## Host approval workflow with Flask: private attachments

Deploy a approval workflow built with Flask on Ample using the private document library 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: a role-to-document access model with negative tests (401 without identity, 403 for the wrong role) and a private-bucket round-trip for the allowed role (private=ok). Not separately tested: your identity integration and role assignments; treat the approval workflow-specific behavior as your application code.

### Representative Queries
- Host approval workflow with Flask: private attachments
- Where can I host Approval workflow built with Flask?
- I need attachment metadata, access checks and a tested private object access path.

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

### Infrastructure Requirements
#### Compute
- **Label:** Compute
- **Summary:** Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.

#### Postgres
- **Label:** Postgres
- **Summary:** Managed PostgreSQL 16 runs in its own microVM and is auto-provisioned when an app needs a database and no DATABASE_URL is supplied.

#### S3-compatible object storage
- **Label:** S3-compatible object storage
- **Summary:** Buckets are S3-compatible with issued credentials; PutObject and GetObject are verified by canary. Other S3 operations are not verified.

### Execution Status
- **Framework:** Flask
- **Workload:** Approval workflow
- **Release Status:** published
- **Support Status:** verified
- **Execution Status:** ready

### Prerequisites
1. A Flask project that builds and starts with the documented commands.
2. A PostgreSQL driver reading DATABASE_URL at runtime.
3. A bucket from `ample bucket create` with its credentials passed as encrypted S3_* environment variables.
4. 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 private document library: a role-to-document access model with negative tests (401 without identity, 403 for the wrong role) and a private-bucket round-trip for the allowed role (private=ok).
3. **Wire object storage**  
   Create the bucket(s), then pass endpoint, region, bucket and keys as --env values. Use path-style addressing.
4. **Deploy**  
   Run the synchronous deploy once and read the result.
5. **Verify**  
   Fetch the live URL and the pattern self-test route(s).

### Cost Estimate
- **Currency:** USD
- **Monthly Amount:** 10.0
- **Basis:** Size prices from pricing.toml (loaded by the API).
- **Components:**
  - App server: s-1vcpu-1gb, quantity: 1.0, monthly amount: 5.0
  - Managed PostgreSQL database: s-1vcpu-1gb, quantity: 1.0, monthly amount: 5.0
  
**Note:** Always-on monthly price of the tested sizes; apps and databases auto-pause when idle. Buckets are allocation-priced per quota and not included.