adding vqa tool for agent and also for general purposes

vision_agent/llm/llm.py

@@ -132,6 +132,12 @@ def generate_zero_shot_counter(self, question: str) -> Callable:
  return lambda x: ZeroShotCounting()(**{"image": x})
 
 
+def generate_image_qa_tool(self, question: str) -> Callable:
+ from vision_agent.tools import ImageQuestionAnswering
+
+ return lambda x: ImageQuestionAnswering()(**{"prompt": question, "image": x})
+
+
 class AzureOpenAILLM(OpenAILLM):
  def __init__(
  self,

vision_agent/lmm/lmm.py

@@ -11,11 +11,7 @@
 
 from vision_agent.tools import (
  CHOOSE_PARAMS,
- CLIP,
  SYSTEM_PROMPT,
- GroundingDINO,
- GroundingSAM,
- ZeroShotCounting,
 )
 
 _LOGGER = logging.getLogger(__name__)
@@ -205,6 +201,8 @@ def generate(
  return cast(str, response.choices[0].message.content)
 
  def generate_classifier(self, question: str) -> Callable:
+ from vision_agent.tools import CLIP
+
  api_doc = CLIP.description + "\n" + str(CLIP.usage)
  prompt = CHOOSE_PARAMS.format(api_doc=api_doc, question=question)
  response = self.client.chat.completions.create(
@@ -228,6 +226,8 @@ def generate_classifier(self, question: str) -> Callable:
  return lambda x: CLIP()(**{"prompt": params["prompt"], "image": x})
 
  def generate_detector(self, question: str) -> Callable:
+ from vision_agent.tools import GroundingDINO
+
  api_doc = GroundingDINO.description + "\n" + str(GroundingDINO.usage)
  prompt = CHOOSE_PARAMS.format(api_doc=api_doc, question=question)
  response = self.client.chat.completions.create(
@@ -251,6 +251,8 @@ def generate_detector(self, question: str) -> Callable:
  return lambda x: GroundingDINO()(**{"prompt": params["prompt"], "image": x})
 
  def generate_segmentor(self, question: str) -> Callable:
+ from vision_agent.tools import GroundingSAM
+
  api_doc = GroundingSAM.description + "\n" + str(GroundingSAM.usage)
  prompt = CHOOSE_PARAMS.format(api_doc=api_doc, question=question)
  response = self.client.chat.completions.create(
@@ -274,8 +276,15 @@ def generate_segmentor(self, question: str) -> Callable:
  return lambda x: GroundingSAM()(**{"prompt": params["prompt"], "image": x})
 
  def generate_zero_shot_counter(self, question: str) -> Callable:
+ from vision_agent.tools import ZeroShotCounting
+
  return lambda x: ZeroShotCounting()(**{"image": x})
 
+ def generate_image_qa_tool(self, question: str) -> Callable:
+ from vision_agent.tools import ImageQuestionAnswering
+
+ return lambda x: ImageQuestionAnswering()(**{"prompt": question, "image": x})
+
 
 class AzureOpenAILMM(OpenAILMM):
  def __init__(

vision_agent/tools/__init__.py

@@ -12,10 +12,12 @@
  GroundingDINO,
  GroundingSAM,
  ImageCaption,
+ ZeroShotCounting,
+ VisualPromptCounting,
+ VisualQuestionAnswering,
+ ImageQuestionAnswering,
  SegArea,
  SegIoU,
  Tool,
- VisualPromptCounting,
- ZeroShotCounting,
  register_tool,
 )

vision_agent/tools/tools.py

@@ -19,6 +19,7 @@
 )
 from vision_agent.tools.video import extract_frames_from_video
 from vision_agent.type_defs import LandingaiAPIKey
+from vision_agent.lmm import OpenAILMM
 
 _LOGGER = logging.getLogger(__name__)
 _LND_API_KEY = LandingaiAPIKey().api_key
@@ -502,7 +503,7 @@ class ZeroShotCounting(Tool):
 
  # 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.
+ """Invoke the Zero shot counting model.
 
  Parameters:
  image: the input image.
@@ -566,7 +567,7 @@ class VisualPromptCounting(Tool):
 
  # TODO: Add support for input multiple images, which aligns with the output type.
  def __call__(self, image: Union[str, ImageType], prompt: str) -> Dict:
- """Invoke the Image captioning model.
+ """Invoke the few shot counting model.
 
  Parameters:
  image: the input image.
@@ -587,6 +588,144 @@ def __call__(self, image: Union[str, ImageType], prompt: str) -> Dict:
  return _send_inference_request(data, "tools")
 
 
+class VisualQuestionAnswering(Tool):
+ r"""VisualQuestionAnswering is a tool that can explain contents of an image and answer questions about the same
+
+ Example
+ -------
+ >>> import vision_agent as va
+ >>> vqa_tool = va.tools.VisualQuestionAnswering()
+ >>> vqa_tool(image="image1.jpg", prompt="describe this image in detail")
+ {'text': "The image contains a cat sitting on a table with a bowl of milk."}
+ """
+
+ name = "visual_question_answering_"
+ description = "'visual_question_answering_' is a tool that can describe the contents of the image and it can also answer basic questions about the image."
+
+ usage = {
+ "required_parameters": [
+ {"name": "image", "type": "str"},
+ {"name": "prompt", "type": "str"},
+ ],
+ "examples": [
+ {
+ "scenario": "Describe this image in detail. Image name: cat.jpg",
+ "parameters": {
+ "image": "cats.jpg",
+ "prompt": "Describe this image in detail",
+ },
+ },
+ {
+ "scenario": "Can you help me with this street sign in this image ? What does it say ? Image name: sign.jpg",
+ "parameters": {
+ "image": "sign.jpg",
+ "prompt": "Can you help me with this street sign ? What does it say ?",
+ },
+ },
+ {
+ "scenario": "Describe the weather in the image for me ? Image name: weather.jpg",
+ "parameters": {
+ "image": "weather.jpg",
+ "prompt": "Describe the weather in the image for me ",
+ },
+ },
+ {
+ "scenario": "Which 2 are the least frequent bins in this histogram ? Image name: chart.jpg",
+ "parameters": {
+ "image": "chart.jpg",
+ "prompt": "Which 2 are the least frequent bins in this histogram",
+ },
+ },
+ ],
+ }
+
+ def __call__(self, image: Union[str, ImageType], prompt: str) -> Dict:
+ """Invoke the visual question answering model.
+
+ Parameters:
+ image: the input image.
+
+ Returns:
+ A dictionary containing the key 'text' and the answer to the prompt. E.g. {'text': 'This image contains a cat sitting on a table with a bowl of milk.'}
+ """
+
+ gpt = OpenAILMM()
+ return gpt(input=prompt, images=[image])
+
+
+class ImageQuestionAnswering(Tool):
+ r"""ImageQuestionAnswering is a tool that can explain contents of an image and answer questions about the same
+ It is same as VisualQuestionAnswering but this tool is not used by agents. It is used when user requests a tool for VQA using generate_image_qa_tool function.
+ It is also useful if the user wants the data to be not exposed to OpenAI endpoints
+
+ Example
+ -------
+ >>> import vision_agent as va
+ >>> vqa_tool = va.tools.ImageQuestionAnswering()
+ >>> vqa_tool(image="image1.jpg", prompt="describe this image in detail")
+ {'text': "The image contains a cat sitting on a table with a bowl of milk."}
+ """
+
+ name = "image_question_answering_"
+ description = "'image_question_answering_' is a tool that can describe the contents of the image and it can also answer basic questions about the image."
+
+ usage = {
+ "required_parameters": [
+ {"name": "image", "type": "str"},
+ {"name": "prompt", "type": "str"},
+ ],
+ "examples": [
+ {
+ "scenario": "Describe this image in detail. Image name: cat.jpg",
+ "parameters": {
+ "image": "cats.jpg",
+ "prompt": "Describe this image in detail",
+ },
+ },
+ {
+ "scenario": "Can you help me with this street sign in this image ? What does it say ? Image name: sign.jpg",
+ "parameters": {
+ "image": "sign.jpg",
+ "prompt": "Can you help me with this street sign ? What does it say ?",
+ },
+ },
+ {
+ "scenario": "Describe the weather in the image for me ? Image name: weather.jpg",
+ "parameters": {
+ "image": "weather.jpg",
+ "prompt": "Describe the weather in the image for me ",
+ },
+ },
+ {
+ "scenario": "Can you generate an image question answering tool ? Image name: chart.jpg, prompt: Which 2 are the least frequent bins in this histogram",
+ "parameters": {
+ "image": "chart.jpg",
+ "prompt": "Which 2 are the least frequent bins in this histogram",
+ },
+ },
+ ],
+ }
+
+ def __call__(self, image: Union[str, ImageType], prompt: str) -> Dict:
+ """Invoke the visual question answering model.
+
+ Parameters:
+ image: the input image.
+
+ Returns:
+ A dictionary containing the key 'text' and the answer to the prompt. E.g. {'text': 'This image contains a cat sitting on a table with a bowl of milk.'}
+ """
+
+ image_b64 = convert_to_b64(image)
+ data = {
+ "image": image_b64,
+ "prompt": prompt,
+ "tool": "image_question_answering",
+ }
+
+ return _send_inference_request(data, "tools")
+
+
 class Crop(Tool):
  r"""Crop crops an image given a bounding box and returns a file name of the cropped image."""
 
@@ -944,11 +1083,11 @@ def __call__(self, equation: str) -> float:
  [
  NoOp,
  CLIP,
- ImageCaption,
  GroundingDINO,
  AgentGroundingSAM,
  ZeroShotCounting,
  VisualPromptCounting,
+ VisualQuestionAnswering,
  AgentDINOv,
  ExtractFrames,
  Crop,