diff --git a/.streamlit/config.toml b/.streamlit/config.toml new file mode 100644 index 0000000..bc47c20 --- /dev/null +++ b/.streamlit/config.toml @@ -0,0 +1,6 @@ +[theme] +base = "light" +primaryColor = "#4ecdc4" +backgroundColor = "#F7FDFA" +secondaryBackgroundColor = "#E8F8F5" +textColor = "#262730" diff --git a/README.md b/README.md index 85eb861..2289cc4 100644 --- a/README.md +++ b/README.md @@ -12,10 +12,21 @@ An interactive command-line tool to automate Exploratory Data Analysis (EDA) and generate beautiful, insightful reports in seconds. +

+ + Open in Streamlit + +
+

+ ## What is DORA? DORA is a tool that does the heavy lifting of data analysis for you. Instead of writing code to create charts and calculate statistics, you give DORA your data file, and it builds a comprehensive, beautiful HTML report automatically. +You can use DORA in two ways: +1. **Web App**: No installation needed. Just upload your data and download the report. +2. **CLI Tool**: Install locally for power usage and automation. + If you have used tools like [ydata-profiling](https://github.com/ydataai/ydata-profiling) and [sweetviz](https://pypi.org/project/sweetviz/), DORA lets you do more. It provides a way to process kaggle dataset as well without a lot of clutter. ## Get started in 2 minutes diff --git a/app.py b/app.py new file mode 100644 index 0000000..7c88018 --- /dev/null +++ b/app.py @@ -0,0 +1,536 @@ +""" +Streamlit application for DORA (Data-Oriented Report Automator). +""" + +from datetime import datetime +import logging +import os +import shutil +import uuid +import re +from pathlib import Path + +import streamlit as st + +from src.dora.data_loader import read_data +from src.dora.kaggle import KaggleHandler +from src.dora.profiling import generate_profile +from src.dora.plots import univariate, bivariate, multivariate +from src.dora.reporting.generator import create_report + +logging.basicConfig(level=logging.INFO) + + +def init_session_state(): + """Initialize session state variables.""" + if "session_id" not in st.session_state: + st.session_state.session_id = str(uuid.uuid4()) + if "df" not in st.session_state: + st.session_state.df = None + if "output_dir" not in st.session_state: + # Create a unique output directory for this session + base_output = Path("output") / st.session_state.session_id + base_output.mkdir(parents=True, exist_ok=True) + st.session_state.output_dir = base_output + if "input_source" not in st.session_state: + st.session_state.input_source = None + if "zip_path" not in st.session_state: + st.session_state.zip_path = None + + +def setup_page(): + """Configure the Streamlit page settings.""" + st.set_page_config( + page_title="DORA | Data-Oriented Report Automator", + page_icon="📊", + layout="wide", + initial_sidebar_state="expanded", + ) + + # Inject Custom CSS + st.markdown( + """ + + """, + unsafe_allow_html=True, + ) + + # Logo and Header + logo_path = Path("data/assets/dora-updated-concept.png") + if logo_path.exists(): + st.image(str(logo_path), width=400) + else: + st.title("📊 DORA") + + st.markdown( + """ + ### Automate your EDA in seconds. + Upload a dataset or connect to Kaggle to generate insightful, beautiful reports instantly. + """ + ) + + +def load_local_data(uploaded_file): + """Handle loading of local file uploads.""" + # We need to save the uploaded file to a temporary path so read_data can read it + temp_path = st.session_state.output_dir / uploaded_file.name + try: + with open(temp_path, "wb") as f: + f.write(uploaded_file.getbuffer()) + + with st.spinner("Loading data..."): + df = read_data(temp_path) + st.session_state.df = df + st.session_state.input_source = uploaded_file.name + st.success(f"Successfully loaded '{uploaded_file.name}'") + except Exception as e: + st.error(f"Error loading file: {e}") + + +def load_kaggle_data(kaggle_input): + """Handle loading of Kaggle datasets.""" + try: + with st.spinner("Connecting to Kaggle..."): + # Extract ID if it's a URL + if KaggleHandler.is_kaggle_url(kaggle_input): + dataset_id = KaggleHandler.extract_dataset_id(kaggle_input) + else: + dataset_id = kaggle_input + + # Download using existing handler + file_path = KaggleHandler.download_dataset(dataset_id) + + # Load data + df = read_data(file_path) + st.session_state.df = df + st.session_state.input_source = dataset_id + st.success(f"Successfully loaded data from '{dataset_id}'") + + except Exception as e: + st.error(f"Error processing Kaggle dataset: {e}") + + +def render_ingestion(): + """Render the data ingestion section (Local File & Kaggle Tabs).""" + st.header("1. Data Ingestion") + tab_local, tab_kaggle = st.tabs(["Local File", "Kaggle Dataset"]) + + with tab_local: + st.info("Upload a CSV, Excel, JSON, or Parquet file.") + uploaded_file = st.file_uploader( + "Choose a file", type=["csv", "xlsx", "json", "parquet"] + ) + + if uploaded_file is not None: + # Button to trigger load + if st.button("Load Local Data", key="btn_local"): + load_local_data(uploaded_file) + + with tab_kaggle: + st.info("Enter a Kaggle Dataset ID (e.g., `owner/dataset`) or full URL.") + kaggle_input = st.text_input("Kaggle Dataset ID or URL") + + if st.button("Download & Load from Kaggle", key="btn_kaggle"): + if kaggle_input: + load_kaggle_data(kaggle_input) + else: + st.warning("Please enter a valid Dataset ID or URL.") + + +def render_preview(): + """Render the dataframe preview.""" + if st.session_state.df is not None: + st.divider() + st.subheader(f"Dataset Preview: {st.session_state.input_source}") + st.write( + f"**Shape:** {st.session_state.df.shape[0]} rows x {st.session_state.df.shape[1]} columns" + ) + st.dataframe(st.session_state.df.head()) + + +def render_sidebar(): + """Render the configuration sidebar.""" + if st.session_state.df is None: + return None + + st.sidebar.header("2. Configuration") + + if st.sidebar.button("⚠️ Clear & Start Fresh", type="primary", use_container_width=True): + st.session_state.clear() + st.rerun() + + # Target Variable + target_variable = st.sidebar.selectbox( + "Target Variable (for Bivariate Analysis)", + options=["None"] + list(st.session_state.df.columns), + index=0, + ) + if target_variable == "None": + target_variable = None + + st.sidebar.subheader("Analysis Steps") + config = { + "target_variable": target_variable, + "run_profile": st.sidebar.checkbox("Data Profile", value=True), + "run_univariate": st.sidebar.checkbox("Univariate Analysis", value=True), + "run_bivariate": st.sidebar.checkbox("Bivariate Analysis", value=True), + "run_multivariate": st.sidebar.checkbox("Multivariate Analysis", value=True), + } + return config + + +def run_profile_step(df, current_report_data): + """Run data profiling step.""" + try: + profile_data = generate_profile(df) + current_report_data["profile"] = profile_data + except Exception as e: + st.error(f"Error in Profiling: {e}") + + +def run_univariate_step(df, charts_dir, current_report_data): + """Run univariate analysis step.""" + try: + params = { + "plot_types": { + "numerical": ["histogram", "boxplot"], + "categorical": ["barplot"], + }, + "max_categories": 20, + } + plot_paths = univariate.generate_plots(df, charts_dir, params) + current_report_data["univariate_plots"] = plot_paths + except Exception as e: + st.error(f"Error in Univariate Analysis: {e}") + + +def run_bivariate_step(df, config, charts_dir, current_report_data): + """Run bivariate analysis step.""" + if not config["target_variable"]: + st.warning("Skipping Bivariate Analysis: No Target Variable selected.") + return + + try: + params = {"target_centric": True, "max_categories": 20} + plot_paths = bivariate.generate_plots( + df, + config["target_variable"], + charts_dir, + params, + ) + current_report_data["bivariate_plots"] = plot_paths + except Exception as e: + st.error(f"Error in Bivariate Analysis: {e}") + + +def run_multivariate_step(df, charts_dir, current_report_data): + """Run multivariate analysis step.""" + try: + params = {"correlation_cols": []} + plot_paths = multivariate.generate_plots(df, charts_dir, params) + current_report_data["multivariate_plots"] = plot_paths + except Exception as e: + st.error(f"Error in Multivariate Analysis: {e}") + + +def generate_final_report(current_report_data): + """Generate the HTML report and ZIP archive.""" + try: + create_report(current_report_data, st.session_state.output_dir) + + # Construct new zip filename: [input_filename]_[timestamp] + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + + # Clean safe filename from input source + raw_name = str(st.session_state.input_source) + # Remove extension if present (simple check) + if "." in raw_name: + safe_name = raw_name.rsplit(".", 1)[0] + else: + safe_name = raw_name + + # Replace non-alphanumeric chars (except _-) with underscore for safety + safe_name = re.sub(r'[^\w\-]', '_', safe_name) + + zip_filename = f"{safe_name}_{timestamp}" + zip_base_path = st.session_state.output_dir.parent / zip_filename + + # shutil.make_archive adds the extension automatically + archive_path_str = shutil.make_archive(str(zip_base_path), "zip", st.session_state.output_dir) + zip_path = Path(archive_path_str) + + # Store in session state for download button + st.session_state.zip_path = zip_path + + st.success( + f"Analysis Complete! Report generated in {st.session_state.output_dir}" + ) + except Exception as e: + st.error(f"Error generating final report: {e}") + + +def execute_analysis(config): + """Execute the analysis steps based on configuration.""" + with st.spinner("Running Analysis..."): + # Reset output dir components + charts_dir = st.session_state.output_dir / "charts" + if charts_dir.exists(): + shutil.rmtree(charts_dir, ignore_errors=True) + + report_path = st.session_state.output_dir / "eda_report.html" + if report_path.exists(): + try: + os.remove(report_path) + except OSError: + pass + + st.session_state.output_dir.mkdir(parents=True, exist_ok=True) + charts_dir.mkdir(exist_ok=True) + + current_report_data = { + "title": f"EDA Report for {st.session_state.input_source}", + } + + # Run configured steps + if config["run_profile"]: + run_profile_step(st.session_state.df, current_report_data) + + if config["run_univariate"]: + run_univariate_step(st.session_state.df, charts_dir, current_report_data) + + if config["run_bivariate"]: + run_bivariate_step(st.session_state.df, config, charts_dir, current_report_data) + + if config["run_multivariate"]: + run_multivariate_step(st.session_state.df, charts_dir, current_report_data) + + # Update Session State + st.session_state.report_data = current_report_data + st.session_state.analysis_complete = True + + # Generate Physical Report Immediately + generate_final_report(current_report_data) + + +def render_profile_tab(report_data): + """Render the data profile content.""" + st.header("Data Profile") + try: + profile_data = report_data["profile"] + + # Metrics Row + col1, col2, col3, col4 = st.columns(4) + col1.metric("Rows", f"{profile_data['dataset_shape'][0]:,}") + col2.metric("Columns", f"{profile_data['dataset_shape'][1]:,}") + col3.metric("Duplicate Rows", profile_data.get("duplicates", "N/A")) + col4.metric("Memory Usage", profile_data.get("memory_usage", "N/A")) + + st.subheader("Column Profiles") + for col_prof in profile_data["column_profiles"]: + with st.expander(f"{col_prof['name']} ({col_prof['type']})"): + st.json(col_prof["stats"]) + if col_prof.get("sparkline_base64"): + st.markdown( + f'', + unsafe_allow_html=True, + ) + + if profile_data.get("missing_values_html"): + st.subheader("Missing Values") + st.markdown( + profile_data["missing_values_html"], unsafe_allow_html=True + ) + except Exception as e: # pylint: disable=broad-exception-caught + st.error(f"Error displaying profile: {e}") + + +def render_univariate_tab(report_data, charts_dir): + """Render the univariate analysis content.""" + st.header("Univariate Analysis") + plot_paths = report_data["univariate_plots"] + if not plot_paths: + st.info("No plots generated.") + else: + cols = st.columns(2) + for i, path_str in enumerate(plot_paths): + full_path = charts_dir / Path(path_str).name + if full_path.exists(): + cols[i % 2].image(str(full_path), width="stretch") + + +def render_bivariate_tab(report_data, charts_dir): + """Render the bivariate analysis content.""" + st.header("Bivariate Analysis") + plot_paths = report_data["bivariate_plots"] + if not plot_paths: + st.info("No plots generated.") + else: + cols = st.columns(2) + for i, path_str in enumerate(plot_paths): + full_path = charts_dir / Path(path_str).name + if full_path.exists(): + cols[i % 2].image(str(full_path), width="stretch") + + +def render_multivariate_tab(report_data, charts_dir): + """Render the multivariate analysis content.""" + st.header("Multivariate Analysis") + plot_paths = report_data["multivariate_plots"] + for path_str in plot_paths: + full_path = charts_dir / Path(path_str).name + if full_path.exists(): + st.image(str(full_path), width="stretch") + + +def render_report_tabs(): + """Render the analysis results in tabs.""" + if not (st.session_state.analysis_complete and st.session_state.report_data): + return + + charts_dir = st.session_state.output_dir / "charts" + report_data = st.session_state.report_data + + tabs_list = [] + if "profile" in report_data: + tabs_list.append("Profile") + if "univariate_plots" in report_data: + tabs_list.append("Univariate") + if "bivariate_plots" in report_data: + tabs_list.append("Bivariate") + if "multivariate_plots" in report_data: + tabs_list.append("Multivariate") + + if tabs_list: + tabs = st.tabs(tabs_list) + tab_idx = 0 + + # Profile Render + if "profile" in report_data: + with tabs[tab_idx]: + render_profile_tab(report_data) + tab_idx += 1 + + # Univariate Render + if "univariate_plots" in report_data: + with tabs[tab_idx]: + render_univariate_tab(report_data, charts_dir) + tab_idx += 1 + + # Bivariate Render + if "bivariate_plots" in report_data: + with tabs[tab_idx]: + render_bivariate_tab(report_data, charts_dir) + tab_idx += 1 + + # Multivariate Render + if "multivariate_plots" in report_data: + with tabs[tab_idx]: + render_multivariate_tab(report_data, charts_dir) + tab_idx += 1 + + +def render_download_section(): + """Render the download button for the full report.""" + if not (st.session_state.analysis_complete and st.session_state.report_data): + return + + st.divider() + st.subheader("Download Report Data") + + st.info( + """ + 📥 **How to view your report:** + 1. Click the button below to download the ZIP file. + 2. Extract the ZIP file to a folder on your computer. + 3. Double-click `eda_report.html` to open it in your browser. + """ + ) + + # Use path from session state if available + zip_path = st.session_state.get("zip_path") + + if zip_path and zip_path.exists(): + with open(zip_path, "rb") as f: + st.download_button( + label="Download Full Report (ZIP)", + data=f, + file_name=zip_path.name, + mime="application/zip", + ) + else: + st.warning("Report file not found. Please re-run the analysis.") + + +def main(): + """Main function to run the Streamlit application.""" + setup_page() + init_session_state() + + # Data Ingestion + render_ingestion() + + # Data Preview + render_preview() + + # Configuration & Analysis + config = render_sidebar() + + # Visualization Layer (Main Area) + if "analysis_complete" not in st.session_state: + st.session_state.analysis_complete = False + if "report_data" not in st.session_state: + st.session_state.report_data = None + + if config: + if st.button("Run Analysis", type="primary"): + execute_analysis(config) + + # Render Results and Download + render_report_tabs() + render_download_section() + + # Placeholder for next steps + if st.session_state.df is not None and not st.session_state.analysis_complete: + st.info("Data loaded! Analysis configuration needed next.") + + +if __name__ == "__main__": + main() diff --git a/poetry.lock b/poetry.lock index 4b33c20..c230f62 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,30 @@ # This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. +[[package]] +name = "altair" +version = "6.0.0" +description = "Vega-Altair: A declarative statistical visualization library for Python." +optional = false +python-versions = ">=3.9" +groups = ["main"] +files = [ + {file = "altair-6.0.0-py3-none-any.whl", hash = "sha256:09ae95b53d5fe5b16987dccc785a7af8588f2dca50de1e7a156efa8a461515f8"}, + {file = "altair-6.0.0.tar.gz", hash = "sha256:614bf5ecbe2337347b590afb111929aa9c16c9527c4887d96c9bc7f6640756b4"}, +] + +[package.dependencies] +jinja2 = "*" +jsonschema = ">=3.0" +narwhals = ">=1.27.1" +packaging = "*" +typing-extensions = {version = ">=4.12.0", markers = "python_version < \"3.15\""} + +[package.extras] +all = ["altair-tiles (>=0.3.0)", "anywidget 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