Create app.py

app.py

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+from operator import index
+import streamlit as st
+import plotly.express as px
+from pycaret.regression import setup, compare_models, pull, save_model, load_model
+import pandas_profiling
+import pandas as pd
+from streamlit_pandas_profiling import st_profile_report
+import os 
+
+if os.path.exists('./dataset.csv'): 
+    df = pd.read_csv('dataset.csv', index_col=None)
+
+with st.sidebar: 
+    st.image("https://www.onepointltd.com/wp-content/uploads/2020/03/inno2.png")
+    st.title("AutoNickML")
+    choice = st.radio("Navigation", ["Upload","Profiling","Modelling", "Download"])
+    st.info("This project application helps you build and explore your data.")
+
+if choice == "Upload":
+    st.title("Upload Your Dataset")
+    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 == "Profiling": 
+    st.title("Exploratory Data Analysis")
+    profile_df = df.profile_report()
+    st_profile_report(profile_df)
+
+if choice == "Modelling": 
+    chosen_target = st.selectbox('Choose the Target Column', df.columns)
+    if st.button('Run Modelling'): 
+        setup(df, target=chosen_target, silent=True)
+        setup_df = pull()
+        st.dataframe(setup_df)
+        best_model = compare_models()
+        compare_df = pull()
+        st.dataframe(compare_df)
+        save_model(best_model, 'best_model')
+
+if choice == "Download": 
+    with open('best_model.pkl', 'rb') as f: 
+        st.download_button('Download Model', f, file_name="best_model.pkl")