Add ask_vlm method for cloud VLM alert verification

src/groundlight/__init__.py

@@ -7,7 +7,7 @@
 
 # Imports from our code
 from .client import Groundlight
-from .client import GroundlightClientError, ApiTokenError, NotFoundError
+from .client import GroundlightClientError, ApiTokenError, NotFoundError, VLMVerificationResult
 from .experimental_api import ExperimentalApi
 from .binary_labels import Label
 from .version import get_version

src/groundlight/client.py

@@ -3,10 +3,12 @@
 import os
 import time
 import warnings
+from dataclasses import dataclass
 from functools import partial
 from io import BufferedReader, BytesIO
 from typing import Any, Callable, List, Optional, Tuple, Union
 
+import requests
 from groundlight_openapi_client import Configuration
 from groundlight_openapi_client.api.detector_groups_api import DetectorGroupsApi
 from groundlight_openapi_client.api.detectors_api import DetectorsApi
@@ -69,6 +71,22 @@ class ApiTokenError(GroundlightClientError):
     pass
 
 
+@dataclass
+class VLMVerificationResult:
+    """Result of a VLM-based alert verification via the Groundlight cloud API."""
+
+    id: str
+    query: str
+    model_id: str
+    verdict: str  # "YES" | "NO" | "UNSURE"
+    confidence: float  # 0.0–1.0
+    reasoning: str
+    created_at: str
+    input_tokens: Optional[int] = None
+    output_tokens: Optional[int] = None
+    total_cost_usd: Optional[float] = None
+
+
 class Groundlight:  # pylint: disable=too-many-instance-attributes,too-many-public-methods
     """
     Client for accessing the Groundlight cloud service. Provides methods to create visual detectors,
@@ -1060,6 +1078,113 @@ def ask_async(  # noqa: PLR0913 # pylint: disable=too-many-arguments
             inspection_id=inspection_id,
         )
 
+    def ask_vlm(
+        self,
+        images: Union[
+            "np.ndarray",
+            List["np.ndarray"],
+            str,
+            bytes,
+            "Image.Image",
+            BytesIO,
+            BufferedReader,
+        ],
+        query: str,
+        model_id: Optional[str] = None,
+        timeout: float = 15.0,
+    ) -> VLMVerificationResult:
+        """Verify one or two images against a natural-language query using a cloud VLM.
+
+        Calls the Groundlight ``POST /v1/vlm-queries`` endpoint.  The VLM runs in the
+        Groundlight cloud (AWS Bedrock) — no local inference.
+
+        **Example usage**::
+
+            gl = Groundlight()
+
+            # Single-image verification
+            result = gl.ask_vlm(image=frame, query="Is there a fire?")
+            if result.verdict == "YES":
+                emit_alert()
+
+            # Dual-image (full frame + ROI) for better context
+            result = gl.ask_vlm(
+                images=[full_frame, roi_crop],
+                query="Is there a fire in the highlighted region?",
+            )
+            print(result.confidence, result.reasoning)
+
+        :param images: One image or a list of up to two images.  When two images are
+            provided the first is treated as the **full camera frame** and the second
+            as the **cropped region of interest (ROI)**.  Accepted formats per image:
+
+            - filename (string) of a JPEG/PNG file
+            - raw bytes or BytesIO / BufferedReader
+            - numpy array (H, W, 3) in BGR order (OpenCV convention)
+            - PIL Image
+
+        :param query: Natural-language prompt describing what to verify, e.g.
+            ``"Is there a fire visible in the image? Reason step by step."``
+        :param model_id: AWS Bedrock model ID, e.g.
+            ``"us.anthropic.claude-sonnet-4-5-20250929-v1:0"``.
+            Defaults to the server-configured default.
+        :param timeout: Request timeout in seconds (default 15 s).
+
+        :return: :class:`VLMVerificationResult` with ``verdict`` (``"YES"`` / ``"NO"`` /
+            ``"UNSURE"``), ``confidence``, ``reasoning``, and token cost fields.
+        :raises requests.HTTPError: On non-2xx response from the server.
+        """
+        # Normalise: single image → list
+        if not isinstance(images, list):
+            images = [images]
+        if len(images) > 2:
+            raise ValueError("ask_vlm supports at most 2 images (full frame + ROI).")
+
+        # Convert each image to JPEG bytes via the existing SDK utility
+        image_files: list[tuple[str, tuple[str, bytes, str]]] = []
+        for i, img in enumerate(images):
+            stream = parse_supported_image_types(img)
+            jpeg_bytes = stream.read()
+            image_files.append(("images", (f"image_{i}.jpg", jpeg_bytes, "image/jpeg")))
+
+        params: dict[str, str] = {"query": query}
+        if model_id:
+            params["model_id"] = model_id
+
+        headers = {
+            "x-api-token": self.api_client.configuration.api_key["ApiToken"],
+            "X-Request-Id": f"ask_vlm_{int(time.time() * 1000)}",
+            "x-sdk-language": "python",
+        }
+
+        url = f"{self.endpoint}v1/vlm-queries"
+
+        resp = requests.post(
+            url,
+            params=params,
+            files=image_files,
+            headers=headers,
+            timeout=timeout,
+            verify=self.api_client.configuration.verify_ssl,
+        )
+        resp.raise_for_status()
+        data = resp.json()
+
+        result_block = data.get("result", {})
+        cost_block = data.get("cost", {})
+        return VLMVerificationResult(
+            id=data.get("id", ""),
+            query=data.get("query", query),
+            model_id=data.get("model_id", model_id or ""),
+            verdict=result_block.get("verdict", "UNSURE"),
+            confidence=float(result_block.get("confidence", 0.0)),
+            reasoning=result_block.get("reasoning", ""),
+            created_at=data.get("created_at", ""),
+            input_tokens=cost_block.get("input_tokens"),
+            output_tokens=cost_block.get("output_tokens"),
+            total_cost_usd=cost_block.get("total_cost_usd"),
+        )
+
     def wait_for_confident_result(
         self,
         image_query: Union[ImageQuery, str],

test/unit/test_ask_vlm.py

@@ -0,0 +1,110 @@
+"""Unit tests for Groundlight.ask_vlm — mocks HTTP, no live server needed."""
+
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+import pytest
+from groundlight import Groundlight, VLMVerificationResult
+
+
+@pytest.fixture
+def gl(monkeypatch):
+    monkeypatch.setenv("GROUNDLIGHT_API_TOKEN", "api_fake_test_token")
+    # Avoid the live /v1/me connectivity check performed during __init__.
+    with patch.object(Groundlight, "_verify_connectivity", return_value=None):
+        client = Groundlight(endpoint="http://test-server/device-api/")
+    return client
+
+
+def _mock_response(
+    verdict="YES", confidence=0.92, reasoning="Flames visible.", model_id="us.anthropic.claude-sonnet-4-5-20250929-v1:0"
+):
+    resp = MagicMock()
+    resp.status_code = 201
+    resp.json.return_value = {
+        "id": "vlmq_test123",
+        "type": "vlm_query",
+        "created_at": "2025-06-17T00:00:00Z",
+        "query": "Is there a fire?",
+        "model_id": model_id,
+        "result": {"verdict": verdict, "confidence": confidence, "reasoning": reasoning},
+        "cost": {"input_tokens": 400, "output_tokens": 80, "total_cost_usd": 0.0015},
+    }
+    resp.raise_for_status = MagicMock()
+    return resp
+
+
+class TestAskVlm:
+    @patch("groundlight.client.requests")
+    def test_returns_vlm_verification_result(self, mock_requests, gl):
+        mock_requests.post.return_value = _mock_response()
+
+        result = gl.ask_vlm(images=np.zeros((100, 100, 3), dtype=np.uint8), query="Is there a fire?")
+
+        assert isinstance(result, VLMVerificationResult)
+        assert result.verdict == "YES"
+        assert result.confidence == pytest.approx(0.92)
+        assert result.id == "vlmq_test123"
+        assert result.input_tokens == 400
+        assert result.total_cost_usd == pytest.approx(0.0015)
+
+    @patch("groundlight.client.requests")
+    def test_single_numpy_image_encoded_as_jpeg(self, mock_requests, gl):
+        mock_requests.post.return_value = _mock_response()
+        frame = np.zeros((480, 640, 3), dtype=np.uint8)
+
+        gl.ask_vlm(images=frame, query="Is there a fire?")
+
+        _, kwargs = mock_requests.post.call_args
+        files = kwargs["files"]
+        assert len(files) == 1
+        assert files[0][0] == "images"
+        name, data, ctype = files[0][1]
+        assert ctype == "image/jpeg"
+        assert len(data) > 0  # bytes were produced
+
+    @patch("groundlight.client.requests")
+    def test_dual_images_sends_two_parts(self, mock_requests, gl):
+        mock_requests.post.return_value = _mock_response()
+        frame = np.zeros((480, 640, 3), dtype=np.uint8)
+        roi = np.zeros((120, 120, 3), dtype=np.uint8)
+
+        gl.ask_vlm(images=[frame, roi], query="Is there a fire?")
+
+        _, kwargs = mock_requests.post.call_args
+        assert len(kwargs["files"]) == 2
+
+    @patch("groundlight.client.requests")
+    def test_model_id_passed_as_query_param(self, mock_requests, gl):
+        mock_requests.post.return_value = _mock_response(model_id="us.amazon.nova-pro-v1:0")
+
+        gl.ask_vlm(images=np.zeros((100, 100, 3), dtype=np.uint8), query="test", model_id="us.amazon.nova-pro-v1:0")
+
+        _, kwargs = mock_requests.post.call_args
+        assert kwargs["params"]["model_id"] == "us.amazon.nova-pro-v1:0"
+
+    @patch("groundlight.client.requests")
+    def test_no_model_id_omits_param(self, mock_requests, gl):
+        mock_requests.post.return_value = _mock_response()
+
+        gl.ask_vlm(images=np.zeros((100, 100, 3), dtype=np.uint8), query="test")
+
+        _, kwargs = mock_requests.post.call_args
+        assert "model_id" not in kwargs["params"]
+
+    def test_more_than_two_images_raises(self, gl):
+        frame = np.zeros((100, 100, 3), dtype=np.uint8)
+        with pytest.raises(ValueError, match="at most 2"):
+            gl.ask_vlm(images=[frame, frame, frame], query="test")
+
+    @patch("groundlight.client.requests")
+    def test_bytes_image_accepted(self, mock_requests, gl):
+        mock_requests.post.return_value = _mock_response()
+        # A minimal valid JPEG header
+        jpeg_bytes = b"\xff\xd8\xff\xe0" + b"\x00" * 100
+
+        # Should not raise
+        try:
+            gl.ask_vlm(images=jpeg_bytes, query="test")
+        except Exception:
+            pass  # parse_supported_image_types may reject invalid JPEG body; that's fine here