### Host appointment booking with Flask: data exports

Deploy a appointment booking built with Flask on Ample using the generated downloads pattern. Compute runs the app in an isolated microVM behind a public HTTPS URL, a managed PostgreSQL 16 database is auto-provisioned and injected as DATABASE_URL, a private S3-compatible bucket holds objects with credentials delivered as encrypted environment variables. Verified on Flask: an export query rendered to a CSV artifact stored with metadata in a private bucket and returned only to an authorized caller (401 without token, export=ok rows=3). Not separately tested: your export formats, retention policy and entitlement model; treat the appointment booking-specific behavior as your application code.

#### Representative Queries
- Host appointment booking with Flask: data exports
- Where can I host Appointment booking built with Flask?
- I need a scoped export format, artifact generation and authorized download procedure.

#### Resource Requirements
- primitive:compute
- primitive:postgres
- primitive:s3-compatible-object-storage

#### Infrastructure Requirements
1. **Compute**  
   Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.
2. **Postgres**  
   Managed PostgreSQL 16 runs in its own microVM and is auto-provisioned when an app needs a database and no DATABASE_URL is supplied.
3. **S3-compatible object storage**  
   Buckets are S3-compatible with issued credentials; PutObject and GetObject are verified by canary. Other S3 operations are not verified.

#### Framework
- Flask

#### Workload
- Appointment booking

#### Prerequisites
- A Flask project that builds and starts with the documented commands (pip install into .ample/python from requirements.txt, then waitress from app.py reading PORT on the python-3.12 template)
- A PostgreSQL driver reading DATABASE_URL at runtime (auto-provisioned when omitted, or supplied with --env)
- A bucket from `ample bucket create` with its credentials passed as encrypted S3_* environment variables
- An Ample account token with servers:write, databases:read, buckets:read

#### Workflow Steps
1. **Build and start**  
   pip install into .ample/python from requirements.txt, then waitress from app.py reading PORT on the python-3.12 template. The server must bind 0.0.0.0 on PORT.
2. **Implement the pattern on PostgreSQL**  
   The fixture's module implements generated downloads: an export query rendered to a CSV artifact stored with metadata in a private bucket and returned only to an authorized caller (401 without token, export=ok rows=3). Copy the approach into your schema; keep migrations idempotent and run them with --release-command.
3. **Wire object storage**  
   Create the bucket(s), then pass endpoint, region, bucket and keys as --env values. Use path-style addressing. Keep private data in an unpublished bucket.
4. **Deploy**  
   Run the synchronous deploy once and read the result (exit 0 live, 1 failed, 2 blocked). Re-running with no change is a no-op.
5. **Verify**  
   Fetch the live URL and the pattern self-test route(s) (/p/generated-downloads) from the example; then run your own checks. On failure read `ample logs  --kind build` then `--kind runtime`.

#### Tested Configuration
- **Template:** python-3.12  
- **Runtime:** python  
- **Size:** s-1vcpu-1gb  
- **Install:** python3 -m pip install --target .ample/python -r requirements.txt  
- **Start:** PYTHONPATH=.ample/python:${PYTHONPATH:-} python3 app.py

#### Success Checks
- app responds on its public URL
- generated-downloads self-test

#### Limitations
- Verified on the python-3.12 template at s-1vcpu-1gb with the example fixture; other sizes and templates are not verified.

### Cost Estimate
- **Monthly Amount:** $10.00
- **Note:** Always-on monthly price of the tested sizes; apps and databases auto-pause when idle.