---
license: apache-2.0
title: Smokey the Bear Helper
sdk: streamlit
emoji: 🐨
colorFrom: purple
colorTo: purple
short_description: App to detect deforestation and explore satellite embeddings
---

# Streamlit App

A 4-page portfolio app for exploring the forest loss classification project. Runs locally or via Docker.

---

## Navigation

A custom sidebar nav (HTML GET-form buttons with active-state highlighting) links the four pages. A region selector below the nav links controls which "other" AOI is shown in supplemental sidebar stats.

---

## Pages

### Page 1 — Project Overview

Hero section with a Lottie animation, project title, and a one-paragraph summary of the approach.

**KPI cards** (loaded from `resources/cache/kpi_summary.json`):
- Pixel-Years, Regions, Features Engineered, Best PR-AUC, Best F1, Brier Score
- PR-AUC Lift metric — improvement over logistic regression baseline

**A Tale of Two Forests** — side-by-side stat cards comparing Canada (PR-AUC 0.91) and Amazon Basin (PR-AUC 0.62), plus a delta callout. This frames the central question the app explores.

*Bonus: Smokey's birthday easter egg fires on August 9th.*

---

### Page 2 — Explore the Data

AOI radio selector (inline) drives all four tabs. Each tab loads pre-computed parquet/JSON from `resources/cache/` via `@st.cache_data`.

| Tab | Contents |
|---|---|
| 📡 Embedding Profiles | Mean embedding delta profile per dimension, grouped by class (Loss / No Loss) or by year. Bar chart of top 10 most divergent embedding dimensions. |
| 🎯 Target Distribution | Pie chart of class distribution. Metric cards: total pixel-years, loss pixel count, no-loss count, class imbalance ratio. |
| 🌊 Drift + Timelapse | Line chart of mean drift magnitude by year. Pixel timelapse — scatter map of sampled pixel locations colored by loss label. |
| 🔬 Drift Explorer | Year selector + violin/histogram of drift magnitude distribution, split by class. |

---

### Page 3 — Canada vs. Amazon Basin

Focused comparison of the two focus-region XGBoost models (PR-AUC 0.91 vs 0.62) to explore what the performance gap reveals about the data and the signal.

**Metrics & Confusion Matrices** — side-by-side columns with PR-AUC / F1 / Recall metric cards and Plotly confusion matrices for Canada and Amazon Basin.

**Feature Importance (XGBoost Gain)** — side-by-side horizontal bar charts of the top 15 features for each model.

**Expandable analysis sections:**
- *Why does Canada score 0.91 and Amazon only 0.62?* — narrative covering class balance, the fire year paradox, forest loss vs. deforestation definitions, feature importance as evidence, and cloud cover.
- *Best Optuna hyperparameters — Canada vs. Amazon Basin* — side-by-side parameter table with trial counts.

**Threshold Explorer** — AOI radio selector, probability score distribution histogram, interactive threshold slider (0.01–0.99), live confusion matrix, and precision / recall / F1 metric cards. Includes a Precision-Recall curve for the selected region.

---

### Page 4 — How I Built This

Two tabs: **🛠️ Build** and **💳 Credits**.

**Build tab:**

*Pipeline Architecture* — animated Plotly pipeline diagram (7 nodes, data-flow animation). A stage selector dropdown below the diagram shows a detail card for each pipeline step (GEE → Feature Engineering → Baseline Experiments → Optuna + MLflow → XGBoost Classifier → Model Evaluation → Streamlit + Docker).

*Build Timeline* — 8-day numbered SVG timeline cards (Day 1: GEE data acquisition through Day 8: CI/CD pipeline).

*Key Decisions & Lessons* — six expandable sections covering:
- Same pixel, different years — why the per-(pixel, year) framing beat single snapshots and LSTM sequences
- Why per-region models instead of one global model
- Drift magnitude as the primary engineered feature — why additional features didn't help
- Handling severe class imbalance (balanced weights, PR-AUC, Brier score)
- Geo-stratified train/test split — preventing spatial leakage
- Optuna over grid search — why Bayesian optimization fits this problem

*Source Code* — GitHub link button.

**Credits tab:** Data source attributions (AEF, Hansen GFC, RADD), library credits, and acknowledgements.

---

## Running the App

**Docker (recommended):**
```bash
docker compose up
# open http://localhost:8501
```

**Local:**
```bash
pip install -r app/requirements-app.txt
streamlit run app/streamlit_app.py
```

---

## Configuration

- **Theme:** `.streamlit/config.toml` — font, primary color, background. Fine-tune colors by running `app/theme_preview.py` locally.
- **Pipeline params:** `config/config.yaml` — AOIs, GEE export settings
- **Pre-computed artifacts:** `resources/cache/` — the app reads JSON metrics and parquet data samples at startup via `@st.cache_data`; no model inference happens at runtime. Regenerate with `python resources/precompute.py`.