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import streamlit as st
from pycaret.classification import setup, compare_models, pull, save_model
import pandas as pd
import os
# Importing necessary modules from pandas_profiling
from pandas_profiling import ProfileReport
from streamlit_pandas_profiling import st_profile_report
def main():
if os.path.exists('./dataset.csv'):
df = pd.read_csv('dataset.csv', index_col=None)
with st.sidebar:
st.image('https://leilaabdel.com/img/deep_learning_course_pic.png')
st.title('AutoML Classification')
choice = st.radio('Navigation', ['Upload', 'EDA', 'Modelling', 'Download'])
if choice == 'Upload':
file_uploader_ui()
elif choice == 'EDA' and 'df' in locals():
eda_ui(df)
elif choice == 'Modelling' and 'df' in locals():
modelling_ui(df)
elif choice == 'Download':
download_ui()
def file_uploader_ui():
st.title('Upload your data file')
file = st.file_uploader('Upload your data')
if file:
df = pd.read_csv(file, index_col=None)
df.to_csv('dataset.csv', index=None)
st.dataframe(df.head())
def eda_ui(df):
st.title('Exploratory Data Analysis')
profile = ProfileReport(df, explorative=True)
st_profile_report(profile)
def modelling_ui(df):
target_col = st.selectbox('Choose the target column', df.columns)
if st.button('Train model'):
setup(data=df, target=target_col)
best_model = compare_models()
compare_df = pull()
st.dataframe(compare_df)
save_model(best_model, 'best_model.pkl')
def download_ui():
try:
with open('best_model.pkl', 'rb') as f:
st.download_button('Download the best model', f, 'best_model.pkl')
except Exception as e:
st.error(f"Error downloading the model: {str(e)}")
if __name__ == "__main__":
main()
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