feat(examples): add Streamlit interactive data cleaning app recipe #511

Context

Streamlit is one of the most widely used tools by data scientists and analysts to build interactive data prototypes. Adding a standalone integration recipe showing how to upload messy tabular data (CSV/Excel), run freshdata.clean(), and inspect interactive before/after summaries and quality reports will help many new users explore FreshData visually.

Task

Create a standalone runnable Streamlit app recipe in examples/integrations/streamlit_app.py demonstrating:

1. File uploader for tabular data (.csv).
2. Execution of freshdata.clean() with report generation (return_report=True).
3. Side-by-side display of the raw vs cleaned DataFrame.
4. Summary metrics cards (rows removed, columns modified, missing values imputed, duplicates dropped).
5. Download button to export the cleaned CSV.

Acceptance Criteria

• Create examples/integrations/streamlit_app.py with standalone, clean, and well-commented code.
• Document required dependency (pip install streamlit freshdata-cleaner) in the script header docstring.
• Include fallback demo data (e.g. synthetic DataFrame) if no file is uploaded so the script can be tested immediately with streamlit run examples/integrations/streamlit_app.py.
• Register the new recipe in examples/README.md.
• Script adheres to repository code style (ruff check examples/integrations/streamlit_app.py).

Pointers

• Reference existing integration recipes: examples/integrations/
• Recipe index: examples/README.md
• Core API reference: src/freshdata/api.py

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