Upload 3 files

app.py

@@ -0,0 +1,58 @@
+import streamlit as st
+from pycaret.classification import setup, compare_models, pull, save_model
+import pandas as pd
+import os
+
+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')
+    from streamlit_pandas_profiling import st_profile_report
+    profile_df = df.profile_report()
+    st_profile_report(profile_df)
+
+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()

dataset.csv

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requirements.txt

@@ -0,0 +1,3 @@
+streamlit
+pycaret
+pandas