Interactive Streamlit app for exploring perfume ratings and popularity (votes) across brands.

The app loads a pre-cleaned dataset from Data/all_brands_clean.csv, lets you select a brand, and visualizes that brand’s fragrances on a Plotly scatter chart (rating vs. votes on a log scale). Clicking a point opens the fragrance’s Fragrantica page (when url is present in the dataset).

• Landing page (app.py) with navigation to the explorer
• Explorer page (pages/fragrance_explorer.py) with:
  • Brand selector with first-letter filtering
  • Summary metrics (count of fragrances, share with rating ≥ 4.0)
  • Interactive Plotly chart (rating vs votes)
  • Click-to-open fragrance URL (if available)

.
├── app.py
├── pages/
│   └── fragrance_explorer.py
├── src/
│   ├── __init__.py
│   ├── data.py
│   ├── plots.py
│   └── widgets.py
└── Data/
    └── all_brands_clean.csv

• Python 3.10+ (recommended)

Main Python dependencies used by the code:

• streamlit
• pandas
• plotly

Create and activate a virtual environment, then install dependencies.

If you have a requirements.txt in your environment, prefer using it:

pip install -r requirements.txt

Otherwise install the core dependencies directly:

pip install streamlit pandas plotly

The app expects the dataset at:

Data/all_brands_clean.csv

Notes:

• The Data/ directory is ignored by git (.gitignore contains Data/).
• Required columns (enforced in src/data.py): brand, name, rating, votes
• Optional column: url (enables click-to-open from the chart)

If any required columns are missing, the app will show a friendly message (it will treat the dataset as empty).

From the project root:

streamlit run app.py

Then use the sidebar navigation (or the button on the landing page) to open Fragrance Explorer.

• Data loading is cached via st.cache_data in src/data.py.
• The explorer resolves the dataset path relative to the project root, so running via streamlit run app.py from the root directory is the expected workflow.