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import os
import pandas as pd
import pandas_profiling
import streamlit as st
#ML stuff
from pycaret.classification import compare_models, pull, save_model, setup
#from pycaret.regression import compare_models, pull, save_model, setup
from streamlit_pandas_profiling import st_profile_report
with st.sidebar:
st.image("https://cdn.pixabay.com/photo/2018/09/18/11/19/artificial-intelligence-3685928_1280.png")
st.title("EasyAutoML")
choice = st.radio("Navigation",["Data loading","Exploratory","Modeling","Download"])
st.info("This application to explore your data & build an automated ML pipeline.")
if os.path.exists("source_data.csv"):
df = pd.read_csv("source_data.csv", index_col=None)
if choice == "Data loading":
st.title("Upload your data for modeling")
file = st.file_uploader("Upload your dataset here")
if file:
df = pd.read_csv(file, index_col=None)
df.to_csv("source_data.csv", index=None)
st.dataframe(df)
elif choice == "Exploratory":
st.title('Automated EDA')
profile_report = df.profile_report()
st_profile_report(profile_report)
elif choice == "Modeling":
st.title('Time for ML')
target = st.selectbox('Choose the target column', df.columns)
if st.button("Train model"):
setup(df, target=target, silent=True)
setup_df = pull()
st.info("This is the ML experiment settings")
st.dataframe(setup_df)
best_model = compare_models()
compare_df = pull()
st.info("This is the ML model")
st.dataframe(compare_df)
best_model
save_model(best_model, 'best_model')
elif choice == "Download":
with open("best_model.pkl",'rb') as f:
st.download_button("Download the model file",f,"best_model.pkl")
else:
pass
st.write("Made with <3 by Amdjed")
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