Host form collection with Flask: file attachments

Deploy a form collection 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 form collection-specific behavior as your application code.

Representative Queries

• Host form collection with Flask: file attachments
• Where can I host Form collection 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.

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
   

Limitations

• Verified on the python-3.12 template at s-1vcpu-1gb with the example fixture; other sizes, templates and Flask major versions are not verified.
• The form collection itself (your upload UI, allowed types, size limits and virus scanning) is application code and was not separately tested; the pattern checks are what was verified.

Success Checks

• App responds on its public URL with ample canary flask patterns
• Browser-file-uploads self-test confirms reject=ok upload=ok linked=ok.

Cost Estimate

• Currency: USD
• Monthly Amount: 10.0
• Basis: Price from pricing.toml (loaded by the API) at build revision 1ac5595375130d45290090e82ed0f554ccd45405-dirty
• Components:
  • App server: s-1vcpu-1gb - Monthly Amount: 5.0
  • Managed PostgreSQL database: s-1vcpu-1gb - Monthly Amount: 5.0

Next Actions

• Browse the catalog index
• Search published recipes by intent, stack and constraints
• Prepare a side-effect-free deployment plan for an authorized project