Add image caption tool (#52)

added image caption tool

vision_agent/agent/vision_agent.py

@@ -351,7 +351,7 @@ def __init__(
  task_model: Optional[Union[LLM, LMM]] = None,
  answer_model: Optional[Union[LLM, LMM]] = None,
  reflect_model: Optional[Union[LLM, LMM]] = None,
- max_retries: int = 3,
+ max_retries: int = 2,
  verbose: bool = False,
  report_progress_callback: Optional[Callable[[str], None]] = None,
  ):

vision_agent/tools/__init__.py

@@ -9,6 +9,7 @@
  ExtractFrames,
  GroundingDINO,
  GroundingSAM,
+ ImageCaption,
  SegArea,
  SegIoU,
  Tool,

vision_agent/tools/tools.py

@@ -144,6 +144,74 @@ def __call__(self, prompt: str, image: Union[str, ImageType]) -> Dict:
  return resp_json["data"] # type: ignore
 
 
+class ImageCaption(Tool):
+ r"""ImageCaption is a tool that can caption an image based on its contents
+ or tags.
+
+ Example
+ -------
+ >>> import vision_agent as va
+ >>> caption = va.tools.ImageCaption()
+ >>> caption("image1.jpg")
+ {'text': ['a box of orange and white socks']}
+ """
+
+ _ENDPOINT = "https://soi4ewr6fjqqdf5vuss6rrilee0kumxq.lambda-url.us-east-2.on.aws"
+
+ name = "image_caption_"
+ description = "'image_caption_' is a tool that can caption an image based on its contents or tags. It returns a text describing the image"
+ usage = {
+ "required_parameters": [
+ {"name": "image", "type": "str"},
+ ],
+ "examples": [
+ {
+ "scenario": "Can you describe this image ? Image name: cat.jpg",
+ "parameters": {"image": "cat.jpg"},
+ },
+ {
+ "scenario": "Can you caption this image with their main contents ? Image name: cat_dog.jpg",
+ "parameters": {"image": "cat_dog.jpg"},
+ },
+ {
+ "scenario": "Can you build me a image captioning tool ? Image name: shirts.jpg",
+ "parameters": {
+ "image": "shirts.jpg",
+ },
+ },
+ ],
+ }
+
+ # TODO: Add support for input multiple images, which aligns with the output type.
+ def __call__(self, image: Union[str, ImageType]) -> Dict:
+ """Invoke the Image captioning model.
+
+ Parameters:
+ image: the input image to caption.
+
+ Returns:
+ A list of dictionaries containing the labels and scores. Each dictionary contains the classification result for an image. E.g. [{"labels": ["red line", "yellow dot"], "scores": [0.98, 0.02]}]
+ """
+ image_b64 = convert_to_b64(image)
+ data = {
+ "image": image_b64,
+ "tool": "image_captioning",
+ }
+ res = requests.post(
+ self._ENDPOINT,
+ headers={"Content-Type": "application/json"},
+ json=data,
+ )
+ resp_json: Dict[str, Any] = res.json()
+ if (
+ "statusCode" in resp_json and resp_json["statusCode"] != 200
+ ) or "statusCode" not in resp_json:
+ _LOGGER.error(f"Request failed: {resp_json}")
+ raise ValueError(f"Request failed: {resp_json}")
+
+ return resp_json["data"] # type: ignore
+
+
 class GroundingDINO(Tool):
  r"""Grounding DINO is a tool that can detect arbitrary objects with inputs such as
  category names or referring expressions.
@@ -631,6 +699,7 @@ def __call__(self, equation: str) -> float:
  [
  NoOp,
  CLIP,
+ ImageCaption,
  GroundingDINO,
  AgentGroundingSAM,
  ExtractFrames,