feat: add function_name to each send_inference_request request (#186

)

add function_name to each send_inference_request

vision_agent/tools/tools.py

@@ -106,6 +106,7 @@ def grounding_dino(
  "visual_grounding" if model_size == "large" else "visual_grounding_tiny"
  ),
  "kwargs": {"box_threshold": box_threshold, "iou_threshold": iou_threshold},
+ "function_name": "grounding_dino",
  }
  data: Dict[str, Any] = send_inference_request(request_data, "tools")
  return_data = []
@@ -161,6 +162,7 @@ def owl_v2(
  "image": image_b64,
  "tool": "open_vocab_detection",
  "kwargs": {"box_threshold": box_threshold, "iou_threshold": iou_threshold},
+ "function_name": "owl_v2",
  }
  data: Dict[str, Any] = send_inference_request(request_data, "tools")
  return_data = []
@@ -225,6 +227,7 @@ def grounding_sam(
  "image": image_b64,
  "tool": "visual_grounding_segment",
  "kwargs": {"box_threshold": box_threshold, "iou_threshold": iou_threshold},
+ "function_name": "grounding_sam",
  }
  data: Dict[str, Any] = send_inference_request(request_data, "tools")
  return_data = []
@@ -364,6 +367,7 @@ def loca_zero_shot_counting(image: np.ndarray) -> Dict[str, Any]:
  data = {
  "image": image_b64,
  "tool": "zero_shot_counting",
+ "function_name": "loca_zero_shot_counting",
  }
  resp_data = send_inference_request(data, "tools")
  resp_data["heat_map"] = np.array(b64_to_pil(resp_data["heat_map"][0]))
@@ -399,6 +403,7 @@ def loca_visual_prompt_counting(
  "image": image_b64,
  "prompt": bbox_str,
  "tool": "few_shot_counting",
+ "function_name": "loca_visual_prompt_counting",
  }
  resp_data = send_inference_request(data, "tools")
  resp_data["heat_map"] = np.array(b64_to_pil(resp_data["heat_map"][0]))
@@ -428,6 +433,7 @@ def florencev2_roberta_vqa(prompt: str, image: np.ndarray) -> str:
  "image": image_b64,
  "prompt": prompt,
  "tool": "image_question_answering_with_context",
+ "function_name": "florencev2_roberta_vqa",
  }
 
  answer = send_inference_request(data, "tools")
@@ -457,6 +463,7 @@ def git_vqa_v2(prompt: str, image: np.ndarray) -> str:
  "image": image_b64,
  "prompt": prompt,
  "tool": "image_question_answering",
+ "function_name": "git_vqa_v2",
  }
 
  answer = send_inference_request(data, "tools")
@@ -487,6 +494,7 @@ def clip(image: np.ndarray, classes: List[str]) -> Dict[str, Any]:
  "prompt": ",".join(classes),
  "image": image_b64,
  "tool": "closed_set_image_classification",
+ "function_name": "clip",
  }
  resp_data = send_inference_request(data, "tools")
  resp_data["scores"] = [round(prob, 4) for prob in resp_data["scores"]]
@@ -514,6 +522,7 @@ def vit_image_classification(image: np.ndarray) -> Dict[str, Any]:
  data = {
  "image": image_b64,
  "tool": "image_classification",
+ "function_name": "vit_image_classification",
  }
  resp_data = send_inference_request(data, "tools")
  resp_data["scores"] = [round(prob, 4) for prob in resp_data["scores"]]
@@ -541,6 +550,7 @@ def vit_nsfw_classification(image: np.ndarray) -> Dict[str, Any]:
  data = {
  "image": image_b64,
  "tool": "nsfw_image_classification",
+ "function_name": "vit_nsfw_classification",
  }
  resp_data = send_inference_request(data, "tools")
  resp_data["scores"] = round(resp_data["scores"], 4)
@@ -567,6 +577,7 @@ def blip_image_caption(image: np.ndarray) -> str:
  data = {
  "image": image_b64,
  "tool": "image_captioning",
+ "function_name": "blip_image_caption",
  }
 
  answer = send_inference_request(data, "tools")
@@ -595,6 +606,7 @@ def florencev2_image_caption(image: np.ndarray, detail_caption: bool = True) ->
  "image": image_b64,
  "tool": "florence2_image_captioning",
  "detail_caption": detail_caption,
+ "function_name": "florencev2_image_caption",
  }
 
  answer = send_inference_request(data, "tools")
@@ -630,6 +642,7 @@ def florencev2_object_detection(image: np.ndarray) -> List[Dict[str, Any]]:
  data = {
  "image": image_b64,
  "tool": "object_detection",
+ "function_name": "florencev2_object_detection",
  }
 
  answer = send_inference_request(data, "tools")
@@ -686,6 +699,7 @@ def detr_segmentation(image: np.ndarray) -> List[Dict[str, Any]]:
  data = {
  "image": image_b64,
  "tool": "panoptic_segmentation",
+ "function_name": "detr_segmentation",
  }
 
  answer = send_inference_request(data, "tools")
@@ -728,6 +742,7 @@ def depth_anything_v2(image: np.ndarray) -> np.ndarray:
  data = {
  "image": image_b64,
  "tool": "generate_depth",
+ "function_name": "depth_anything_v2",
  }
 
  answer = send_inference_request(data, "tools")
@@ -759,6 +774,7 @@ def generate_soft_edge_image(image: np.ndarray) -> np.ndarray:
  data = {
  "image": image_b64,
  "tool": "generate_hed",
+ "function_name": "generate_soft_edge_image",
  }
 
  answer = send_inference_request(data, "tools")
@@ -791,6 +807,7 @@ def dpt_hybrid_midas(image: np.ndarray) -> np.ndarray:
  data = {
  "image": image_b64,
  "tool": "generate_normal",
+ "function_name": "dpt_hybrid_midas",
  }
 
  answer = send_inference_request(data, "tools")
@@ -822,6 +839,7 @@ def generate_pose_image(image: np.ndarray) -> np.ndarray:
  data = {
  "image": image_b64,
  "tool": "generate_pose",
+ "function_name": "generate_pose_image",
  }
 
  answer = send_inference_request(data, "tools")
@@ -862,6 +880,7 @@ def template_match(
  "image": image_b64,
  "template": template_image_b64,
  "tool": "template_match",
+ "function_name": "template_match",
  }
 
  answer = send_inference_request(data, "tools")