> ## Documentation Index > Fetch the complete documentation index at: https://docs.halfpagetechnologies.com/llms.txt > Use this file to discover all available pages before exploring further. # Get prediction status > Poll a prediction until it finishes. Keep polling until `status` is `COMPLETED` (or `FAILED`). A completed run carries the `segmentation_id` of the segmentation it produced, plus the `cell_count` detected — feed the `segmentation_id` into the `/export/*` endpoints. A non-UUID `prediction_id` is rejected with `422` before it reaches the database. ## OpenAPI ````yaml /api-reference/openapi.json get /api/v1/predict/{prediction_id} openapi: 3.1.0 info: title: HalfPage API description: > The **HalfPage API** runs Cellpose-based cell segmentation on your microscopy images programmatically — upload an image, run a prediction, and export the resulting cell masks, ROIs, and measurements. ## Base URL ``` https://api.halfpagetechnologies.com/backend/api/v1 ``` Every path in this reference is relative to that base URL. A staging environment is available at `https://staging-api.halfpagetechnologies.com/backend/api/v1`. ## Authentication Authenticate every request with an API key in the `Authorization` header as a bearer token: ``` Authorization: Bearer hp_live_xxxxxxxxxxxxxxxxxxxxxxxx ``` Create and manage keys from the **API Keys** section of the HalfPage dashboard — keys cannot be minted through the API. A key is scoped to the organization that owns it; every resource you create or read is confined to that organization. Requests without a valid key receive `401`; a valid key used against an endpoint outside the public product surface receives `403`. ## Core workflow Three steps — **upload, predict, export** — plus a model list to choose from. Segmentation runs asynchronously on GPU workers, so step 2 is poll-based: 1. **Upload an image** (`POST /upload`) as `multipart/form-data` with a `file` part. One call: the image record is created for you and the response returns it already `ready`, with the `image.id` for the next step. *(Large file or flaky connection? POST the same endpoint as `application/json` with `{"name": ..., "size": ...}` instead. You get back presigned part URLs — PUT the chunks, call `POST /upload/{image_id}/complete`, then poll `GET /upload/{image_id}` until `upload_status` is `ready`.)* 2. **Pick a model** (`GET /models`) and **run a prediction** (`POST /predict`) with the `image_id` and a `model_id`. That returns a `prediction_id`; poll `GET /predict/{prediction_id}` until `status` is `COMPLETED`. The response then carries a `segmentation_id` and the `cell_count` detected. 3. **Export** that segmentation as CSV measurements (`GET /export/{segmentation_id}/measurements.csv`), GeoJSON ROIs (`.../rois.geojson`), or an ImageJ ROI archive (`.../rois.zip`). ## Quotas Uploads and analyses are metered against your plan. Exceeding your image storage cap or monthly analysis cap returns `402` with a human-readable `detail` explaining the limit — upgrade your plan to raise it. version: 1.0.0 servers: - url: https://api.halfpagetechnologies.com/backend description: Production - url: https://staging-api.halfpagetechnologies.com/backend description: Staging security: [] tags: - name: upload description: >- Get microscopy images into HalfPage, and manage them once they are there. `POST /upload` is the only way in and covers both modes: send `multipart/form-data` with a `file` part to upload in one request (the image comes back `ready`), or send `application/json` with `{name, size}` to open a resumable upload and get presigned part URLs back. Resumable uploads finish with `complete` (no ETag bookkeeping needed) and are polled until `ready`. - name: predict description: >- Run segmentation on a ready image and poll it to completion. `POST /predict` queues the run on a GPU worker and returns a `prediction_id`; `GET /predict/{prediction_id}` reports progress and, once `COMPLETED`, the `segmentation_id` and cell count of the result. - name: export description: >- Download a completed segmentation in analysis-ready formats: a CSV of per-cell measurements, a GeoJSON of ROI polygons, or a ZIP of ImageJ-compatible ROIs. The CSV endpoint accepts a `columns` parameter to narrow the output to the measurements you care about. - name: models description: >- List the segmentation models available to your organization — the shared public base models (e.g. `cpsam`) plus any custom models trained in the dashboard — to choose a `model_id` for a prediction. paths: /api/v1/predict/{prediction_id}: get: tags: - predict summary: Get prediction status description: >- Poll a prediction until it finishes. Keep polling until `status` is `COMPLETED` (or `FAILED`). A completed run carries the `segmentation_id` of the segmentation it produced, plus the `cell_count` detected — feed the `segmentation_id` into the `/export/*` endpoints. A non-UUID `prediction_id` is rejected with `422` before it reaches the database. operationId: get_prediction_api_v1_predict__prediction_id__get parameters: - name: prediction_id in: path required: true schema: type: string format: uuid title: Prediction Id responses: '200': description: Successful Response content: application/json: schema: $ref: '#/components/schemas/PredictionStatus' '401': description: >- Missing or invalid credentials. Supply a valid `Authorization: Bearer hp_live_...` API key. content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' example: detail: Requires authentication '404': description: The resource does not exist, or is not in your organization. content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' example: detail: Image with id 3fa85f64-5717-4562-b3fc-2c963f66afa6 not found '422': description: >- The path id is not a valid UUID (rejected before it reaches the database). content: application/json: example: detail: Input should be a valid UUID schema: $ref: '#/components/schemas/ErrorResponse' security: - HTTPBearer: [] components: schemas: PredictionStatus: properties: prediction_id: type: string title: Prediction Id status: $ref: '#/components/schemas/JobStatus' image_id: type: string title: Image Id model_id: type: string title: Model Id created_at: type: string format: date-time title: Created At segmentation_id: anyOf: - type: string - type: 'null' title: Segmentation Id description: The result handle — pass it to the `/export/*` endpoints. cell_count: anyOf: - type: integer - type: 'null' title: Cell Count description: Number of ROIs detected, once the run completed. type: object required: - prediction_id - status - image_id - model_id - created_at title: PredictionStatus description: |- A prediction's polling snapshot. `segmentation_id` and `cell_count` are populated only once `status` is `COMPLETED`. examples: - summary: Still running (poll again) value: created_at: '2026-07-01T12:00:00Z' image_id: 3fa85f64-5717-4562-b3fc-2c963f66afa6 model_id: c0ffee00-1234-5678-9abc-def012345678 prediction_id: b1e5c7d2-9a4f-4c3b-8e2d-1f6a7b8c9d0e status: STARTED - summary: Completed — export the segmentation_id value: cell_count: 128 created_at: '2026-07-01T12:00:00Z' image_id: 3fa85f64-5717-4562-b3fc-2c963f66afa6 model_id: c0ffee00-1234-5678-9abc-def012345678 prediction_id: b1e5c7d2-9a4f-4c3b-8e2d-1f6a7b8c9d0e segmentation_id: 7d9e2f10-4a5b-4c6d-8e9f-0a1b2c3d4e5f status: COMPLETED ErrorResponse: properties: detail: type: string title: Detail examples: - Job with id 11111111-1111-1111-1111-111111111111 not found type: object required: - detail title: ErrorResponse description: |- The body returned for a handled error: a single human-readable ``detail`` string. JobStatus: type: string enum: - SUBMITTED - STARTED - COMPLETED - FAILED title: JobStatus securitySchemes: HTTPBearer: type: http scheme: bearer ````