feat(examples): add Streamlit cleaning app recipe

examples/README.md

@@ -38,6 +38,7 @@ Located in [`examples/integrations/`](integrations/):
 | **DuckDB** | [`duckdb_workflow.py`](integrations/duckdb_workflow.py) | Direct cleaning of `DuckDBPyRelation` objects and registering back to DuckDB for SQL. |
 | **scikit-learn** | [`sklearn_pipeline.py`](integrations/sklearn_pipeline.py) | Custom `FreshDataCleaner` transformer for native `sklearn.pipeline.Pipeline` usage. |
 | **FastAPI** | [`fastapi_clean_service.py`](integrations/fastapi_clean_service.py) | Asynchronous cleaning microservice accepting CSV uploads or JSON records without blocking the event loop. |
+| **Streamlit** | [`streamlit_app.py`](integrations/streamlit_app.py) | Interactive CSV cleaning app with raw/cleaned previews, quality metrics, and a cleaned CSV download. |
 | **Apache Airflow** | [`airflow_task.py`](integrations/airflow_task.py) | Quality gating with `evaluate_trust_gate` and `FreshDataCleanOperator` DAG pattern. |
 | **Pandera** | [`09_pandera_recipe.py`](09_pandera_recipe.py) | Pre- and post-validation using declarative Pandera DataFrame schemas. |
 | **PyJanitor** | [`10_pyjanitor_interop.py`](10_pyjanitor_interop.py) | Combining explicit PyJanitor transforms with FreshData quality repair. |

examples/integrations/streamlit_app.py

@@ -0,0 +1,145 @@
+"""Interactive Streamlit data-cleaning recipe.
+
+Install dependencies with::
+
+    pip install streamlit freshdata-cleaner
+
+Run the app with::
+
+    streamlit run examples/integrations/streamlit_app.py
+"""
+
+from __future__ import annotations
+
+from io import BytesIO
+from typing import Any
+
+import pandas as pd
+import streamlit as st
+
+import freshdata as fd
+
+
+def demo_data() -> pd.DataFrame:
+    """Return a small messy dataset for trying the app without an upload."""
+    return pd.DataFrame(
+        {
+            "Customer Name": ["  Ava Chen ", "Noah Patel", "Mia Lopez", "Noah Patel"],
+            "Orders": ["3", "N/A", "5", "N/A"],
+            "Region": ["West", "East", "West", "East"],
+        }
+    )
+
+
+def load_uploaded_csv(contents: bytes) -> pd.DataFrame:
+    """Read uploaded CSV bytes into a DataFrame."""
+    return pd.read_csv(BytesIO(contents))
+
+
+def count_modified_columns(report: Any) -> int:
+    """Count columns modified by applied cleaning actions, imputations, or drops."""
+    modified = set(report.columns_imputed) | set(report.columns_dropped)
+    modified.update(
+        action.column
+        for action in report.actions
+        if action.column is not None
+        and action.count > 0
+        and getattr(action, "status", "automatic") in {"automatic", "approved"}
+    )
+    return len(modified)
+
+
+def main() -> None:
+    st.set_page_config(page_title="FreshData Cleaning Lab", layout="wide")
+    st.title("FreshData Cleaning Lab")
+    st.caption(
+        "Upload a CSV to compare its original data with FreshData's cleaned output."
+    )
+
+    uploaded_file = st.file_uploader("Choose a CSV file", type=["csv"])
+
+    if uploaded_file is None:
+        raw_df = demo_data()
+        st.info("Showing sample data. Upload a CSV to clean your own dataset.")
+    else:
+        try:
+            raw_df = load_uploaded_csv(uploaded_file.getvalue())
+        except (ValueError, UnicodeDecodeError, pd.errors.ParserError) as exc:
+            st.error(f"Could not read this CSV: {exc}")
+            st.stop()
+
+    st.sidebar.header("Cleaning Settings")
+    strategy = st.sidebar.selectbox(
+        "Strategy", ["balanced", "conservative", "aggressive"], index=0
+    )
+    drop_duplicates = st.sidebar.checkbox("Drop duplicate rows", value=True)
+
+    try:
+        cleaned_df, report = fd.clean(
+            raw_df,
+            strategy=strategy,
+            drop_duplicates=drop_duplicates,
+            return_report=True,
+        )
+    except Exception as exc:
+        st.error(f"FreshData could not clean this dataset: {exc}")
+        st.stop()
+
+    st.subheader("Cleaning summary")
+    metric_columns = st.columns(4)
+    metric_columns[0].metric(
+        "Rows removed", max(report.rows_before - report.rows_after, 0)
+    )
+    metric_columns[1].metric("Columns modified", count_modified_columns(report))
+    imputed_values = sum(
+        action.count
+        for action in report.actions
+        if (
+            action.step == "impute"
+            or (
+                action.step == "missing"
+                and action.column in report.columns_imputed
+            )
+        )
+        and getattr(action, "status", "automatic") in {"automatic", "approved"}
+        and action.count > 0
+    )
+    metric_columns[2].metric("Missing values imputed", imputed_values)
+    metric_columns[3].metric("Duplicates dropped", report.duplicates_removed)
+
+    raw_column, cleaned_column = st.columns(2)
+    with raw_column:
+        st.subheader("Raw data")
+        st.dataframe(raw_df, use_container_width=True)
+    with cleaned_column:
+        st.subheader("Cleaned data")
+        st.dataframe(cleaned_df, use_container_width=True)
+
+    st.download_button(
+        "Download cleaned CSV",
+        data=cleaned_df.to_csv(index=False).encode("utf-8"),
+        file_name="cleaned_data.csv",
+        mime="text/csv",
+    )
+
+    with st.expander("Quality report"):
+        st.code(report.summary(), language="text")
+        if report.actions:
+            st.dataframe(
+                pd.DataFrame(
+                    [
+                        {
+                            "Step": action.step,
+                            "Column": action.column or "",
+                            "Change": action.description,
+                            "Count": action.count,
+                        }
+                        for action in report.actions
+                    ]
+                ),
+                use_container_width=True,
+            )
+
+
+if __name__ == "__main__":
+    main()