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| from operator import index | |
| import streamlit as st | |
| import plotly.express as px | |
| import numpy as np | |
| from lazypredict.Supervised import LazyRegressor, LazyClassifier | |
| from sklearn.model_selection import train_test_split | |
| import pandas as pd | |
| from autoviz.AutoViz_Class import AutoViz_Class | |
| from sklearn.datasets import load_iris | |
| import os | |
| def load_data(): | |
| X, y = load_iris(return_X_y=True) | |
| df = pd.DataFrame(X, columns=["sepal_length", "sepal_width", "petal_length", "petal_width"]) | |
| df['target'] = y | |
| return df | |
| df = load_data() | |
| with st.sidebar: | |
| st.image("https://analyticsindiamag.com/wp-content/uploads/2020/10/lazypredict.jpg") | |
| 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.to_csv('dataset.csv', index=None) | |
| st.dataframe(df) | |
| if choice == "Profiling": | |
| st.title("Exploratory Data Analysis") | |
| # AV = AutoViz_Class() | |
| # config = { | |
| # 'filename': None, | |
| # 'sep': ',', | |
| # 'depVar': '', | |
| # 'dfte': df, | |
| # 'header': 0, | |
| # 'verbose': 0, | |
| # 'lowess': False, | |
| # 'chart_format': 'html', | |
| # 'max_rows_analyzed': 10000, | |
| # 'max_cols_analyzed': 50, | |
| # # 'save_plot_path': None, | |
| # } | |
| # AV.AutoViz() | |
| # st.components.v1.html(AV.html, width=1000, height=1000, scrolling=True) | |
| AV = AutoViz_Class() | |
| AV.AutoViz("",dfte=df) | |
| report_path = os.path.join(os.path.dirname(__file__), "autoviz_report.html") | |
| try: | |
| AV.save_html(report_path) | |
| HtmlFile = open(report_path, 'r', encoding='utf-8') | |
| source_code = HtmlFile.read() | |
| st.components.v1.html(source_code, width=1000, height=1000, scrolling=True) | |
| except: | |
| st.write("Error occurred while generating the AutoViz report.") | |
| if choice == "Modelling": | |
| target_options = df.columns.tolist() | |
| chosen_target = st.selectbox('Choose the Target Column', target_options) | |
| model_type = st.selectbox('Choose Model Type', ['Regression', 'Classification']) | |
| if st.button('Run Modelling'): | |
| X = df.drop(chosen_target, axis=1) | |
| y = df[chosen_target] | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=65481254) | |
| if model_type == 'Classification': | |
| # Classification | |
| clf = LazyClassifier(verbose=0, ignore_warnings=False, custom_metric=None) | |
| models, predictions = clf.fit(X_train, X_test, y_train, y_test) | |
| model_dictionary = clf.provide_models(X_train, X_test, y_train, y_test) | |
| else: | |
| # Regression | |
| reg = LazyRegressor(verbose=0, ignore_warnings=False, custom_metric=None) | |
| models, predictions = reg.fit(X_train, X_test, y_train, y_test) | |
| model_dictionary = reg.provide_models(X_train, X_test, y_train, y_test) | |
| st.dataframe(models) | |
| if choice == "Download": | |
| with open('model_dictionary.pkl', 'wb') as f: | |
| st.download_button('Download Model', f, file_name="model_dictionary.pkl") |