README.md

metadata

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

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.