount_Term']=[term]
test_df['Credit_History']=[credit]
test_df['Property_Area']=[prop_are]
if st.button("Predict"):
Cat_pred=predict_model(tuned_cat,data=test_df)['Label']
output = Cat_pred.values
if(output==0):
text="Rejected"
st.error(text)
elif(output==1):
text="Approved"
st.success(text)
if add_selectbox == 'Batch Prediction':
file_upload = st.file_uploader("Upload excel file for predictions", type=["xlsx"])
if file_upload is not None:
data = pd.read_excel(file_upload)
st.success('File uploaded successfully!')
Cat_pred=predict_model(tuned_cat,data=data)['Label']
data['Prediction']=Cat_pred
st.write(data)
st.markdown(get_table_download_link(data), unsafe_allow_html=True)
import base64
def get_table_download_link(df):
"""Generates a link allowing the data in a given panda dataframe to be downloaded
in: dataframe
out: href string
"""
csv = df.to_csv(index=False)
b64 = base64.b64encode(
csv.encode()
).decode() # some strings <-> bytes conversions necessary here
return f'Download csv file'
if __name__ == '__main__':
run()