add function name back

vision_agent/tools/tools.py

@@ -1,25 +1,28 @@
 import io
 import json
-import inspect
 import logging
 import tempfile
 from uuid import UUID
 from pathlib import Path
 from importlib import resources
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast
+from typing import Any, Dict, List, Optional, Tuple, Union, cast
 
 import cv2
 import requests
 import numpy as np
-import pandas as pd
 from pytube import YouTube # type: ignore
 from moviepy.editor import ImageSequenceClip
 from PIL import Image, ImageDraw, ImageFont
 from pillow_heif import register_heif_opener # type: ignore
 
 from vision_agent.clients.landing_public_api import LandingPublicAPI
 from vision_agent.tools.tool_types import BboxInput, BboxInputBase64
-from vision_agent.tools.tool_utils import send_inference_request
+from vision_agent.tools.tool_utils import (
+ send_inference_request,
+ get_tool_descriptions,
+ get_tool_documentation,
+ get_tools_df,
+)
 from vision_agent.utils import extract_frames_from_video
 from vision_agent.utils.execute import FileSerializer, MimeType
 from vision_agent.utils.image_utils import (
@@ -57,7 +60,6 @@
 ]
 _API_KEY = "land_sk_WVYwP00xA3iXely2vuar6YUDZ3MJT9yLX6oW5noUkwICzYLiDV"
 _OCR_URL = "https://app.landing.ai/ocr/v1/detect-text"
-logging.basicConfig(level=logging.INFO)
 _LOGGER = logging.getLogger(__name__)
 
 
@@ -108,6 +110,7 @@ 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 = []
@@ -163,6 +166,7 @@ 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 = []
@@ -227,6 +231,7 @@ 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 = []
@@ -366,6 +371,7 @@ 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]))
@@ -401,6 +407,7 @@ 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]))
@@ -430,6 +437,7 @@ 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")
@@ -459,6 +467,7 @@ 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")
@@ -489,6 +498,7 @@ 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"]]
@@ -516,6 +526,7 @@ 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"]]
@@ -543,6 +554,7 @@ 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)
@@ -569,6 +581,7 @@ 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")
@@ -597,6 +610,7 @@ 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")
@@ -632,6 +646,7 @@ 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")
@@ -673,7 +688,7 @@ def florencev2_fine_tuning(bboxes: List[Dict[str, Any]]) -> UUID:
  image=convert_to_b64(bbox_input.image),
  filename=bbox_input.filename,
  labels=bbox_input.labels,
- bboxes=bbox_input.bboxes
+ bboxes=bbox_input.bboxes,
  )
  for bbox_input in bboxes_input
  ]
@@ -724,6 +739,7 @@ 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")
@@ -766,6 +782,7 @@ 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")
@@ -797,6 +814,7 @@ 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")
@@ -829,6 +847,7 @@ 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")
@@ -900,6 +919,7 @@ def template_match(
  "image": image_b64,
  "template": template_image_b64,
  "tool": "template_match",
+ "function_name": "template_match",
  }
 
  answer = send_inference_request(data, "tools")
@@ -1259,50 +1279,6 @@ def overlay_heat_map(
  return np.array(combined)
 
 
-def get_tool_documentation(funcs: List[Callable[..., Any]]) -> str:
- docstrings = ""
- for func in funcs:
- docstrings += f"{func.__name__}{inspect.signature(func)}:\n{func.__doc__}\n\n"
-
- return docstrings
-
-
-def get_tool_descriptions(funcs: List[Callable[..., Any]]) -> str:
- descriptions = ""
- for func in funcs:
- description = func.__doc__
- if description is None:
- description = ""
-
- if "Parameters:" in description:
- description = (
- description[: description.find("Parameters:")]
- .replace("\n", " ")
- .strip()
- )
-
- description = " ".join(description.split())
- descriptions += f"- {func.__name__}{inspect.signature(func)}: {description}\n"
- return descriptions
-
-
-def get_tools_df(funcs: List[Callable[..., Any]]) -> pd.DataFrame:
- data: Dict[str, List[str]] = {"desc": [], "doc": []}
-
- for func in funcs:
- desc = func.__doc__
- if desc is None:
- desc = ""
- desc = desc[: desc.find("Parameters:")].replace("\n", " ").strip()
- desc = " ".join(desc.split())
-
- doc = f"{func.__name__}{inspect.signature(func)}:\n{func.__doc__}"
- data["desc"].append(desc)
- data["doc"].append(doc)
-
- return pd.DataFrame(data) # type: ignore
-
-
 TOOLS = [
  owl_v2,
  grounding_sam,