| title: Recipe Health Analyzer | |
| emoji: 🥗 | |
| colorFrom: green | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: "6.9.0" | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: AI pipeline that classifies recipe health from text or audio | |
| # 🥗 Recipe Health Analyzer | |
| An end-to-end AI pipeline that analyzes spoken or written food recipes and classifies them as **Healthy**, **Moderately Healthy**, or **Unhealthy** — with full SHAP-based explainability. | |
| ## Pipeline stages | |
| 1. **Speech recognition** — OpenAI Whisper transcribes audio input | |
| 2. **NLP extraction** — spaCy dependency parsing extracts ingredients, quantities, and cooking methods | |
| 3. **Nutrition mapping** — USDA FoodData Central API maps each ingredient to its nutritional profile | |
| 4. **Health classification** — RandomForest / XGBoost trained on nutritional features | |
| 5. **Explainability** — SHAP values + natural language reasons + actionable suggestions | |
| ## Setup | |
| Set your `USDA_API_KEY` in Space Secrets (Settings → Variables and secrets). | |
| Get a free key at [fdc.nal.usda.gov/api-key-signup.html](https://fdc.nal.usda.gov/api-key-signup.html). | |
| Without a key the app uses `DEMO_KEY` which is rate-limited to ~30 req/hour. | |
| ## Tech stack | |
| `spaCy` · `openai-whisper` · `scikit-learn` · `xgboost` · `shap` · `gradio` | |