# Host Approval Workflow with Flask: CSV Imports

Deploy an approval workflow built with Flask on Ample using the bulk import staging 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 CSV staged as an object, row-level validation with per-row errors recorded, and a restart-safe import keyed by import id (imported=2 rejected=1 restart=ok). Not separately tested: your import format, mapping and conflict rules; treat the approval workflow-specific behavior as your application code.

## Representative Queries
- Host approval workflow with Flask: CSV imports
- Where can I host Approval workflow built with Flask?
- I need a domain CSV format, row validation, deduplication and an import recovery path.

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

## Infrastructure Requirements
1. **Compute**  
   - Status: Verified
   - Summary: 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**  
   - Status: Verified  
   - 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.
   
3. **S3-compatible Object Storage**  
   - Status: Verified  
   - Summary: 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**  
   - Command: `pip install into .ample/python from requirements.txt, then waitress from app.py reading PORT on the python-3.12 template.`  
   - Note: The server must bind 0.0.0.0 on PORT.
   
2. **Implement the pattern on PostgreSQL**  
   - Description: The fixture's module implements bulk import staging: a CSV staged as an object, row-level validation with per-row errors recorded, and a restart-safe import keyed by import id (imported=2 rejected=1 restart=ok). Copy the approach into your schema; keep migrations idempotent and run them with --release-command.
   
3. **Wire Object Storage**  
   - Description: 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**  
   - Command: `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=...`
   - Note: 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**  
   - Command: `ample logs  --kind build`
   - Description: Fetch the live URL and the pattern self-test route(s) (/p/bulk-import-staging) from the example; then run your own checks. On failure read `ample logs  --kind build` then `--kind runtime`.

## Examples
- **Flask Pattern Fixture**  
  - Description: Multi-pattern Flask app whose bulk import staging module was checked live; the module is under tests/deploy-canaries/_pattern-modules.
  - Source Reference: `tests/deploy-canaries/flask-patterns`

## Success Checks
1. **App responds on its public URL**  
   - Kind: http_get  
   - Path: /  
   - Expectation: ample canary flask patterns
   
2. **Bulk-import-staging self-test**  
   - Kind: http_get  
   - Path: /p/bulk-import-staging  
   - Expectation: imported=2 rejected=1 restart=ok

## 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 approval workflow itself (your import format, mapping and conflict rules) is application code and was not separately tested; the pattern checks are what was verified.
- Region, compliance attestations and request-duration limits are unknown and not claimed.
- Apps auto-pause when idle and wake on the next request; always-on is an operator setting, not a plan feature.

## Cost Estimate
- Currency: USD
- Monthly Amount: 10.0
- Basis: size prices from pricing.toml (loaded by the API) at build revision 1ac5595375130d45290090e82ed0f554ccd45405-dirty
- Components:  
  - App Server: size s-1vcpu-1gb, quantity 1.0, monthly amount 5.0  
  - Managed PostgreSQL Database: size 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.