## Host asset library with Flask: file attachments

### Summary
Deploy a asset library built with Flask on Ample using the browser file uploads 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: content-type and size validation that rejects disallowed files, an object written to a private bucket and a row linking the record to the object key (reject=ok upload=ok linked=ok). Not separately tested: your upload UI, allowed types, size limits and virus scanning; treat the asset library-specific behavior as your application code.

### Representative Queries
- Host asset library with Flask: file attachments
- Where can I host Asset library built with Flask?
- I need a restricted upload flow, file validation and record-to-object linkage.

### 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
Asset library

### Release Status
Published

### Support Status
Verified

### Execution Status
Ready

### 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 browser file uploads: content-type and size validation that rejects disallowed files, an object written to a private bucket and a row linking the record to the object key (reject=ok upload=ok linked=ok). 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.
   ```
   ample deploy . --name  --public --start "python3 app.py" --env S3_ENDPOINT=... --env S3_REGION=... --env S3_BUCKET=... --env S3_ACCESS_KEY_ID=... --env S3_SECRET_ACCESS_KEY=...
   ```
5. **Verify**  
   Fetch the live URL and the pattern self-test route(s) (/p/browser-file-uploads) from the example; then run your own checks. On failure read `ample logs  --kind build` then `--kind runtime`.
   ```
   ample logs  --kind build
   ```

### Cost Estimate
- **Currency:** USD  
- **Monthly Amount:** 10.0  
- **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.