### Host recruiting tracker with Flask: CSV imports

Deploy a recruiting tracker 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 recruiting tracker-specific behavior as your application code.

#### Representative Queries
- Host recruiting tracker with Flask: CSV imports
- Where can I host Recruiting tracker 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

#### 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 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**  
   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 Keep private data in an unpublished bucket.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/bulk-import-staging) from the example; then run your own checks. On failure read `ample logs  --kind build` then `--kind runtime`.  
   ```
   ample logs  --kind build  
   ```

#### Examples
- **Flask pattern fixture**  
  Multi-pattern Flask app whose bulk import staging module was checked live; the module is under tests/deploy-canaries/_pattern-modules.

#### 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 recruiting tracker itself (your import format, mapping and conflict rules) is application code and was not separately tested; the pattern checks are what was verified.

#### 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: s-1vcpu-1gb, 1.0, 5.0  
    - Managed PostgreSQL database: s-1vcpu-1gb, 1.0, 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.

#### Next Actions
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