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| title: Mission 17 AI | |
| emoji: 🤖 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: docker | |
| app_port: 7860 | |
| pinned: false | |
| # Mission17-AI | |
| Mission17-AI is a Flask microservice for civic-task photo classification and duplicate-photo screening. It is deployed as a Hugging Face Docker Space and can be called only by the Mission17 backend using a service token. | |
| ## What it does | |
| - Uses a TensorFlow CNN (`mission_model.h5`) to classify supported civic-task proof images. | |
| - Maps model results to SDG mission verdicts. | |
| - Rejects exact duplicate photos and screens bounded candidate sets for near duplicates using pHash and dHash. | |
| - Accepts PNG, JPG, JPEG, and WebP images up to 5 MB. | |
| - Validates image content with Pillow before classification. | |
| ## Security and storage | |
| - `/predict` requires `Authorization: Bearer <AI_SERVICE_TOKEN>`. | |
| - `ANTICHEAT_MONGO_URI` must use a separate least-privilege MongoDB user restricted to the anti-cheat database. | |
| - The anti-cheat collection stores only perceptual hashes and minimal non-personal metadata. It does not store submitted images, email addresses, names, Firebase UIDs, or tokens. | |
| - If durable anti-cheat storage is unavailable, the service returns `UNCERTAIN`; it must not approve a photo without duplicate screening. | |
| - Near-duplicate checks use indexed candidate buckets to keep queries bounded. This is an approximate screen and may require human review for ambiguous cases. | |
| ## Required environment variables | |
| | Variable | Purpose | | |
| | --- | --- | | |
| | `AI_SERVICE_TOKEN` | Shared secret required for backend access to `/predict`. | | |
| | `ANTICHEAT_MONGO_URI` | MongoDB URI for the restricted anti-cheat database user. | | |
| | `ANTICHEAT_DB_NAME` | Optional database name; defaults to `mission17_anticheat`. | | |
| | `ANTICHEAT_COLLECTION` | Optional collection name; defaults to `photo_hashes`. | | |
| Never commit real values for these variables. | |
| ## Local setup | |
| ```bash | |
| python -m venv venv | |
| venv\Scripts\activate | |
| pip install -r requirements.txt | |
| pip install -r requirements-dev.txt | |
| python test_anticheat.py | |
| python test_app.py | |
| python app.py | |
| ``` | |
| The service listens on `http://localhost:7860`. | |
| ## Endpoints | |
| | Endpoint | Access | Behavior | | |
| | --- | --- | --- | | |
| | `GET /health` | Public | Reports service and anti-cheat storage readiness without exposing configuration. | | |
| | `POST /predict` | Backend service token | Returns a verified, rejected, or uncertain photo-analysis result. | | |
| ## Model evidence and limitations | |
| - The saved model has 10 output classes, matching the 10 labels in `labels.txt`. | |
| - Public accuracy, precision, recall, and F1 claims are **unverified** until `scripts/training/evaluate_model.py` is run against the documented held-out dataset and its outputs are retained as evidence. | |
| - The model can make false positive and false negative classifications. Low-confidence, unavailable, and ambiguous anti-cheat results require human review. | |
| - Perceptual hashes help identify reused/recompressed images but are not proof of fraud by themselves. | |