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| try: | |
| import streamlit as st | |
| from ml_pipeline import load_data, eda_report, build_model, download_model,load_pycaret_dataset, get_all_datasets, handle_exception | |
| from st_social_media_links import SocialMediaIcons | |
| import streamlit.components.v1 as components | |
| import traceback | |
| VERSION = "0.5.4" | |
| # Title and description | |
| st.set_page_config( | |
| page_title="MLWiz - AutoML WorkBench", | |
| page_icon="🤖", | |
| menu_items={ | |
| "About": f"MLWize v{VERSION}" | |
| f"\nApp contact: [Sumit Khanna](https://github.com/bitbotcoder/)", | |
| "Report a Bug": "https://github.com/bitbotcoder/mlwiz/issues/new", | |
| "Get help": None, | |
| }, | |
| layout="wide", | |
| ) | |
| st.subheader("🤖 MLWiz - Automating ML Tasks") | |
| st.divider() | |
| with st.sidebar: | |
| st.image('logo.png', width=150) | |
| st.write("🔠 Supported Features") | |
| st.caption(""" | |
| - ✅ Datasets (Custom, PyCaret(disabled)) | |
| - ✅ Data Profiling & EDA | |
| - ✅ Build ML Models | |
| - ✅ Regression | |
| - ✅ Classification | |
| - ✅ Clustering | |
| - ✅ Time Series Forecasting | |
| - ✅ Anamoly Detection | |
| - ✅ Download Models | |
| """) | |
| st.divider() | |
| st.write("📢 Share with wider community") | |
| social_media_links = [ | |
| "https://x.com/intent/tweet?hashtags=streamlit%2Cpython&text=Check%20out%20this%20awesome%20Streamlit%20app%20I%20built%0A&url=https%3A%2F%2Fautoml-wiz.streamlit.app", | |
| "https://www.linkedin.com/sharing/share-offsite/?summary=https%3A%2F%2Fautoml-wiz.streamlit.app%20%23streamlit%20%23python&title=Check%20out%20this%20awesome%20Streamlit%20app%20I%20built%0A&url=https%3A%2F%2Fautoml-wiz.streamlit.app", | |
| "https://www.facebook.com/sharer/sharer.php?kid_directed_site=0&u=https%3A%2F%2Fautoml-wiz.streamlit.app", | |
| "https://github.com/bitbotcoder/mlwiz" | |
| ] | |
| social_media_icons = SocialMediaIcons(social_media_links, colors=["white"] * len(social_media_links)) | |
| social_media_icons.render(sidebar=True) | |
| #Tasks based on user selection | |
| tab1, tab2, tab3, tab4, = st.tabs(["📑Choose Dataset", "📊Perform EDA", "🧠Build Model", "📩Download Model"]) | |
| with tab1: | |
| c1, c2 = st.columns([1,2]) | |
| c1.write("Upload Custom Dataset files") | |
| #dataset_source = c1.radio("Select Dataset Source", options=["PyCaret", "Upload"], captions=["Load PyCaret Datasets", "Upload Custom Dataset files"]) | |
| #if dataset_source == "PyCaret": | |
| # pycaret_datasets = get_all_datasets() | |
| # selected_dataset = c2.selectbox("Select a dataset", pycaret_datasets) | |
| # if c2.button("Load Dataset"): | |
| # load_pycaret_dataset(selected_dataset) | |
| #elif dataset_source == "Upload": | |
| uploaded_file = c2.file_uploader("Choose a file", type=["csv", "xlsx"]) | |
| if uploaded_file is not None: | |
| load_data(uploaded_file) | |
| with tab2: | |
| eda_report() | |
| with tab3: | |
| st.write("**Configure ML Model**") | |
| col1,col2 = st.columns([0.4,0.7]) | |
| task = col1.selectbox("Select ML task", ["Classification", "Regression", "Clustering", "Anomaly Detection", "Time Series Forecasting"]) | |
| build_model(task,col2) | |
| with tab4: | |
| download_model(task) | |
| except Exception as e: | |
| handle_exception(e) | |
| st.success( | |
| "Show your 💘 ➡️ [Star the repo](https://github.com/bitbotcoder/mlwiz/)", | |
| icon="ℹ️", | |
| ) |