| import streamlit as st | |
| import pandas as pd | |
| from flaml import AutoML | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.metrics import accuracy_score | |
| from utils import csv_to_featuers_list, pre_process_df, pre_process_features | |
| st.set_page_config(layout="wide") | |
| st.title("Auto ML") | |
| with st.sidebar: | |
| demo_but = st.button(label="Demo with Wine Rate Dataset") | |
| csv = st.file_uploader(label='CSV file') | |
| type_ = st.selectbox(label='Type', options=['Classification', 'Regression']) | |
| lst_features = csv_to_featuers_list(csv) | |
| label = st.selectbox(label='label', options=lst_features) | |
| automl_type = st.selectbox(label='Type of AutoML', options=['AutoML (Flmal)', 'Sklearn AutoML']) | |
| budget = st.text_area(label='Budget Time', value="10") | |
| start_but = st.button(label='AutoML') | |
| if demo_but == True: | |
| df = pd.read_csv('WineRate.csv') | |
| df = pre_process_df(df) | |
| label = 'quality' | |
| X = df[df.columns.difference([label])] | |
| y = df[label] | |
| X = pre_process_features(X) | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.2, random_state=89) | |
| automl = AutoML() | |
| automl.fit(X_train, y_train, task="classification", time_budget=int(budget)) | |
| acc = accuracy_score(automl.predict(X_test), y_test) | |
| st.text(f'accuracy = {acc}') |