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| import streamlit as st | |
| import plotly.express as px | |
| import os | |
| import pandas as pd | |
| import pandas_profiling | |
| from streamlit_pandas_profiling import st_profile_report | |
| from pycaret.regression import setup, compare_models, pull, save_model | |
| with st.sidebar: | |
| st.image("https://www.onepointltd.com/wp-content/uploads/2020/03/inno2.png") | |
| st.title("Auto Streamlit") | |
| choice = st.radio("Navigation", ["Upload", "Profile", "Machine Learning", "Download"]) | |
| st.info("Hello") | |
| if os.path.exists("dataset.csv"): | |
| df = pd.read_csv('dataset.csv', index_col=None) | |
| if choice == "Upload": | |
| st.title("Upload") | |
| file = st.file_uploader("Upload Your Dataset") | |
| if file: | |
| df = pd.read_csv(file, index_col=None) | |
| df.to_csv("dataset.csv", index=None) | |
| st.dataframe(df) | |
| if choice == "Profile": | |
| st.title("Profile") | |
| profile_df = df.profile_report() | |
| st_profile_report(profile_df) | |
| if choice == "Machine Learning": | |
| target = st.selectbox("Choose the Target", df.columns) | |
| if st.button("Run Modelling"): | |
| setup(df, target=target, silent=True) | |
| setup_df = pull() | |
| st.dataframe(setup_df) | |
| best_model = compare_models() | |
| compare_df = pull() | |
| save_model(best_model, 'best_model') | |
| st.dataframe(compare_df) | |
| if choice == "Download": | |
| with open("best_model.pkl", 'rb') as f: | |
| st.download_button("Download Model", f, "best_model_test.pkl") | |