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main.py

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+'''
+Author: hibana2077 hibana2077@gmail.com
+Date: 2024-01-02 21:43:38
+LastEditors: hibana2077 hibana2077@gmail.com
+LastEditTime: 2024-01-03 18:23:40
+FilePath: \hayabusa\src\main.py
+Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE
+'''
+from operator import index
+import streamlit as st
+import plotly.express as px
+from ydata_profiling import ProfileReport
+import pandas as pd
+import pickle
+import time
+import ydata_profiling
+from streamlit_pandas_profiling import st_profile_report
+import os 
+
+if os.path.exists('./dataset/') == False:
+    os.mkdir('./dataset/')
+if os.path.exists('./model/') == False:
+    os.mkdir('./model/')
+if os.path.exists('./pipeline/') == False:
+    os.mkdir('./pipeline/')
+
+with st.sidebar:
+    st.title("AutoML")
+    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.drop("Unnamed: 0", axis=1, inplace=True)
+        df.to_csv('dataset.csv', index=None)
+        st.dataframe(df)
+        st.session_state['df'] = df
+
+if choice == "Profiling": 
+    st.title("Exploratory Data Analysis")
+    df:pd.DataFrame = st.session_state['df']
+    pr = ProfileReport(df, title="Profiling Report")
+    st_profile_report(pr)
+
+if choice == "Modelling": 
+    df:pd.DataFrame = st.session_state['df']
+    chosen_target = st.selectbox('Choose the Target Column', df.columns)
+    ml_task = st.selectbox('Choose the ML Task', ['Classification', 'Regression'])
+    if st.button('Run Modelling'):
+        if ml_task == 'Classification':
+            from pycaret.classification import setup, compare_models, pull, save_model, get_config
+            setup(df, target=chosen_target)
+            setup_df = pull()
+            st.dataframe(setup_df)
+            best_model = compare_models(exclude=['lightgbm'])
+            compare_df = pull()
+            st.dataframe(compare_df)
+            save_model(best_model, 'best_model')
+            save_model(best_model, f"./model/{chosen_target}_{time.time()}")
+            pipeline = get_config('pipeline')
+            st.write(pipeline)
+            # save the pipeline
+            with open('pipeline.pkl', 'wb') as f:
+                pickle.dump(pipeline, f)
+        else:
+            from pycaret.regression import setup, compare_models, pull, save_model
+            setup(df, target=chosen_target)
+            setup_df = pull()
+            st.dataframe(setup_df)
+            best_model = compare_models(exclude=['lightgbm'])
+            compare_df = pull()
+            st.dataframe(compare_df)
+            save_model(best_model, 'best_model')
+            save_model(best_model, f"./model/{chosen_target}_{time.time()}")
+            pipeline = get_config('pipeline')
+            st.write(pipeline)
+            # save the pipeline
+            with open('pipeline.pkl', 'wb') as f:
+                pickle.dump(pipeline, f)
+
+if choice == "Download": 
+    with open('best_model.pkl', 'rb') as f: 
+        st.download_button('Download Model', f, file_name="best_model.pkl")
+    with open('pipeline.pkl', 'rb') as f:
+        st.download_button('Download Pipeline', f, file_name="pipeline.pkl")

requirements.txt

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+ydata-profiling
+pandas
+pycaret[models]
+streamlit
+numpy
+streamlit-pandas-profiling