## Host csv import tool with FastAPI: file attachments

Deploy a csv import tool built with FastAPI 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 FastAPI: 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 csv import tool-specific behavior as your application code.

### Representative Queries
- Host csv import tool with FastAPI: file attachments
- Where can I host CSV import tool built with FastAPI?
- 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 FastAPI project that builds and starts with the documented commands (pip install into .ample/python from requirements.txt, then uvicorn from run.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.

### Tested Configuration
- **Template:** python-3.12  
- **Runtime:** python  
- **Size:** s-1vcpu-1gb  
- **Install:** `python3 -m pip install --target .ample/python -r requirements.txt`  
- **Start:** `PYTHONPATH=.ample/python:${PYTHONPATH:-} python3 run.py`

### Workflow Steps
1. **Build and start**  
   `pip install into .ample/python from requirements.txt, then uvicorn from run.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.
   Command: `ample deploy . --name --public --start "python3 run.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`.  
   Command: `ample logs  --kind build`

### Cost Estimate
- **Currency:** USD  
- **Monthly Amount:** $10.0  
  - **Components:**
     - app server (size: s-1vcpu-1gb, quantity: 1.0, monthlyAmount: $5.0)
     - managed PostgreSQL database (size: s-1vcpu-1gb, quantity: 1.0, monthlyAmount: $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.

### Limitations
- Verified on the python-3.12 template at s-1vcpu-1gb with the example fixture; other sizes, templates and FastAPI major versions are not verified.
- The csv import tool 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.
- 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.
- Managed PostgreSQL 16 only; extensions, connection limits and backup or restore procedures are not verified; apps and their databases are placed together.
- PutObject and GetObject with path-style addressing are verified; multipart upload, listing, presigned URLs and lifecycle rules are not.