From eb101f00a8f00cae88c9189ca535cee7ccbf69d8 Mon Sep 17 00:00:00 2001
From: Asia <92344512+AsiaCao@users.noreply.github.com>
Date: Mon, 22 Apr 2024 13:52:23 -0700
Subject: [PATCH 1/5] Add a new tool: determine if a bbox is contained within
 another bbox (#59)

* Add a new bounding box contains tool

* Fix format
---
 vision_agent/agent/vision_agent.py |  5 ++-
 vision_agent/tools/tools.py        | 69 +++++++++++++++++++++++++-----
 2 files changed, 62 insertions(+), 12 deletions(-)

diff --git a/vision_agent/agent/vision_agent.py b/vision_agent/agent/vision_agent.py
index bbd2c1a5..3287a174 100644
--- a/vision_agent/agent/vision_agent.py
+++ b/vision_agent/agent/vision_agent.py
@@ -33,6 +33,7 @@
 
 logging.basicConfig(stream=sys.stdout)
 _LOGGER = logging.getLogger(__name__)
+_MAX_TABULATE_COL_WIDTH = 80
 
 
 def parse_json(s: str) -> Any:
@@ -614,7 +615,7 @@ def retrieval(
 
         self.log_progress(
             f"""Going to run the following tool(s) in sequence:
-{tabulate([tool_results], headers="keys", tablefmt="mixed_grid")}"""
+{tabulate(tabular_data=[tool_results], headers="keys", tablefmt="mixed_grid", maxcolwidths=_MAX_TABULATE_COL_WIDTH)}"""
         )
 
         def parse_tool_results(result: Dict[str, Union[Dict, List]]) -> Any:
@@ -660,6 +661,6 @@ def create_tasks(
             task_list = []
         self.log_progress(
             f"""Planned tasks:
-{tabulate(task_list, headers="keys", tablefmt="mixed_grid")}"""
+{tabulate(task_list, headers="keys", tablefmt="mixed_grid", maxcolwidths=_MAX_TABULATE_COL_WIDTH)}"""
         )
         return task_list
diff --git a/vision_agent/tools/tools.py b/vision_agent/tools/tools.py
index 6d2a7b47..7657a362 100644
--- a/vision_agent/tools/tools.py
+++ b/vision_agent/tools/tools.py
@@ -250,7 +250,7 @@ def __call__(
             iou_threshold: the threshold for intersection over union used in nms algorithm. It will suppress the boxes which have iou greater than this threshold.
 
         Returns:
-            A list of dictionaries containing the labels, scores, and bboxes. Each dictionary contains the detection result for an image.
+            A dictionary containing the labels, scores, and bboxes, which is the detection result for the input image.
         """
         image_size = get_image_size(image)
         image_b64 = convert_to_b64(image)
@@ -346,7 +346,7 @@ def __call__(
             iou_threshold: the threshold for intersection over union used in nms algorithm. It will suppress the boxes which have iou greater than this threshold.
 
         Returns:
-            A list of dictionaries containing the labels, scores, bboxes and masks. Each dictionary contains the segmentation result for an image.
+            A dictionary containing the labels, scores, bboxes and masks for the input image.
         """
         image_size = get_image_size(image)
         image_b64 = convert_to_b64(image)
@@ -357,19 +357,15 @@ def __call__(
             "kwargs": {"box_threshold": box_threshold, "iou_threshold": iou_threshold},
         }
         data: Dict[str, Any] = _send_inference_request(request_data, "tools")
-        ret_pred: Dict[str, List] = {"labels": [], "bboxes": [], "masks": []}
         if "bboxes" in data:
-            ret_pred["bboxes"] = [
-                normalize_bbox(box, image_size) for box in data["bboxes"]
-            ]
+            data["bboxes"] = [normalize_bbox(box, image_size) for box in data["bboxes"]]
         if "masks" in data:
-            ret_pred["masks"] = [
+            data["masks"] = [
                 rle_decode(mask_rle=mask, shape=data["mask_shape"])
                 for mask in data["masks"]
             ]
-        ret_pred["labels"] = data["labels"]
-        ret_pred["scores"] = data["scores"]
-        return ret_pred
+        data.pop("mask_shape", None)
+        return data
 
 
 class DINOv(Tool):
@@ -643,6 +639,58 @@ def __call__(self, mask1: Union[str, Path], mask2: Union[str, Path]) -> float:
         return cast(float, round(iou, 2))
 
 
+class BboxContains(Tool):
+    name = "bbox_contains_"
+    description = "Given two bounding boxes, a target bounding box and a region bounding box, 'bbox_contains_' returns the intersection of the two bounding boxes over the target bounding box, reflects the percentage area of the target bounding box overlaps with the region bounding box. This is a good tool for determining if the region object contains the target object."
+    usage = {
+        "required_parameters": [
+            {"name": "target", "type": "List[int]"},
+            {"name": "target_class", "type": "str"},
+            {"name": "region", "type": "List[int]"},
+            {"name": "region_class", "type": "str"},
+        ],
+        "examples": [
+            {
+                "scenario": "Determine if the dog on the couch, bounding box of the dog: [0.2, 0.21, 0.34, 0.42], bounding box of the couch: [0.3, 0.31, 0.44, 0.52]",
+                "parameters": {
+                    "target": [0.2, 0.21, 0.34, 0.42],
+                    "target_class": "dog",
+                    "region": [0.3, 0.31, 0.44, 0.52],
+                    "region_class": "couch",
+                },
+            },
+            {
+                "scenario": "Check if the kid is in the pool? bounding box of the kid: [0.2, 0.21, 0.34, 0.42], bounding box of the pool: [0.3, 0.31, 0.44, 0.52]",
+                "parameters": {
+                    "target": [0.2, 0.21, 0.34, 0.42],
+                    "target_class": "kid",
+                    "region": [0.3, 0.31, 0.44, 0.52],
+                    "region_class": "pool",
+                },
+            },
+        ],
+    }
+
+    def __call__(
+        self, target: List[int], target_class: str, region: List[int], region_class: str
+    ) -> Dict[str, Union[str, float]]:
+        x1, y1, x2, y2 = target
+        x3, y3, x4, y4 = region
+        xA = max(x1, x3)
+        yA = max(y1, y3)
+        xB = min(x2, x4)
+        yB = min(y2, y4)
+        inter_area = max(0, xB - xA) * max(0, yB - yA)
+        boxa_area = (x2 - x1) * (y2 - y1)
+        iou = inter_area / float(boxa_area)
+        area = round(iou, 2)
+        return {
+            "target_class": target_class,
+            "region_class": region_class,
+            "intersection": area,
+        }
+
+
 class BoxDistance(Tool):
     name = "box_distance_"
     description = (
@@ -757,6 +805,7 @@ def __call__(self, equation: str) -> float:
             SegArea,
             BboxIoU,
             SegIoU,
+            BboxContains,
             BoxDistance,
             Calculator,
         ]

From a860ebedfa7cc0835c2e8c0d93324f2472a4ed28 Mon Sep 17 00:00:00 2001
From: GitHub Actions Bot <yazhou.cao@landing.ai>
Date: Mon, 22 Apr 2024 20:56:25 +0000
Subject: [PATCH 2/5] [skip ci] chore(release): vision-agent 0.1.5

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 14b1519c..0670265f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "vision-agent"
-version = "0.1.4"
+version = "0.1.5"
 description = "Toolset for Vision Agent"
 authors = ["Landing AI <dev@landing.ai>"]
 readme = "README.md"

From 5f11aea79387e75eb59fad35bf500ef3dfbf7bd2 Mon Sep 17 00:00:00 2001
From: Shankar <90070882+shankar-landing-ai@users.noreply.github.com>
Date: Mon, 22 Apr 2024 14:09:48 -0700
Subject: [PATCH 3/5] Add Count tools (#56)

* Adding counting tools to vision agent

* fixed heatmap overlay and addressesessed PR comments

* adding the counting tool to take both absolute coordinate and normalized coordinates, refactoring code, adding llm generate counter tool

* fix linting
---
 README.md                          |   5 +-
 vision_agent/agent/vision_agent.py |  43 +++++---
 vision_agent/image_utils.py        | 101 +++++++++++++++++-
 vision_agent/llm/llm.py            |   4 +
 vision_agent/lmm/lmm.py            |   4 +
 vision_agent/tools/__init__.py     |   2 +
 vision_agent/tools/tools.py        | 163 +++++++++++++++++++++++------
 7 files changed, 273 insertions(+), 49 deletions(-)

diff --git a/README.md b/README.md
index 741b8ff2..c09e5823 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,7 @@
 
 Vision Agent is a library that helps you utilize agent frameworks for your vision tasks.
 Many current vision problems can easily take hours or days to solve, you need to find the
-right model, figure out how to use it, possibly write programming logic around it to 
+right model, figure out how to use it, possibly write programming logic around it to
 accomplish the task you want or even more expensive, train your own model. Vision Agent
 aims to provide an in-seconds experience by allowing users to describe their problem in
 text and utilizing agent frameworks to solve the task for them. Check out our discord
@@ -108,6 +108,9 @@ you. For example:
 | BboxIoU | BboxIoU returns the intersection over union of two bounding boxes normalized to 2 decimal places. |
 | SegIoU | SegIoU returns the intersection over union of two segmentation masks normalized to 2 decimal places. |
 | ExtractFrames | ExtractFrames extracts frames with motion from a video. |
+| ExtractFrames | ExtractFrames extracts frames with motion from a video. |
+| ZeroShotCounting | ZeroShotCounting returns the total number of objects belonging to a single class in a given image |
+| VisualPromptCounting | VisualPromptCounting returns the total number of objects belonging to a single class given an image and visual prompt |
 
 
 It also has a basic set of calculate tools such as add, subtract, multiply and divide.
diff --git a/vision_agent/agent/vision_agent.py b/vision_agent/agent/vision_agent.py
index 3287a174..b02cdf72 100644
--- a/vision_agent/agent/vision_agent.py
+++ b/vision_agent/agent/vision_agent.py
@@ -8,7 +8,7 @@
 from PIL import Image
 from tabulate import tabulate
 
-from vision_agent.image_utils import overlay_bboxes, overlay_masks
+from vision_agent.image_utils import overlay_bboxes, overlay_masks, overlay_heat_map
 from vision_agent.llm import LLM, OpenAILLM
 from vision_agent.lmm import LMM, OpenAILMM
 from vision_agent.tools import TOOLS
@@ -336,7 +336,9 @@ def _handle_viz_tools(
 
     for param, call_result in zip(parameters, tool_result["call_results"]):
         # calls can fail, so we need to check if the call was successful
-        if not isinstance(call_result, dict) or "bboxes" not in call_result:
+        if not isinstance(call_result, dict) or (
+            "bboxes" not in call_result and "masks" not in call_result
+        ):
             return image_to_data
 
         # if the call was successful, then we can add the image data
@@ -349,11 +351,12 @@ def _handle_viz_tools(
                 "scores": [],
             }
 
-        image_to_data[image]["bboxes"].extend(call_result["bboxes"])
-        image_to_data[image]["labels"].extend(call_result["labels"])
-        image_to_data[image]["scores"].extend(call_result["scores"])
-        if "masks" in call_result:
-            image_to_data[image]["masks"].extend(call_result["masks"])
+        image_to_data[image]["bboxes"].extend(call_result.get("bboxes", []))
+        image_to_data[image]["labels"].extend(call_result.get("labels", []))
+        image_to_data[image]["scores"].extend(call_result.get("scores", []))
+        image_to_data[image]["masks"].extend(call_result.get("masks", []))
+        if "mask_shape" in call_result:
+            image_to_data[image]["mask_shape"] = call_result["mask_shape"]
 
     return image_to_data
 
@@ -367,6 +370,8 @@ def visualize_result(all_tool_results: List[Dict]) -> Sequence[Union[str, Path]]
             "grounding_dino_",
             "extract_frames_",
             "dinov_",
+            "zero_shot_counting_",
+            "visual_prompt_counting_",
         ]:
             continue
 
@@ -379,8 +384,11 @@ def visualize_result(all_tool_results: List[Dict]) -> Sequence[Union[str, Path]]
     for image_str in image_to_data:
         image_path = Path(image_str)
         image_data = image_to_data[image_str]
-        image = overlay_masks(image_path, image_data)
-        image = overlay_bboxes(image, image_data)
+        if "_counting_" in tool_result["tool_name"]:
+            image = overlay_heat_map(image_path, image_data)
+        else:
+            image = overlay_masks(image_path, image_data)
+            image = overlay_bboxes(image, image_data)
         with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as f:
             image.save(f.name)
             visualized_images.append(f.name)
@@ -484,11 +492,21 @@ def chat_with_workflow(
         if image:
             question += f" Image name: {image}"
         if reference_data:
-            if not ("image" in reference_data and "mask" in reference_data):
+            if not (
+                "image" in reference_data
+                and ("mask" in reference_data or "bbox" in reference_data)
+            ):
                 raise ValueError(
-                    f"Reference data must contain 'image' and 'mask'. but got {reference_data}"
+                    f"Reference data must contain 'image' and a visual prompt which can be 'mask' or 'bbox'. but got {reference_data}"
                 )
-            question += f" Reference image: {reference_data['image']}, Reference mask: {reference_data['mask']}"
+            visual_prompt_data = (
+                f"Reference mask: {reference_data['mask']}"
+                if "mask" in reference_data
+                else f"Reference bbox: {reference_data['bbox']}"
+            )
+            question += (
+                f" Reference image: {reference_data['image']}, {visual_prompt_data}"
+            )
 
         reflections = ""
         final_answer = ""
@@ -531,7 +549,6 @@ def chat_with_workflow(
             final_answer = answer_summarize(
                 self.answer_model, question, answers, reflections
             )
-
             visualized_output = visualize_result(all_tool_results)
             all_tool_results.append({"visualized_output": visualized_output})
             if len(visualized_output) > 0:
diff --git a/vision_agent/image_utils.py b/vision_agent/image_utils.py
index f36a2033..43da645f 100644
--- a/vision_agent/image_utils.py
+++ b/vision_agent/image_utils.py
@@ -4,7 +4,7 @@
 from importlib import resources
 from io import BytesIO
 from pathlib import Path
-from typing import Dict, Tuple, Union
+from typing import Dict, Tuple, Union, List
 
 import numpy as np
 from PIL import Image, ImageDraw, ImageFont
@@ -34,6 +34,35 @@
 ]
 
 
+def normalize_bbox(
+    bbox: List[Union[int, float]], image_size: Tuple[int, ...]
+) -> List[float]:
+    r"""Normalize the bounding box coordinates to be between 0 and 1."""
+    x1, y1, x2, y2 = bbox
+    x1 = round(x1 / image_size[1], 2)
+    y1 = round(y1 / image_size[0], 2)
+    x2 = round(x2 / image_size[1], 2)
+    y2 = round(y2 / image_size[0], 2)
+    return [x1, y1, x2, y2]
+
+
+def rle_decode(mask_rle: str, shape: Tuple[int, int]) -> np.ndarray:
+    r"""Decode a run-length encoded mask. Returns numpy array, 1 - mask, 0 - background.
+
+    Parameters:
+        mask_rle: Run-length as string formated (start length)
+        shape: The (height, width) of array to return
+    """
+    s = mask_rle.split()
+    starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]
+    starts -= 1
+    ends = starts + lengths
+    img = np.zeros(shape[0] * shape[1], dtype=np.uint8)
+    for lo, hi in zip(starts, ends):
+        img[lo:hi] = 1
+    return img.reshape(shape)
+
+
 def b64_to_pil(b64_str: str) -> ImageType:
     r"""Convert a base64 string to a PIL Image.
 
@@ -86,6 +115,26 @@ def convert_to_b64(data: Union[str, Path, np.ndarray, ImageType]) -> str:
         return base64.b64encode(arr_bytes).decode("utf-8")
 
 
+def denormalize_bbox(
+    bbox: List[Union[int, float]], image_size: Tuple[int, ...]
+) -> List[float]:
+    r"""DeNormalize the bounding box coordinates so that they are in absolute values."""
+
+    if len(bbox) != 4:
+        raise ValueError("Bounding box must be of length 4.")
+
+    arr = np.array(bbox)
+    if np.all((arr >= 0) & (arr <= 1)):
+        x1, y1, x2, y2 = bbox
+        x1 = round(x1 * image_size[1])
+        y1 = round(y1 * image_size[0])
+        x2 = round(x2 * image_size[1])
+        y2 = round(y2 * image_size[0])
+        return [x1, y1, x2, y2]
+    else:
+        return bbox
+
+
 def overlay_bboxes(
     image: Union[str, Path, np.ndarray, ImageType], bboxes: Dict
 ) -> ImageType:
@@ -103,6 +152,9 @@ def overlay_bboxes(
     elif isinstance(image, np.ndarray):
         image = Image.fromarray(image)
 
+    if "bboxes" not in bboxes:
+        return image.convert("RGB")
+
     color = {
         label: COLORS[i % len(COLORS)] for i, label in enumerate(set(bboxes["labels"]))
     }
@@ -114,8 +166,6 @@ def overlay_bboxes(
         str(resources.files("vision_agent.fonts").joinpath("default_font_ch_en.ttf")),
         fontsize,
     )
-    if "bboxes" not in bboxes:
-        return image.convert("RGB")
 
     for label, box, scores in zip(bboxes["labels"], bboxes["bboxes"], bboxes["scores"]):
         box = [
@@ -150,11 +200,15 @@ def overlay_masks(
     elif isinstance(image, np.ndarray):
         image = Image.fromarray(image)
 
+    if "masks" not in masks:
+        return image.convert("RGB")
+
+    if "labels" not in masks:
+        masks["labels"] = [""] * len(masks["masks"])
+
     color = {
         label: COLORS[i % len(COLORS)] for i, label in enumerate(set(masks["labels"]))
     }
-    if "masks" not in masks:
-        return image.convert("RGB")
 
     for label, mask in zip(masks["labels"], masks["masks"]):
         if isinstance(mask, str):
@@ -164,3 +218,40 @@ def overlay_masks(
         mask_img = Image.fromarray(np_mask.astype(np.uint8))
         image = Image.alpha_composite(image.convert("RGBA"), mask_img)
     return image.convert("RGB")
+
+
+def overlay_heat_map(
+    image: Union[str, Path, np.ndarray, ImageType], masks: Dict, alpha: float = 0.8
+) -> ImageType:
+    r"""Plots heat map on to an image.
+
+    Parameters:
+        image: the input image
+        masks: the heatmap to overlay
+        alpha: the transparency of the overlay
+
+    Returns:
+        The image with the heatmap overlayed
+    """
+    if isinstance(image, (str, Path)):
+        image = Image.open(image)
+    elif isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+
+    if "masks" not in masks:
+        return image.convert("RGB")
+
+    # Only one heat map per image, so no need to loop through masks
+    image = image.convert("L")
+
+    if isinstance(masks["masks"][0], str):
+        mask = b64_to_pil(masks["masks"][0])
+
+    overlay = Image.new("RGBA", mask.size)
+    odraw = ImageDraw.Draw(overlay)
+    odraw.bitmap(
+        (0, 0), mask, fill=(255, 0, 0, round(alpha * 255))
+    )  # fill=(R, G, B, Alpha)
+    combined = Image.alpha_composite(image.convert("RGBA"), overlay.resize(image.size))
+
+    return combined.convert("RGB")
diff --git a/vision_agent/llm/llm.py b/vision_agent/llm/llm.py
index 9352f58b..3f83c269 100644
--- a/vision_agent/llm/llm.py
+++ b/vision_agent/llm/llm.py
@@ -11,6 +11,7 @@
     SYSTEM_PROMPT,
     GroundingDINO,
     GroundingSAM,
+    ZeroShotCounting,
 )
 
 
@@ -127,6 +128,9 @@ 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:
+        return lambda x: ZeroShotCounting()(**{"image": x})
+
 
 class AzureOpenAILLM(OpenAILLM):
     def __init__(
diff --git a/vision_agent/lmm/lmm.py b/vision_agent/lmm/lmm.py
index 615804ed..06ce94a2 100644
--- a/vision_agent/lmm/lmm.py
+++ b/vision_agent/lmm/lmm.py
@@ -15,6 +15,7 @@
     SYSTEM_PROMPT,
     GroundingDINO,
     GroundingSAM,
+    ZeroShotCounting,
 )
 
 _LOGGER = logging.getLogger(__name__)
@@ -272,6 +273,9 @@ 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:
+        return lambda x: ZeroShotCounting()(**{"image": x})
+
 
 class AzureOpenAILMM(OpenAILMM):
     def __init__(
diff --git a/vision_agent/tools/__init__.py b/vision_agent/tools/__init__.py
index 1c1c6e73..38bb08d4 100644
--- a/vision_agent/tools/__init__.py
+++ b/vision_agent/tools/__init__.py
@@ -11,6 +11,8 @@
     GroundingDINO,
     GroundingSAM,
     ImageCaption,
+    ZeroShotCounting,
+    VisualPromptCounting,
     SegArea,
     SegIoU,
     Tool,
diff --git a/vision_agent/tools/tools.py b/vision_agent/tools/tools.py
index 7657a362..c964a895 100644
--- a/vision_agent/tools/tools.py
+++ b/vision_agent/tools/tools.py
@@ -9,7 +9,13 @@
 from PIL import Image
 from PIL.Image import Image as ImageType
 
-from vision_agent.image_utils import convert_to_b64, get_image_size
+from vision_agent.image_utils import (
+    convert_to_b64,
+    get_image_size,
+    rle_decode,
+    normalize_bbox,
+    denormalize_bbox,
+)
 from vision_agent.tools.video import extract_frames_from_video
 from vision_agent.type_defs import LandingaiAPIKey
 
@@ -18,35 +24,6 @@
 _LND_API_URL = "https://api.dev.landing.ai/v1/agent"
 
 
-def normalize_bbox(
-    bbox: List[Union[int, float]], image_size: Tuple[int, ...]
-) -> List[float]:
-    r"""Normalize the bounding box coordinates to be between 0 and 1."""
-    x1, y1, x2, y2 = bbox
-    x1 = round(x1 / image_size[1], 2)
-    y1 = round(y1 / image_size[0], 2)
-    x2 = round(x2 / image_size[1], 2)
-    y2 = round(y2 / image_size[0], 2)
-    return [x1, y1, x2, y2]
-
-
-def rle_decode(mask_rle: str, shape: Tuple[int, int]) -> np.ndarray:
-    r"""Decode a run-length encoded mask. Returns numpy array, 1 - mask, 0 - background.
-
-    Parameters:
-        mask_rle: Run-length as string formated (start length)
-        shape: The (height, width) of array to return
-    """
-    s = mask_rle.split()
-    starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]
-    starts -= 1
-    ends = starts + lengths
-    img = np.zeros(shape[0] * shape[1], dtype=np.uint8)
-    for lo, hi in zip(starts, ends):
-        img[lo:hi] = 1
-    return img.reshape(shape)
-
-
 class Tool(ABC):
     name: str
     description: str
@@ -489,6 +466,130 @@ def __call__(
         return rets
 
 
+class ZeroShotCounting(Tool):
+    r"""ZeroShotCounting is a tool that can count total number of instances of an object
+    present in an image belonging to same class without a text or visual prompt.
+
+    Example
+    -------
+        >>> import vision_agent as va
+        >>> zshot_count = va.tools.ZeroShotCounting()
+        >>> zshot_count("image1.jpg")
+        {'count': 45}
+    """
+
+    name = "zero_shot_counting_"
+    description = "'zero_shot_counting_' is a tool that counts and returns the total number of instances of an object present in an image belonging to the same class without a text or visual prompt."
+
+    usage = {
+        "required_parameters": [
+            {"name": "image", "type": "str"},
+        ],
+        "examples": [
+            {
+                "scenario": "Can you count the lids in the image ? Image name: lids.jpg",
+                "parameters": {"image": "lids.jpg"},
+            },
+            {
+                "scenario": "Can you count the total number of objects in this image ? Image name: tray.jpg",
+                "parameters": {"image": "tray.jpg"},
+            },
+            {
+                "scenario": "Can you build me an object counting 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.
+
+        Returns:
+            A dictionary containing the key 'count' and the count as value. E.g. {count: 12}
+        """
+        image_b64 = convert_to_b64(image)
+        data = {
+            "image": image_b64,
+            "tool": "zero_shot_counting",
+        }
+        return _send_inference_request(data, "tools")
+
+
+class VisualPromptCounting(Tool):
+    r"""VisualPromptCounting is a tool that can count total number of instances of an object
+    present in an image belonging to same class with help of an visual prompt which is a bounding box.
+
+    Example
+    -------
+        >>> import vision_agent as va
+        >>> prompt_count = va.tools.VisualPromptCounting()
+        >>> prompt_count(image="image1.jpg", prompt="0.1, 0.1, 0.4, 0.42")
+        {'count': 23}
+    """
+
+    name = "visual_prompt_counting_"
+    description = "'visual_prompt_counting_' is a tool that can count and return total number of instances of an object present in an image belonging to the same class given an example bounding box."
+
+    usage = {
+        "required_parameters": [
+            {"name": "image", "type": "str"},
+            {"name": "prompt", "type": "str"},
+        ],
+        "examples": [
+            {
+                "scenario": "Here is an example of a lid '0.1, 0.1, 0.14, 0.2', Can you count the lids in the image ? Image name: lids.jpg",
+                "parameters": {"image": "lids.jpg", "prompt": "0.1, 0.1, 0.14, 0.2"},
+            },
+            {
+                "scenario": "Can you count the total number of objects in this image ? Image name: tray.jpg",
+                "parameters": {"image": "tray.jpg", "prompt": "0.1, 0.1, 0.2, 0.25"},
+            },
+            {
+                "scenario": "Can you build me a few shot object counting tool ? Image name: shirts.jpg",
+                "parameters": {
+                    "image": "shirts.jpg",
+                    "prompt": "0.1, 0.15, 0.2, 0.2",
+                },
+            },
+            {
+                "scenario": "Can you build me a counting tool based on an example prompt ? Image name: shoes.jpg",
+                "parameters": {
+                    "image": "shoes.jpg",
+                    "prompt": "0.1, 0.1, 0.6, 0.65",
+                },
+            },
+        ],
+    }
+
+    # 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.
+
+        Parameters:
+            image: the input image.
+
+        Returns:
+            A dictionary containing the key 'count' and the count as value. E.g. {count: 12}
+        """
+        image_size = get_image_size(image)
+        bbox = [float(x) for x in prompt.split(",")]
+        prompt = ", ".join(map(str, denormalize_bbox(bbox, image_size)))
+        image_b64 = convert_to_b64(image)
+
+        data = {
+            "image": image_b64,
+            "prompt": prompt,
+            "tool": "few_shot_counting",
+        }
+        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."""
 
@@ -798,6 +899,8 @@ def __call__(self, equation: str) -> float:
             ImageCaption,
             GroundingDINO,
             AgentGroundingSAM,
+            ZeroShotCounting,
+            VisualPromptCounting,
             AgentDINOv,
             ExtractFrames,
             Crop,

From e803fe14c2ff64d2c7d800b25fd4a05e28bfea61 Mon Sep 17 00:00:00 2001
From: Asia <92344512+AsiaCao@users.noreply.github.com>
Date: Mon, 22 Apr 2024 14:14:35 -0700
Subject: [PATCH 4/5] Remove torch and cuda dependencies (#60)

Resolve merge conflicts
---
 docs/api/data.md              |   3 -
 docs/api/emb.md               |   3 -
 mkdocs.yml                    |   2 -
 poetry.lock                   | 793 +---------------------------------
 pyproject.toml                |   8 +-
 tests/test_data.py            |  93 ----
 vision_agent/__init__.py      |   2 -
 vision_agent/data/__init__.py |   1 -
 vision_agent/data/data.py     | 142 ------
 vision_agent/emb/__init__.py  |   1 -
 vision_agent/emb/emb.py       |  47 --
 11 files changed, 7 insertions(+), 1088 deletions(-)
 delete mode 100644 docs/api/data.md
 delete mode 100644 docs/api/emb.md
 delete mode 100644 tests/test_data.py
 delete mode 100644 vision_agent/data/__init__.py
 delete mode 100644 vision_agent/data/data.py
 delete mode 100644 vision_agent/emb/__init__.py
 delete mode 100644 vision_agent/emb/emb.py

diff --git a/docs/api/data.md b/docs/api/data.md
deleted file mode 100644
index 2e157f7c..00000000
--- a/docs/api/data.md
+++ /dev/null
@@ -1,3 +0,0 @@
-::: vision_agent.data
-
-::: vision_agent.data.data
\ No newline at end of file
diff --git a/docs/api/emb.md b/docs/api/emb.md
deleted file mode 100644
index 1421ef8d..00000000
--- a/docs/api/emb.md
+++ /dev/null
@@ -1,3 +0,0 @@
-::: vision_agent.emb
-
-::: vision_agent.emb.emb
\ No newline at end of file
diff --git a/mkdocs.yml b/mkdocs.yml
index 9dd45949..7409b561 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -37,7 +37,5 @@ nav:
     - vision_agent.tools: api/tools.md
     - vision_agent.llm: api/llm.md
     - vision_agent.lmm: api/lmm.md
-    - vision_agent.data: api/data.md
-    - vision_agent.emb: api/emb.md
     - vision_agent.image_utils: api/image_utils.md
   - Old documentation: old.md
diff --git a/poetry.lock b/poetry.lock
index a0c62cab..04733555 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -293,60 +293,6 @@ files = [
 [package.extras]
 test = ["pytest (>=6)"]
 
-[[package]]
-name = "faiss-cpu"
-version = "1.8.0"
-description = "A library for efficient similarity search and clustering of dense vectors."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "faiss-cpu-1.8.0.tar.gz", hash = "sha256:3ee1549491728f37b65267c192a94661a907154a8ae0546ad50a564b8be0d82e"},
-    {file = "faiss_cpu-1.8.0-cp310-cp310-macosx_10_14_x86_64.whl", hash = "sha256:134a064c7411acf7d1d863173a9d2605c5a59bd573639ab39a5ded5ca983b1b2"},
-    {file = "faiss_cpu-1.8.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ba8e6202d561ac57394c9d691ff17f8fa6eb9a077913a993fce0a154ec0176f1"},
-    {file = "faiss_cpu-1.8.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a66e9fa7b70556a39681f06e0652f4124c8ddb0a1924afe4f0e40b6924dc845b"},
-    {file = "faiss_cpu-1.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:51aaef5a1255d0ea88ea7e52a2415f98c5dd2dd9cec10348d55136541eeec99f"},
-    {file = "faiss_cpu-1.8.0-cp310-cp310-win_amd64.whl", hash = "sha256:38152761242870ec7019e0397cbd0ed0b0716562029ce41a71bb38448bd6d5bc"},
-    {file = "faiss_cpu-1.8.0-cp311-cp311-macosx_10_14_x86_64.whl", hash = "sha256:c9e6ad94b86626be1a0faff3e53c4ca169eba88aa156d7e90c5a2e9ba30558fb"},
-    {file = "faiss_cpu-1.8.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4601dbd81733bf1bc3bff690aac981289fb386dc8e60d0c4eec8a37ba6856d20"},
-    {file = "faiss_cpu-1.8.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fa943d3b5e8c5c77cdd629d9c3c6f78d7da616e586fdd1b94aecbf2e5fa9ba06"},
-    {file = "faiss_cpu-1.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b644b366c3b239b34fa3e08bf65bfc78a24eda1e1ea5b2b6d9be3e8fc73d8179"},
-    {file = "faiss_cpu-1.8.0-cp311-cp311-win_amd64.whl", hash = "sha256:f85ecf3514850f93985be238351f5a70736133cfae784b372640aa17c6343a1b"},
-    {file = "faiss_cpu-1.8.0-cp312-cp312-macosx_10_14_x86_64.whl", hash = "sha256:61abc0129a357ac00f17f5167f14dff41480de2cc852f306c3d4cd36b893ccbd"},
-    {file = "faiss_cpu-1.8.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b788186d6eb94e6333e1aa8bb6c84b66e967458ecdd1cee22e16f04c43ee674c"},
-    {file = "faiss_cpu-1.8.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5658d90a202c62e4a69c5b065785e9ddcaf6986cb395c16afed8dbe4c58c31a2"},
-    {file = "faiss_cpu-1.8.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5d460a372efce547e53d3c47d2c2a8a90b186ad245969048c10c1d7a1e5cf21b"},
-    {file = "faiss_cpu-1.8.0-cp312-cp312-win_amd64.whl", hash = "sha256:9e6520324f0a6764dd267b3c32c76958bf2b1ec36752950f6fab31a7295980a0"},
-    {file = "faiss_cpu-1.8.0-cp38-cp38-macosx_10_14_x86_64.whl", hash = "sha256:fc44be179d5b7f690484ef0d0caf817fea2698a5275a0c7fb6cbf406e5b2e4d1"},
-    {file = "faiss_cpu-1.8.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:bbd6f0bc2e1424a12dc7e19d2cc95b53124867966b21110d26f909227e7ed1f1"},
-    {file = "faiss_cpu-1.8.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:06e7add0c8a06ce8fb0443c38fcaf49c45fb74527ea633b819e56452608e64f5"},
-    {file = "faiss_cpu-1.8.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b864e23c1817fa6cfe9bbec096fd7140d596002934f71aa89b196ffb1b9cd846"},
-    {file = "faiss_cpu-1.8.0-cp38-cp38-win_amd64.whl", hash = "sha256:655433755845adbb6f0961e2f8980703640cb9faa96f1cd1ea190252149e0d0a"},
-    {file = "faiss_cpu-1.8.0-cp39-cp39-macosx_10_14_x86_64.whl", hash = "sha256:e81fc376a3bcda213ffb395dda1018c953ce927c587731ad582f4e6c2b225363"},
-    {file = "faiss_cpu-1.8.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:8c6fa6b7eaf558307b4ab118a236e8d1da79a8685222928e4dd52e277dba144a"},
-    {file = "faiss_cpu-1.8.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:652f6812ef2e8b0f9b18209828c590bc618aca82e7f1c1b1888f52928258e406"},
-    {file = "faiss_cpu-1.8.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:304da4e0d19044374b63a5b6467028572eac4bd3f32bc9e8783d800a03fb1f02"},
-    {file = "faiss_cpu-1.8.0-cp39-cp39-win_amd64.whl", hash = "sha256:cb475d3f25f08c97ac64dfe026f113e2aeb9829b206b3b046256c3b40dd7eb62"},
-]
-
-[package.dependencies]
-numpy = "*"
-
-[[package]]
-name = "filelock"
-version = "3.13.1"
-description = "A platform independent file lock."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "filelock-3.13.1-py3-none-any.whl", hash = "sha256:57dbda9b35157b05fb3e58ee91448612eb674172fab98ee235ccb0b5bee19a1c"},
-    {file = "filelock-3.13.1.tar.gz", hash = "sha256:521f5f56c50f8426f5e03ad3b281b490a87ef15bc6c526f168290f0c7148d44e"},
-]
-
-[package.extras]
-docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.24)"]
-testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
-typing = ["typing-extensions (>=4.8)"]
-
 [[package]]
 name = "flake8"
 version = "5.0.4"
@@ -363,41 +309,6 @@ mccabe = ">=0.7.0,<0.8.0"
 pycodestyle = ">=2.9.0,<2.10.0"
 pyflakes = ">=2.5.0,<2.6.0"
 
-[[package]]
-name = "fsspec"
-version = "2024.3.1"
-description = "File-system specification"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "fsspec-2024.3.1-py3-none-any.whl", hash = "sha256:918d18d41bf73f0e2b261824baeb1b124bcf771767e3a26425cd7dec3332f512"},
-    {file = "fsspec-2024.3.1.tar.gz", hash = "sha256:f39780e282d7d117ffb42bb96992f8a90795e4d0fb0f661a70ca39fe9c43ded9"},
-]
-
-[package.extras]
-abfs = ["adlfs"]
-adl = ["adlfs"]
-arrow = ["pyarrow (>=1)"]
-dask = ["dask", "distributed"]
-devel = ["pytest", "pytest-cov"]
-dropbox = ["dropbox", "dropboxdrivefs", "requests"]
-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
-fuse = ["fusepy"]
-gcs = ["gcsfs"]
-git = ["pygit2"]
-github = ["requests"]
-gs = ["gcsfs"]
-gui = ["panel"]
-hdfs = ["pyarrow (>=1)"]
-http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
-libarchive = ["libarchive-c"]
-oci = ["ocifs"]
-s3 = ["s3fs"]
-sftp = ["paramiko"]
-smb = ["smbprotocol"]
-ssh = ["paramiko"]
-tqdm = ["tqdm"]
-
 [[package]]
 name = "ghp-import"
 version = "2.1.0"
@@ -485,39 +396,6 @@ cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
 http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 
-[[package]]
-name = "huggingface-hub"
-version = "0.21.4"
-description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
-optional = false
-python-versions = ">=3.8.0"
-files = [
-    {file = "huggingface_hub-0.21.4-py3-none-any.whl", hash = "sha256:df37c2c37fc6c82163cdd8a67ede261687d80d1e262526d6c0ce73b6b3630a7b"},
-    {file = "huggingface_hub-0.21.4.tar.gz", hash = "sha256:e1f4968c93726565a80edf6dc309763c7b546d0cfe79aa221206034d50155531"},
-]
-
-[package.dependencies]
-filelock = "*"
-fsspec = ">=2023.5.0"
-packaging = ">=20.9"
-pyyaml = ">=5.1"
-requests = "*"
-tqdm = ">=4.42.1"
-typing-extensions = ">=3.7.4.3"
-
-[package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
-cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
-fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
-hf-transfer = ["hf-transfer (>=0.1.4)"]
-inference = ["aiohttp", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)"]
-quality = ["mypy (==1.5.1)", "ruff (>=0.1.3)"]
-tensorflow = ["graphviz", "pydot", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
-torch = ["safetensors", "torch"]
-typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
-
 [[package]]
 name = "idna"
 version = "3.6"
@@ -640,17 +518,6 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]
 
-[[package]]
-name = "joblib"
-version = "1.3.2"
-description = "Lightweight pipelining with Python functions"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "joblib-1.3.2-py3-none-any.whl", hash = "sha256:ef4331c65f239985f3f2220ecc87db222f08fd22097a3dd5698f693875f8cbb9"},
-    {file = "joblib-1.3.2.tar.gz", hash = "sha256:92f865e621e17784e7955080b6d042489e3b8e294949cc44c6eac304f59772b1"},
-]
-
 [[package]]
 name = "lazy-loader"
 version = "0.4"
@@ -932,23 +799,6 @@ doc = ["Sphinx (>=1.5.2,<2.0)", "numpydoc (>=0.6.0,<1.0)", "pygame (>=1.9.3,<2.0
 optional = ["matplotlib (>=2.0.0,<3.0)", "opencv-python (>=3.0,<4.0)", "scikit-image (>=0.13.0,<1.0)", "scikit-learn", "scipy (>=0.19.0,<1.5)", "youtube_dl"]
 test = ["coverage (<5.0)", "coveralls (>=1.1,<2.0)", "pytest (>=3.0.0,<4.0)", "pytest-cov (>=2.5.1,<3.0)", "requests (>=2.8.1,<3.0)"]
 
-[[package]]
-name = "mpmath"
-version = "1.3.0"
-description = "Python library for arbitrary-precision floating-point arithmetic"
-optional = false
-python-versions = "*"
-files = [
-    {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
-    {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
-]
-
-[package.extras]
-develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"]
-docs = ["sphinx"]
-gmpy = ["gmpy2 (>=2.1.0a4)"]
-tests = ["pytest (>=4.6)"]
-
 [[package]]
 name = "mypy"
 version = "1.7.1"
@@ -1070,148 +920,6 @@ files = [
     {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"},
 ]
 
-[[package]]
-name = "nvidia-cublas-cu12"
-version = "12.1.3.1"
-description = "CUBLAS native runtime libraries"
-optional = false
-python-versions = ">=3"
-files = [
-    {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl", hash = "sha256:ee53ccca76a6fc08fb9701aa95b6ceb242cdaab118c3bb152af4e579af792728"},
-    {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-win_amd64.whl", hash = "sha256:2b964d60e8cf11b5e1073d179d85fa340c120e99b3067558f3cf98dd69d02906"},
-]
-
-[[package]]
-name = "nvidia-cuda-cupti-cu12"
-version = "12.1.105"
-description = "CUDA profiling tools runtime libs."
-optional = false
-python-versions = ">=3"
-files = [
-    {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:e54fde3983165c624cb79254ae9818a456eb6e87a7fd4d56a2352c24ee542d7e"},
-    {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:bea8236d13a0ac7190bd2919c3e8e6ce1e402104276e6f9694479e48bb0eb2a4"},
-]
-
-[[package]]
-name = "nvidia-cuda-nvrtc-cu12"
-version = "12.1.105"
-description = "NVRTC native runtime libraries"
-optional = false
-python-versions = ">=3"
-files = [
-    {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:339b385f50c309763ca65456ec75e17bbefcbbf2893f462cb8b90584cd27a1c2"},
-    {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:0a98a522d9ff138b96c010a65e145dc1b4850e9ecb75a0172371793752fd46ed"},
-]
-
-[[package]]
-name = "nvidia-cuda-runtime-cu12"
-version = "12.1.105"
-description = "CUDA Runtime native Libraries"
-optional = false
-python-versions = ">=3"
-files = [
-    {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:6e258468ddf5796e25f1dc591a31029fa317d97a0a94ed93468fc86301d61e40"},
-    {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:dfb46ef84d73fababab44cf03e3b83f80700d27ca300e537f85f636fac474344"},
-]
-
-[[package]]
-name = "nvidia-cudnn-cu12"
-version = "8.9.2.26"
-description = "cuDNN runtime libraries"
-optional = false
-python-versions = ">=3"
-files = [
-    {file = "nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl", hash = "sha256:5ccb288774fdfb07a7e7025ffec286971c06d8d7b4fb162525334616d7629ff9"},
-]
-
-[package.dependencies]
-nvidia-cublas-cu12 = "*"
-
-[[package]]
-name = "nvidia-cufft-cu12"
-version = "11.0.2.54"
-description = "CUFFT native runtime libraries"
-optional = false
-python-versions = ">=3"
-files = [
-    {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl", hash = "sha256:794e3948a1aa71fd817c3775866943936774d1c14e7628c74f6f7417224cdf56"},
-    {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-win_amd64.whl", hash = "sha256:d9ac353f78ff89951da4af698f80870b1534ed69993f10a4cf1d96f21357e253"},
-]
-
-[[package]]
-name = "nvidia-curand-cu12"
-version = "10.3.2.106"
-description = "CURAND native runtime libraries"
-optional = false
-python-versions = ">=3"
-files = [
-    {file = "nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:9d264c5036dde4e64f1de8c50ae753237c12e0b1348738169cd0f8a536c0e1e0"},
-    {file = "nvidia_curand_cu12-10.3.2.106-py3-none-win_amd64.whl", hash = "sha256:75b6b0c574c0037839121317e17fd01f8a69fd2ef8e25853d826fec30bdba74a"},
-]
-
-[[package]]
-name = "nvidia-cusolver-cu12"
-version = "11.4.5.107"
-description = "CUDA solver native runtime libraries"
-optional = false
-python-versions = ">=3"
-files = [
-    {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd"},
-    {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-win_amd64.whl", hash = "sha256:74e0c3a24c78612192a74fcd90dd117f1cf21dea4822e66d89e8ea80e3cd2da5"},
-]
-
-[package.dependencies]
-nvidia-cublas-cu12 = "*"
-nvidia-cusparse-cu12 = "*"
-nvidia-nvjitlink-cu12 = "*"
-
-[[package]]
-name = "nvidia-cusparse-cu12"
-version = "12.1.0.106"
-description = "CUSPARSE native runtime libraries"
-optional = false
-python-versions = ">=3"
-files = [
-    {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c"},
-    {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-win_amd64.whl", hash = "sha256:b798237e81b9719373e8fae8d4f091b70a0cf09d9d85c95a557e11df2d8e9a5a"},
-]
-
-[package.dependencies]
-nvidia-nvjitlink-cu12 = "*"
-
-[[package]]
-name = "nvidia-nccl-cu12"
-version = "2.18.1"
-description = "NVIDIA Collective Communication Library (NCCL) Runtime"
-optional = false
-python-versions = ">=3"
-files = [
-    {file = "nvidia_nccl_cu12-2.18.1-py3-none-manylinux1_x86_64.whl", hash = "sha256:1a6c4acefcbebfa6de320f412bf7866de856e786e0462326ba1bac40de0b5e71"},
-]
-
-[[package]]
-name = "nvidia-nvjitlink-cu12"
-version = "12.4.99"
-description = "Nvidia JIT LTO Library"
-optional = false
-python-versions = ">=3"
-files = [
-    {file = "nvidia_nvjitlink_cu12-12.4.99-py3-none-manylinux2014_aarch64.whl", hash = "sha256:75d6498c96d9adb9435f2bbdbddb479805ddfb97b5c1b32395c694185c20ca57"},
-    {file = "nvidia_nvjitlink_cu12-12.4.99-py3-none-manylinux2014_x86_64.whl", hash = "sha256:c6428836d20fe7e327191c175791d38570e10762edc588fb46749217cd444c74"},
-    {file = "nvidia_nvjitlink_cu12-12.4.99-py3-none-win_amd64.whl", hash = "sha256:991905ffa2144cb603d8ca7962d75c35334ae82bf92820b6ba78157277da1ad2"},
-]
-
-[[package]]
-name = "nvidia-nvtx-cu12"
-version = "12.1.105"
-description = "NVIDIA Tools Extension"
-optional = false
-python-versions = ">=3"
-files = [
-    {file = "nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:dc21cf308ca5691e7c04d962e213f8a4aa9bbfa23d95412f452254c2caeb09e5"},
-    {file = "nvidia_nvtx_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:65f4d98982b31b60026e0e6de73fbdfc09d08a96f4656dd3665ca616a11e1e82"},
-]
-
 [[package]]
 name = "openai"
 version = "1.14.2"
@@ -1254,7 +962,8 @@ files = [
 [package.dependencies]
 numpy = [
     {version = ">=1.21.0", markers = "python_version == \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\""},
-    {version = ">=1.23.5", markers = "python_version >= \"3.11\""},
+    {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
+    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
     {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
     {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
     {version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
@@ -1322,6 +1031,7 @@ files = [
 [package.dependencies]
 numpy = [
     {version = ">=1.22.4,<2", markers = "python_version < \"3.11\""},
+    {version = ">=1.26.0,<2", markers = "python_version >= \"3.12\""},
     {version = ">=1.23.2,<2", markers = "python_version == \"3.11\""},
 ]
 python-dateutil = ">=2.8.2"
@@ -1955,138 +1665,6 @@ urllib3 = ">=1.25.10,<3.0"
 [package.extras]
 tests = ["coverage (>=6.0.0)", "flake8", "mypy", "pytest (>=7.0.0)", "pytest-asyncio", "pytest-cov", "pytest-httpserver", "tomli", "tomli-w", "types-requests"]
 
-[[package]]
-name = "safetensors"
-version = "0.4.2"
-description = ""
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "safetensors-0.4.2-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:69d8bb8384dc2cb5b72c36c4d6980771b293d1a1377b378763f5e37b6bb8d133"},
-    {file = "safetensors-0.4.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3d420e19fcef96d0067f4de4699682b4bbd85fc8fea0bd45fcd961fdf3e8c82c"},
-    {file = "safetensors-0.4.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9ca54742122fa3c4821754adb67318e1cd25c3a22bbf0c5520d5176e77a099ac"},
-    {file = "safetensors-0.4.2-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:8b47aa643afdfd66cf7ce4c184092ae734e15d10aba2c2948f24270211801c3c"},
-    {file = "safetensors-0.4.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d88a16bbc330f27e7f2d4caaf6fb061ad0b8a756ecc4033260b0378e128ce8a2"},
-    {file = "safetensors-0.4.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e9223b8ac21085db614a510eb3445e7083cae915a9202357555fa939695d4f57"},
-    {file = "safetensors-0.4.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ce6cb86133dc8930a7ab5e7438545a7f205f7a1cdd5aaf108c1d0da6bdcfbc2b"},
-    {file = "safetensors-0.4.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:b8a628e0ae2bbc334b62952c384aa5f41621d01850f8d67b04a96b9c39dd7326"},
-    {file = "safetensors-0.4.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:88d6beb7f811a081e0e5f1d9669fdac816c45340c04b1eaf7ebfda0ce93ea403"},
-    {file = "safetensors-0.4.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:b57fc5b1b54cb12d8690a58a4cf4b7144730d4bde9d98aa0e1dab6295a1cd579"},
-    {file = "safetensors-0.4.2-cp310-none-win32.whl", hash = "sha256:9d87a1c98803c16cf113b9ba03f07b2dce5e8eabfd1811a7f7323fcaa2a1bf47"},
-    {file = "safetensors-0.4.2-cp310-none-win_amd64.whl", hash = "sha256:18930ec1d1ecb526d3d9835abc2489b8f1530877518f0c541e77ef0b7abcbd99"},
-    {file = "safetensors-0.4.2-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:c5dd2ed788730ed56b415d1a11c62026b8cc8c573f55a2092afb3ab383e94fff"},
-    {file = "safetensors-0.4.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:cc41791b33efb9c83a59b731619f3d15f543dfe71f3a793cb8fbf9bd5d0d5d71"},
-    {file = "safetensors-0.4.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4c888bf71d5ca12a720f1ed87d407c4918afa022fb247a6546d8fac15b1f112b"},
-    {file = "safetensors-0.4.2-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:e6b2feb4b47226a16a792e6fac3f49442714884a3d4c1008569d5068a3941be9"},
-    {file = "safetensors-0.4.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f41cc0ee4b838ae8f4d8364a1b162067693d11a3893f0863be8c228d40e4d0ee"},
-    {file = "safetensors-0.4.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:51b7228e46c0a483c40ba4b9470dea00fb1ff8685026bb4766799000f6328ac2"},
-    {file = "safetensors-0.4.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:02697f8f2be8ca3c37a4958702dbdb1864447ef765e18b5328a1617022dcf164"},
-    {file = "safetensors-0.4.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:27fd8f65cf7c80e4280cae1ee6bcd85c483882f6580821abe71ee1a0d3dcfca7"},
-    {file = "safetensors-0.4.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:c487b5f113b0924c9534a07dc034830fb4ef05ce9bb6d78cfe016a7dedfe281f"},
-    {file = "safetensors-0.4.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:da7f6483f3fe67ff39b3a55552552c67930ea10a36e9f2539d36fc205273d767"},
-    {file = "safetensors-0.4.2-cp311-none-win32.whl", hash = "sha256:52a7012f6cb9cb4a132760b6308daede18a9f5f8952ce08adc7c67a7d865c2d8"},
-    {file = "safetensors-0.4.2-cp311-none-win_amd64.whl", hash = "sha256:4d1361a097ac430b310ce9eed8ed4746edee33ddafdfbb965debc8966fc34dc2"},
-    {file = "safetensors-0.4.2-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:77af8aa0edcc2863760fd6febbfdb82e88fd75d0e60c1ce4ba57208ba5e4a89b"},
-    {file = "safetensors-0.4.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:846666c1c5a8c8888d2dfda8d3921cb9cb8e2c5f78365be756c11021e75a0a2a"},
-    {file = "safetensors-0.4.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4f4bfc7ea19b446bfad41510d4b4c76101698c00caaa8a332c8edd8090a412ef"},
-    {file = "safetensors-0.4.2-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:233436fd30f27ffeb3c3780d0b84f496518868445c7a8db003639a649cc98453"},
-    {file = "safetensors-0.4.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:7a09237a795d11cd11f9dae505d170a29b5616151db1e10c14f892b11caadc7d"},
-    {file = "safetensors-0.4.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:de01c9a3a3b7b69627d624ff69d9f11d28ce9908eea2fb6245adafa4b1d43df6"},
-    {file = "safetensors-0.4.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8c1f25c5069ee42a5bcffdc66c300a407941edd73f3239e9fdefd26216407391"},
-    {file = "safetensors-0.4.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:7a73b3649456d09ca8506140d44484b63154a7378434cc1e8719f8056550b224"},
-    {file = "safetensors-0.4.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:e1625a8d07d046e968bd5c4961810aba1225984e4fb9243626f9d04a06ed3fee"},
-    {file = "safetensors-0.4.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8f74c86b25615cb24ad4cff765a2eefc09d71bf0fed97588cf585aad9c38fbb4"},
-    {file = "safetensors-0.4.2-cp312-none-win32.whl", hash = "sha256:8523b9c5777d771bcde5c2389c03f1cdf7ebe8797432a1bd5e345efe25c55987"},
-    {file = "safetensors-0.4.2-cp312-none-win_amd64.whl", hash = "sha256:dcff0243e1737a21f83d664c63fed89d1f532c23fc6830d0427279fabd789ccb"},
-    {file = "safetensors-0.4.2-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:96ad3d7d472612e26cbe413922b4fb13933310f0511d346ea5cc9a1e856e52eb"},
-    {file = "safetensors-0.4.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:88250922401b5ae4e37de929178caf46be47ed16c817b2237b81679bec07c120"},
-    {file = "safetensors-0.4.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d40443554142fc0ab30652d5cc8554c4b7a613513bde00373e18afd5de8cbe4b"},
-    {file = "safetensors-0.4.2-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:27f53f70106224d32d874aacecbeb4a6e4c5b16a1d2006d0e876d97229086d71"},
-    {file = "safetensors-0.4.2-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:cc068afe23734dfb26ce19db0a7877499ddf73b1d55ceb762417e8da4a1b05fb"},
-    {file = "safetensors-0.4.2-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9be1918eb8d43a11a6f8806759fccfa0eeb0542b12924caba66af8a7800ad01a"},
-    {file = "safetensors-0.4.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:41911087d20a7bbd78cb4ad4f98aab0c431533107584df6635d8b54b99945573"},
-    {file = "safetensors-0.4.2-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:50771c662aab909f31e94d048e76861fd027d66076ea773eef2e66c717766e24"},
-    {file = "safetensors-0.4.2-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:13f2e57be007b7ea9329133d2399e6bdfcf1910f655440a4da17df3a45afcd30"},
-    {file = "safetensors-0.4.2-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:c772147e6395bc829842e0a98e1b30c67fe25d816299c28196488511d5a5e951"},
-    {file = "safetensors-0.4.2-cp37-cp37m-macosx_10_12_x86_64.whl", hash = "sha256:36239a0060b537a3e8c473df78cffee14c3ec4f51d5f1a853af99371a2fb2a35"},
-    {file = "safetensors-0.4.2-cp37-cp37m-macosx_11_0_arm64.whl", hash = "sha256:d0cbb7664fad2c307f95195f951b7059e95dc23e0e1822e5978c8b500098543c"},
-    {file = "safetensors-0.4.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2b3e55adb6bd9dc1c2a341e72f48f075953fa35d173dd8e29a95b3b02d0d1462"},
-    {file = "safetensors-0.4.2-cp37-cp37m-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:42f743b3cca863fba53ca57a193f510e5ec359b97f38c282437716b6768e4a25"},
-    {file = "safetensors-0.4.2-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:04e6af4a6dbeb06c4e6e7d46cf9c716cbc4cc5ef62584fd8a7c0fe558562df45"},
-    {file = "safetensors-0.4.2-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a492ba21b5c8f14ee5ec9b20f42ba969e53ca1f909a4d04aad736b66a341dcc2"},
-    {file = "safetensors-0.4.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b25b8233a1a85dc67e39838951cfb01595d792f3b7b644add63edb652992e030"},
-    {file = "safetensors-0.4.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:fd27e063fbdafe776f7b1714da59110e88f270e86db00788a8fd65f4eacfeba7"},
-    {file = "safetensors-0.4.2-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:1b6fa399f251bbeb52029bf5a0ac2878d7705dd3612a2f8895b48e9c11f0367d"},
-    {file = "safetensors-0.4.2-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:de642d46b459e4afd5c2020b26c0d6d869a171ea00411897d5776c127cac74f0"},
-    {file = "safetensors-0.4.2-cp37-none-win32.whl", hash = "sha256:77b72d17754c93bb68f3598182f14d78776e0b9b31682ca5bb2c7c5bd9a75267"},
-    {file = "safetensors-0.4.2-cp37-none-win_amd64.whl", hash = "sha256:d36ee3244d461cd655aeef493792c3bccf4875282f8407fd9af99e9a41cf2530"},
-    {file = "safetensors-0.4.2-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:16b6b3884f7876c6b3b23a742428223a7170a5a9dac819d8c12a1569422c4b5a"},
-    {file = "safetensors-0.4.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:ee25d311493fbbe0be9d395faee46e9d79e8948f461e388ff39e59875ed9a350"},
-    {file = "safetensors-0.4.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:eed8097968585cd752a1171f86fce9aa1d89a29033e5cd8bec5a502e29f6b7af"},
-    {file = "safetensors-0.4.2-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:880e6865cf72cb67f9ab8d04a3c4b49dd95ae92fb1583929ce65aed94e1f685f"},
-    {file = "safetensors-0.4.2-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:91290f83daf80ce6d1a7f629b244443c200060a80f908b29d879021409e5ea94"},
-    {file = "safetensors-0.4.2-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3517d568486ab3508a7acc360b82d7a4a3e26b86efdf210a9ecd9d233c40708a"},
-    {file = "safetensors-0.4.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e1f43a77eb38540f782999e5dc5645164fe9027d3f0194f6c9a5126168017efa"},
-    {file = "safetensors-0.4.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:b684d9818aa5d63fddc65f7d0151968037d255d91adf74eba82125b41c680aaa"},
-    {file = "safetensors-0.4.2-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:ab1f5d84185f9fefaf21413efb764e4908057b8a9a0b987ede890c353490fd70"},
-    {file = "safetensors-0.4.2-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:2bd979642e6c3a517ef4b84ff36c2fee4015664fea05a61154fc565978347553"},
-    {file = "safetensors-0.4.2-cp38-none-win32.whl", hash = "sha256:11be6e7afed29e5a5628f0aa6214e34bc194da73f558dc69fc7d56e07037422a"},
-    {file = "safetensors-0.4.2-cp38-none-win_amd64.whl", hash = "sha256:2f7a6e5d29bd2cc340cffaa391fa437b1be9d21a2bd8b8724d2875d13a6ef2a9"},
-    {file = "safetensors-0.4.2-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:a5a921b4fe6925f9942adff3ebae8c16e0487908c54586a5a42f35b59fd69794"},
-    {file = "safetensors-0.4.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:b691727228c28f2d82d8a92b2bc26e7a1f129ee40b2f2a3185b5974e038ed47c"},
-    {file = "safetensors-0.4.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:91ca1056decc4e981248786e87b2a202d4841ee5f99d433f1adf3d44d4bcfa0e"},
-    {file = "safetensors-0.4.2-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:55969fd2e6fdb38dc221b0ab380668c21b0efa12a7562db9924759faa3c51757"},
-    {file = "safetensors-0.4.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6ae429bfaecc10ab5fe78c93009b3d1656c1581da560041e700eadb497dbe7a4"},
-    {file = "safetensors-0.4.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4ff88f194fe4ac50b463a4a6f0c03af9ad72eb5d24ec6d6730af59522e37fedb"},
-    {file = "safetensors-0.4.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a80cb48d0a447f8dd18e61813efa7d3f8f8d52edf0f05806abc0c59b83431f57"},
-    {file = "safetensors-0.4.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:b286fb7adfee70a4189898ac2342b8a67d5f493e6b21b0af89ca8eac1b967cbf"},
-    {file = "safetensors-0.4.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:0ceeff9ddbab4f78738489eb6682867ae946178776f33699737b2129b5394dc1"},
-    {file = "safetensors-0.4.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:a26fae748a7488cb3aac381eddfa818c42052c87b5e689fb4c6e82ed58cec209"},
-    {file = "safetensors-0.4.2-cp39-none-win32.whl", hash = "sha256:039a42ab33c9d68b39706fd38f1922ace26866eff246bf20271edb619f5f848b"},
-    {file = "safetensors-0.4.2-cp39-none-win_amd64.whl", hash = "sha256:b3a3e1f5b85859e398773f064943b62a4059f225008a2a8ee6add1edcf77cacf"},
-    {file = "safetensors-0.4.2-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:4e70d442ad17e8b153ef9095bf48ea64f15a66bf26dc2b6ca94660c154edbc24"},
-    {file = "safetensors-0.4.2-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:b90f1d9809caf4ff395951b4703295a68d12907f6945bbc3129e934ff8ae46f6"},
-    {file = "safetensors-0.4.2-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8c7ac9ad3728838006598e296b3ae9f27d80b489effd4685b92d97b3fc4c98f6"},
-    {file = "safetensors-0.4.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:de5730d77e6ff7f4c7039e20913661ad0ea2f86c09e71c039e73dfdd1f394f08"},
-    {file = "safetensors-0.4.2-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:44feb8cb156d6803dcd19fc6b81b27235f29b877660605a6ac35e1da7d64f0e4"},
-    {file = "safetensors-0.4.2-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:523a241c33e7c827ab9a3a23760d75c7d062f43dfe55b6b019409f89b0fb52d1"},
-    {file = "safetensors-0.4.2-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:fb18300e8eb74291225214f26c9a8ae2110fd61a6c9b5a2ff4c4e0eb1bb9a998"},
-    {file = "safetensors-0.4.2-pp37-pypy37_pp73-macosx_10_12_x86_64.whl", hash = "sha256:fe5437ff9fb116e44f2ab558981249ae63f978392b4576e62fcfe167d353edbc"},
-    {file = "safetensors-0.4.2-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d9304a0934ced5a5d272f39de36291dc141dfc152d277f03fb4d65f2fb2ffa7c"},
-    {file = "safetensors-0.4.2-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:160ba1b1e11cf874602c233ab80a14f588571d09556cbc3586900121d622b5ed"},
-    {file = "safetensors-0.4.2-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:04fcd6fcf7d9c13c7e5dc7e08de5e492ee4daa8f4ad74b4d8299d3eb0224292f"},
-    {file = "safetensors-0.4.2-pp37-pypy37_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:906d14c4a677d35834fb0f3a5455ef8305e1bba10a5e0f2e0f357b3d1ad989f2"},
-    {file = "safetensors-0.4.2-pp37-pypy37_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:df3fcdec0cd543084610d1f09c65cdb10fb3079f79bceddc092b0d187c6a265b"},
-    {file = "safetensors-0.4.2-pp38-pypy38_pp73-macosx_10_12_x86_64.whl", hash = "sha256:5ca76f13fb1cef242ea3ad2cb37388e7d005994f42af8b44bee56ba48b2d45ce"},
-    {file = "safetensors-0.4.2-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:278a1a3414c020785decdcd741c578725721274d2f9f787fcc930882e83b89cc"},
-    {file = "safetensors-0.4.2-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:05b5a461cc68ecd42d9d546e5e1268a39d8ede7934a68d1ce17c3c659cb829d6"},
-    {file = "safetensors-0.4.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c2341411412a41671d25e26bed59ec121e46bf4fadb8132895e610411c4b9681"},
-    {file = "safetensors-0.4.2-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:3497ac3895acf17c5f98197f1fa4769f09c5e7ede07fcb102f1c201e663e052c"},
-    {file = "safetensors-0.4.2-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:01b5e71d3754d2201294f1eb7a6d59cce3a5702ff96d83d226571b2ca2183837"},
-    {file = "safetensors-0.4.2-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:3627dbd1ea488dd8046a0491de5087f3c0d641e7acc80c0189a33c69398f1cd1"},
-    {file = "safetensors-0.4.2-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:9d56f0ef53afad26ec54ceede78a43e9a23a076dadbbda7b44d304c591abf4c1"},
-    {file = "safetensors-0.4.2-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:b259ca73d42daf658a1bda463f1f83885ae4d93a60869be80d7f7dfcc9d8bbb5"},
-    {file = "safetensors-0.4.2-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1ebc3cd401e4eb54e7c0a70346be565e81942d9a41fafd5f4bf7ab3a55d10378"},
-    {file = "safetensors-0.4.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5bc384a0309b706aa0425c93abb0390508a61bf029ce99c7d9df4220f25871a5"},
-    {file = "safetensors-0.4.2-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:af2d8f7235d8a08fbccfb8394387890e7fa38942b349a94e6eff13c52ac98087"},
-    {file = "safetensors-0.4.2-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:0911315bbcc5289087d063c2c2c7ccd711ea97a7e557a7bce005ac2cf80146aa"},
-    {file = "safetensors-0.4.2-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:1efe31673be91832d73439a2af426743e1395fc9ef7b081914e9e1d567bd7b5f"},
-    {file = "safetensors-0.4.2.tar.gz", hash = "sha256:acc85dcb09ec5e8aa787f588d7ad4d55c103f31e4ff060e17d92cc0e8b8cac73"},
-]
-
-[package.extras]
-all = ["safetensors[jax]", "safetensors[numpy]", "safetensors[paddlepaddle]", "safetensors[pinned-tf]", "safetensors[quality]", "safetensors[testing]", "safetensors[torch]"]
-dev = ["safetensors[all]"]
-jax = ["flax (>=0.6.3)", "jax (>=0.3.25)", "jaxlib (>=0.3.25)", "safetensors[numpy]"]
-mlx = ["mlx (>=0.0.9)"]
-numpy = ["numpy (>=1.21.6)"]
-paddlepaddle = ["paddlepaddle (>=2.4.1)", "safetensors[numpy]"]
-pinned-tf = ["safetensors[numpy]", "tensorflow (==2.11.0)"]
-quality = ["black (==22.3)", "click (==8.0.4)", "flake8 (>=3.8.3)", "isort (>=5.5.4)"]
-tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
-testing = ["h5py (>=3.7.0)", "huggingface_hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools_rust (>=1.5.2)"]
-torch = ["safetensors[numpy]", "torch (>=1.10)"]
-
 [[package]]
 name = "scikit-image"
 version = "0.22.0"
@@ -2135,48 +1713,6 @@ docs = ["PyWavelets (>=1.1.1)", "dask[array] (>=2022.9.2)", "ipykernel", "ipywid
 optional = ["PyWavelets (>=1.1.1)", "SimpleITK", "astropy (>=5.0)", "cloudpickle (>=0.2.1)", "dask[array] (>=2021.1.0)", "matplotlib (>=3.5)", "pooch (>=1.6.0)", "pyamg", "scikit-learn (>=1.1)"]
 test = ["asv", "matplotlib (>=3.5)", "numpydoc (>=1.5)", "pooch (>=1.6.0)", "pytest (>=7.0)", "pytest-cov (>=2.11.0)", "pytest-faulthandler", "pytest-localserver"]
 
-[[package]]
-name = "scikit-learn"
-version = "1.4.1.post1"
-description = "A set of python modules for machine learning and data mining"
-optional = false
-python-versions = ">=3.9"
-files = [
-    {file = "scikit-learn-1.4.1.post1.tar.gz", hash = "sha256:93d3d496ff1965470f9977d05e5ec3376fb1e63b10e4fda5e39d23c2d8969a30"},
-    {file = "scikit_learn-1.4.1.post1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c540aaf44729ab5cd4bd5e394f2b375e65ceaea9cdd8c195788e70433d91bbc5"},
-    {file = "scikit_learn-1.4.1.post1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:4310bff71aa98b45b46cd26fa641309deb73a5d1c0461d181587ad4f30ea3c36"},
-    {file = "scikit_learn-1.4.1.post1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9f43dd527dabff5521af2786a2f8de5ba381e182ec7292663508901cf6ceaf6e"},
-    {file = "scikit_learn-1.4.1.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c02e27d65b0c7dc32f2c5eb601aaf5530b7a02bfbe92438188624524878336f2"},
-    {file = "scikit_learn-1.4.1.post1-cp310-cp310-win_amd64.whl", hash = "sha256:629e09f772ad42f657ca60a1a52342eef786218dd20cf1369a3b8d085e55ef8f"},
-    {file = "scikit_learn-1.4.1.post1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6145dfd9605b0b50ae72cdf72b61a2acd87501369a763b0d73d004710ebb76b5"},
-    {file = "scikit_learn-1.4.1.post1-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:1afed6951bc9d2053c6ee9a518a466cbc9b07c6a3f9d43bfe734192b6125d508"},
-    {file = "scikit_learn-1.4.1.post1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ce03506ccf5f96b7e9030fea7eb148999b254c44c10182ac55857bc9b5d4815f"},
-    {file = "scikit_learn-1.4.1.post1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4ba516fcdc73d60e7f48cbb0bccb9acbdb21807de3651531208aac73c758e3ab"},
-    {file = "scikit_learn-1.4.1.post1-cp311-cp311-win_amd64.whl", hash = "sha256:78cd27b4669513b50db4f683ef41ea35b5dddc797bd2bbd990d49897fd1c8a46"},
-    {file = "scikit_learn-1.4.1.post1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:a1e289f33f613cefe6707dead50db31930530dc386b6ccff176c786335a7b01c"},
-    {file = "scikit_learn-1.4.1.post1-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:0df87de9ce1c0140f2818beef310fb2e2afdc1e66fc9ad587965577f17733649"},
-    {file = "scikit_learn-1.4.1.post1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:712c1c69c45b58ef21635360b3d0a680ff7d83ac95b6f9b82cf9294070cda710"},
-    {file = "scikit_learn-1.4.1.post1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1754b0c2409d6ed5a3380512d0adcf182a01363c669033a2b55cca429ed86a81"},
-    {file = "scikit_learn-1.4.1.post1-cp312-cp312-win_amd64.whl", hash = "sha256:1d491ef66e37f4e812db7e6c8286520c2c3fc61b34bf5e59b67b4ce528de93af"},
-    {file = "scikit_learn-1.4.1.post1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:aa0029b78ef59af22cfbd833e8ace8526e4df90212db7ceccbea582ebb5d6794"},
-    {file = "scikit_learn-1.4.1.post1-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:14e4c88436ac96bf69eb6d746ac76a574c314a23c6961b7d344b38877f20fee1"},
-    {file = "scikit_learn-1.4.1.post1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d7cd3a77c32879311f2aa93466d3c288c955ef71d191503cf0677c3340ae8ae0"},
-    {file = "scikit_learn-1.4.1.post1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2a3ee19211ded1a52ee37b0a7b373a8bfc66f95353af058a210b692bd4cda0dd"},
-    {file = "scikit_learn-1.4.1.post1-cp39-cp39-win_amd64.whl", hash = "sha256:234b6bda70fdcae9e4abbbe028582ce99c280458665a155eed0b820599377d25"},
-]
-
-[package.dependencies]
-joblib = ">=1.2.0"
-numpy = ">=1.19.5,<2.0"
-scipy = ">=1.6.0"
-threadpoolctl = ">=2.0.0"
-
-[package.extras]
-benchmark = ["matplotlib (>=3.3.4)", "memory-profiler (>=0.57.0)", "pandas (>=1.1.5)"]
-docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.3.4)", "memory-profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)", "sphinx (>=6.0.0)", "sphinx-copybutton (>=0.5.2)", "sphinx-gallery (>=0.15.0)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"]
-examples = ["matplotlib (>=3.3.4)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)"]
-tests = ["black (>=23.3.0)", "matplotlib (>=3.3.4)", "mypy (>=1.3)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "polars (>=0.19.12)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pyarrow (>=12.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.0.272)", "scikit-image (>=0.17.2)"]
-
 [[package]]
 name = "scipy"
 version = "1.12.0"
@@ -2219,27 +1755,6 @@ dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyl
 doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
 test = ["asv", "gmpy2", "hypothesis", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
-[[package]]
-name = "sentence-transformers"
-version = "2.6.0"
-description = "Multilingual text embeddings"
-optional = false
-python-versions = ">=3.8.0"
-files = [
-    {file = "sentence-transformers-2.6.0.tar.gz", hash = "sha256:cf519d311ddcc8ff84d78d18fae051985bef348716354e069e3d6d670e4db604"},
-    {file = "sentence_transformers-2.6.0-py3-none-any.whl", hash = "sha256:8807db6db0cf0a92f02be799b7c8260029ff91315d18f7b4b51c30b10d6b4fdb"},
-]
-
-[package.dependencies]
-huggingface-hub = ">=0.15.1"
-numpy = "*"
-Pillow = "*"
-scikit-learn = "*"
-scipy = "*"
-torch = ">=1.11.0"
-tqdm = "*"
-transformers = ">=4.32.0,<5.0.0"
-
 [[package]]
 name = "setuptools"
 version = "68.2.2"
@@ -2278,20 +1793,6 @@ files = [
     {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
 ]
 
-[[package]]
-name = "sympy"
-version = "1.12"
-description = "Computer algebra system (CAS) in Python"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "sympy-1.12-py3-none-any.whl", hash = "sha256:c3588cd4295d0c0f603d0f2ae780587e64e2efeedb3521e46b9bb1d08d184fa5"},
-    {file = "sympy-1.12.tar.gz", hash = "sha256:ebf595c8dac3e0fdc4152c51878b498396ec7f30e7a914d6071e674d49420fb8"},
-]
-
-[package.dependencies]
-mpmath = ">=0.19"
-
 [[package]]
 name = "tabulate"
 version = "0.9.0"
@@ -2306,17 +1807,6 @@ files = [
 [package.extras]
 widechars = ["wcwidth"]
 
-[[package]]
-name = "threadpoolctl"
-version = "3.4.0"
-description = "threadpoolctl"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "threadpoolctl-3.4.0-py3-none-any.whl", hash = "sha256:8f4c689a65b23e5ed825c8436a92b818aac005e0f3715f6a1664d7c7ee29d262"},
-    {file = "threadpoolctl-3.4.0.tar.gz", hash = "sha256:f11b491a03661d6dd7ef692dd422ab34185d982466c49c8f98c8f716b5c93196"},
-]
-
 [[package]]
 name = "tifffile"
 version = "2024.2.12"
@@ -2334,133 +1824,6 @@ numpy = "*"
 [package.extras]
 all = ["defusedxml", "fsspec", "imagecodecs (>=2023.8.12)", "lxml", "matplotlib", "zarr"]
 
-[[package]]
-name = "tokenizers"
-version = "0.15.2"
-description = ""
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "tokenizers-0.15.2-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:52f6130c9cbf70544287575a985bf44ae1bda2da7e8c24e97716080593638012"},
-    {file = "tokenizers-0.15.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:054c1cc9c6d68f7ffa4e810b3d5131e0ba511b6e4be34157aa08ee54c2f8d9ee"},
-    {file = "tokenizers-0.15.2-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:a9b9b070fdad06e347563b88c278995735292ded1132f8657084989a4c84a6d5"},
-    {file = "tokenizers-0.15.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ea621a7eef4b70e1f7a4e84dd989ae3f0eeb50fc8690254eacc08acb623e82f1"},
-    {file = "tokenizers-0.15.2-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:cf7fd9a5141634fa3aa8d6b7be362e6ae1b4cda60da81388fa533e0b552c98fd"},
-    {file = "tokenizers-0.15.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:44f2a832cd0825295f7179eaf173381dc45230f9227ec4b44378322d900447c9"},
-    {file = "tokenizers-0.15.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8b9ec69247a23747669ec4b0ca10f8e3dfb3545d550258129bd62291aabe8605"},
-    {file = "tokenizers-0.15.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:40b6a4c78da863ff26dbd5ad9a8ecc33d8a8d97b535172601cf00aee9d7ce9ce"},
-    {file = "tokenizers-0.15.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:5ab2a4d21dcf76af60e05af8063138849eb1d6553a0d059f6534357bce8ba364"},
-    {file = "tokenizers-0.15.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a47acfac7e511f6bbfcf2d3fb8c26979c780a91e06fb5b9a43831b2c0153d024"},
-    {file = "tokenizers-0.15.2-cp310-none-win32.whl", hash = "sha256:064ff87bb6acdbd693666de9a4b692add41308a2c0ec0770d6385737117215f2"},
-    {file = "tokenizers-0.15.2-cp310-none-win_amd64.whl", hash = "sha256:3b919afe4df7eb6ac7cafd2bd14fb507d3f408db7a68c43117f579c984a73843"},
-    {file = "tokenizers-0.15.2-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:89cd1cb93e4b12ff39bb2d626ad77e35209de9309a71e4d3d4672667b4b256e7"},
-    {file = "tokenizers-0.15.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:cfed5c64e5be23d7ee0f0e98081a25c2a46b0b77ce99a4f0605b1ec43dd481fa"},
-    {file = "tokenizers-0.15.2-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:a907d76dcfda37023ba203ab4ceeb21bc5683436ebefbd895a0841fd52f6f6f2"},
-    {file = "tokenizers-0.15.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:20ea60479de6fc7b8ae756b4b097572372d7e4032e2521c1bbf3d90c90a99ff0"},
-    {file = "tokenizers-0.15.2-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:48e2b9335be2bc0171df9281385c2ed06a15f5cf121c44094338306ab7b33f2c"},
-    {file = "tokenizers-0.15.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:112a1dd436d2cc06e6ffdc0b06d55ac019a35a63afd26475205cb4b1bf0bfbff"},
-    {file = "tokenizers-0.15.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4620cca5c2817177ee8706f860364cc3a8845bc1e291aaf661fb899e5d1c45b0"},
-    {file = "tokenizers-0.15.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ccd73a82751c523b3fc31ff8194702e4af4db21dc20e55b30ecc2079c5d43cb7"},
-    {file = "tokenizers-0.15.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:107089f135b4ae7817affe6264f8c7a5c5b4fd9a90f9439ed495f54fcea56fb4"},
-    {file = "tokenizers-0.15.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:0ff110ecc57b7aa4a594396525a3451ad70988e517237fe91c540997c4e50e29"},
-    {file = "tokenizers-0.15.2-cp311-none-win32.whl", hash = "sha256:6d76f00f5c32da36c61f41c58346a4fa7f0a61be02f4301fd30ad59834977cc3"},
-    {file = "tokenizers-0.15.2-cp311-none-win_amd64.whl", hash = "sha256:cc90102ed17271cf0a1262babe5939e0134b3890345d11a19c3145184b706055"},
-    {file = "tokenizers-0.15.2-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:f86593c18d2e6248e72fb91c77d413a815153b8ea4e31f7cd443bdf28e467670"},
-    {file = "tokenizers-0.15.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:0774bccc6608eca23eb9d620196687c8b2360624619623cf4ba9dc9bd53e8b51"},
-    {file = "tokenizers-0.15.2-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:d0222c5b7c9b26c0b4822a82f6a7011de0a9d3060e1da176f66274b70f846b98"},
-    {file = "tokenizers-0.15.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3835738be1de66624fff2f4f6f6684775da4e9c00bde053be7564cbf3545cc66"},
-    {file = "tokenizers-0.15.2-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:0143e7d9dcd811855c1ce1ab9bf5d96d29bf5e528fd6c7824d0465741e8c10fd"},
-    {file = "tokenizers-0.15.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:db35825f6d54215f6b6009a7ff3eedee0848c99a6271c870d2826fbbedf31a38"},
-    {file = "tokenizers-0.15.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3f5e64b0389a2be47091d8cc53c87859783b837ea1a06edd9d8e04004df55a5c"},
-    {file = "tokenizers-0.15.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9e0480c452217edd35eca56fafe2029fb4d368b7c0475f8dfa3c5c9c400a7456"},
-    {file = "tokenizers-0.15.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:a33ab881c8fe70474980577e033d0bc9a27b7ab8272896e500708b212995d834"},
-    {file = "tokenizers-0.15.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:a308a607ca9de2c64c1b9ba79ec9a403969715a1b8ba5f998a676826f1a7039d"},
-    {file = "tokenizers-0.15.2-cp312-none-win32.whl", hash = "sha256:b8fcfa81bcb9447df582c5bc96a031e6df4da2a774b8080d4f02c0c16b42be0b"},
-    {file = "tokenizers-0.15.2-cp312-none-win_amd64.whl", hash = "sha256:38d7ab43c6825abfc0b661d95f39c7f8af2449364f01d331f3b51c94dcff7221"},
-    {file = "tokenizers-0.15.2-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:38bfb0204ff3246ca4d5e726e8cc8403bfc931090151e6eede54d0e0cf162ef0"},
-    {file = "tokenizers-0.15.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:9c861d35e8286a53e06e9e28d030b5a05bcbf5ac9d7229e561e53c352a85b1fc"},
-    {file = "tokenizers-0.15.2-cp313-cp313-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:936bf3842db5b2048eaa53dade907b1160f318e7c90c74bfab86f1e47720bdd6"},
-    {file = "tokenizers-0.15.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:620beacc3373277700d0e27718aa8b25f7b383eb8001fba94ee00aeea1459d89"},
-    {file = "tokenizers-0.15.2-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:2735ecbbf37e52db4ea970e539fd2d450d213517b77745114f92867f3fc246eb"},
-    {file = "tokenizers-0.15.2-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:473c83c5e2359bb81b0b6fde870b41b2764fcdd36d997485e07e72cc3a62264a"},
-    {file = "tokenizers-0.15.2-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:968fa1fb3c27398b28a4eca1cbd1e19355c4d3a6007f7398d48826bbe3a0f728"},
-    {file = "tokenizers-0.15.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:865c60ae6eaebdde7da66191ee9b7db52e542ed8ee9d2c653b6d190a9351b980"},
-    {file = "tokenizers-0.15.2-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:7c0d8b52664ab2d4a8d6686eb5effc68b78608a9008f086a122a7b2996befbab"},
-    {file = "tokenizers-0.15.2-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:f33dfbdec3784093a9aebb3680d1f91336c56d86cc70ddf88708251da1fe9064"},
-    {file = "tokenizers-0.15.2-cp37-cp37m-macosx_10_12_x86_64.whl", hash = "sha256:d44ba80988ff9424e33e0a49445072ac7029d8c0e1601ad25a0ca5f41ed0c1d6"},
-    {file = "tokenizers-0.15.2-cp37-cp37m-macosx_11_0_arm64.whl", hash = "sha256:dce74266919b892f82b1b86025a613956ea0ea62a4843d4c4237be2c5498ed3a"},
-    {file = "tokenizers-0.15.2-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:0ef06b9707baeb98b316577acb04f4852239d856b93e9ec3a299622f6084e4be"},
-    {file = "tokenizers-0.15.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c73e2e74bbb07910da0d37c326869f34113137b23eadad3fc00856e6b3d9930c"},
-    {file = "tokenizers-0.15.2-cp37-cp37m-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:4eeb12daf02a59e29f578a865f55d87cd103ce62bd8a3a5874f8fdeaa82e336b"},
-    {file = "tokenizers-0.15.2-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9ba9f6895af58487ca4f54e8a664a322f16c26bbb442effd01087eba391a719e"},
-    {file = "tokenizers-0.15.2-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ccec77aa7150e38eec6878a493bf8c263ff1fa8a62404e16c6203c64c1f16a26"},
-    {file = "tokenizers-0.15.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f3f40604f5042ff210ba82743dda2b6aa3e55aa12df4e9f2378ee01a17e2855e"},
-    {file = "tokenizers-0.15.2-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:5645938a42d78c4885086767c70923abad047163d809c16da75d6b290cb30bbe"},
-    {file = "tokenizers-0.15.2-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:05a77cbfebe28a61ab5c3891f9939cc24798b63fa236d84e5f29f3a85a200c00"},
-    {file = "tokenizers-0.15.2-cp37-none-win32.whl", hash = "sha256:361abdc068e8afe9c5b818769a48624687fb6aaed49636ee39bec4e95e1a215b"},
-    {file = "tokenizers-0.15.2-cp37-none-win_amd64.whl", hash = "sha256:7ef789f83eb0f9baeb4d09a86cd639c0a5518528f9992f38b28e819df397eb06"},
-    {file = "tokenizers-0.15.2-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:4fe1f74a902bee74a3b25aff180fbfbf4f8b444ab37c4d496af7afd13a784ed2"},
-    {file = "tokenizers-0.15.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4c4b89038a684f40a6b15d6b09f49650ac64d951ad0f2a3ea9169687bbf2a8ba"},
-    {file = "tokenizers-0.15.2-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:d05a1b06f986d41aed5f2de464c003004b2df8aaf66f2b7628254bcbfb72a438"},
-    {file = "tokenizers-0.15.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:508711a108684111ec8af89d3a9e9e08755247eda27d0ba5e3c50e9da1600f6d"},
-    {file = "tokenizers-0.15.2-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:daa348f02d15160cb35439098ac96e3a53bacf35885072611cd9e5be7d333daa"},
-    {file = "tokenizers-0.15.2-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:494fdbe5932d3416de2a85fc2470b797e6f3226c12845cadf054dd906afd0442"},
-    {file = "tokenizers-0.15.2-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c2d60f5246f4da9373f75ff18d64c69cbf60c3bca597290cea01059c336d2470"},
-    {file = "tokenizers-0.15.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:93268e788825f52de4c7bdcb6ebc1fcd4a5442c02e730faa9b6b08f23ead0e24"},
-    {file = "tokenizers-0.15.2-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:6fc7083ab404019fc9acafe78662c192673c1e696bd598d16dc005bd663a5cf9"},
-    {file = "tokenizers-0.15.2-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:41e39b41e5531d6b2122a77532dbea60e171ef87a3820b5a3888daa847df4153"},
-    {file = "tokenizers-0.15.2-cp38-none-win32.whl", hash = "sha256:06cd0487b1cbfabefb2cc52fbd6b1f8d4c37799bd6c6e1641281adaa6b2504a7"},
-    {file = "tokenizers-0.15.2-cp38-none-win_amd64.whl", hash = "sha256:5179c271aa5de9c71712e31cb5a79e436ecd0d7532a408fa42a8dbfa4bc23fd9"},
-    {file = "tokenizers-0.15.2-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:82f8652a74cc107052328b87ea8b34291c0f55b96d8fb261b3880216a9f9e48e"},
-    {file = "tokenizers-0.15.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:02458bee6f5f3139f1ebbb6d042b283af712c0981f5bc50edf771d6b762d5e4f"},
-    {file = "tokenizers-0.15.2-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:c9a09cd26cca2e1c349f91aa665309ddb48d71636370749414fbf67bc83c5343"},
-    {file = "tokenizers-0.15.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:158be8ea8554e5ed69acc1ce3fbb23a06060bd4bbb09029431ad6b9a466a7121"},
-    {file = "tokenizers-0.15.2-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:1ddba9a2b0c8c81633eca0bb2e1aa5b3a15362b1277f1ae64176d0f6eba78ab1"},
-    {file = "tokenizers-0.15.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3ef5dd1d39797044642dbe53eb2bc56435308432e9c7907728da74c69ee2adca"},
-    {file = "tokenizers-0.15.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:454c203164e07a860dbeb3b1f4a733be52b0edbb4dd2e5bd75023ffa8b49403a"},
-    {file = "tokenizers-0.15.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0cf6b7f1d4dc59af960e6ffdc4faffe6460bbfa8dce27a58bf75755ffdb2526d"},
-    {file = "tokenizers-0.15.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:2ef09bbc16519f6c25d0c7fc0c6a33a6f62923e263c9d7cca4e58b8c61572afb"},
-    {file = "tokenizers-0.15.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:c9a2ebdd2ad4ec7a68e7615086e633857c85e2f18025bd05d2a4399e6c5f7169"},
-    {file = "tokenizers-0.15.2-cp39-none-win32.whl", hash = "sha256:918fbb0eab96fe08e72a8c2b5461e9cce95585d82a58688e7f01c2bd546c79d0"},
-    {file = "tokenizers-0.15.2-cp39-none-win_amd64.whl", hash = "sha256:524e60da0135e106b254bd71f0659be9f89d83f006ea9093ce4d1fab498c6d0d"},
-    {file = "tokenizers-0.15.2-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:6a9b648a58281c4672212fab04e60648fde574877d0139cd4b4f93fe28ca8944"},
-    {file = "tokenizers-0.15.2-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:7c7d18b733be6bbca8a55084027f7be428c947ddf871c500ee603e375013ffba"},
-    {file = "tokenizers-0.15.2-pp310-pypy310_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:13ca3611de8d9ddfbc4dc39ef54ab1d2d4aaa114ac8727dfdc6a6ec4be017378"},
-    {file = "tokenizers-0.15.2-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:237d1bf3361cf2e6463e6c140628e6406766e8b27274f5fcc62c747ae3c6f094"},
-    {file = "tokenizers-0.15.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:67a0fe1e49e60c664915e9fb6b0cb19bac082ab1f309188230e4b2920230edb3"},
-    {file = "tokenizers-0.15.2-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:4e022fe65e99230b8fd89ebdfea138c24421f91c1a4f4781a8f5016fd5cdfb4d"},
-    {file = "tokenizers-0.15.2-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:d857be2df69763362ac699f8b251a8cd3fac9d21893de129bc788f8baaef2693"},
-    {file = "tokenizers-0.15.2-pp37-pypy37_pp73-macosx_10_12_x86_64.whl", hash = "sha256:708bb3e4283177236309e698da5fcd0879ce8fd37457d7c266d16b550bcbbd18"},
-    {file = "tokenizers-0.15.2-pp37-pypy37_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:64c35e09e9899b72a76e762f9854e8750213f67567787d45f37ce06daf57ca78"},
-    {file = "tokenizers-0.15.2-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c1257f4394be0d3b00de8c9e840ca5601d0a4a8438361ce9c2b05c7d25f6057b"},
-    {file = "tokenizers-0.15.2-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:02272fe48280e0293a04245ca5d919b2c94a48b408b55e858feae9618138aeda"},
-    {file = "tokenizers-0.15.2-pp37-pypy37_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:dc3ad9ebc76eabe8b1d7c04d38be884b8f9d60c0cdc09b0aa4e3bcf746de0388"},
-    {file = "tokenizers-0.15.2-pp37-pypy37_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:32e16bdeffa7c4f46bf2152172ca511808b952701d13e7c18833c0b73cb5c23f"},
-    {file = "tokenizers-0.15.2-pp38-pypy38_pp73-macosx_10_12_x86_64.whl", hash = "sha256:fb16ba563d59003028b678d2361a27f7e4ae0ab29c7a80690efa20d829c81fdb"},
-    {file = "tokenizers-0.15.2-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:2277c36d2d6cdb7876c274547921a42425b6810d38354327dd65a8009acf870c"},
-    {file = "tokenizers-0.15.2-pp38-pypy38_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:1cf75d32e8d250781940d07f7eece253f2fe9ecdb1dc7ba6e3833fa17b82fcbc"},
-    {file = "tokenizers-0.15.2-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f1b3b31884dc8e9b21508bb76da80ebf7308fdb947a17affce815665d5c4d028"},
-    {file = "tokenizers-0.15.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b10122d8d8e30afb43bb1fe21a3619f62c3e2574bff2699cf8af8b0b6c5dc4a3"},
-    {file = "tokenizers-0.15.2-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:d88b96ff0fe8e91f6ef01ba50b0d71db5017fa4e3b1d99681cec89a85faf7bf7"},
-    {file = "tokenizers-0.15.2-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:37aaec5a52e959892870a7c47cef80c53797c0db9149d458460f4f31e2fb250e"},
-    {file = "tokenizers-0.15.2-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:e2ea752f2b0fe96eb6e2f3adbbf4d72aaa1272079b0dfa1145507bd6a5d537e6"},
-    {file = "tokenizers-0.15.2-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:4b19a808d8799fda23504a5cd31d2f58e6f52f140380082b352f877017d6342b"},
-    {file = "tokenizers-0.15.2-pp39-pypy39_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:64c86e5e068ac8b19204419ed8ca90f9d25db20578f5881e337d203b314f4104"},
-    {file = "tokenizers-0.15.2-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:de19c4dc503c612847edf833c82e9f73cd79926a384af9d801dcf93f110cea4e"},
-    {file = "tokenizers-0.15.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ea09acd2fe3324174063d61ad620dec3bcf042b495515f27f638270a7d466e8b"},
-    {file = "tokenizers-0.15.2-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:cf27fd43472e07b57cf420eee1e814549203d56de00b5af8659cb99885472f1f"},
-    {file = "tokenizers-0.15.2-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:7ca22bd897537a0080521445d91a58886c8c04084a6a19e6c78c586e0cfa92a5"},
-    {file = "tokenizers-0.15.2.tar.gz", hash = "sha256:e6e9c6e019dd5484be5beafc775ae6c925f4c69a3487040ed09b45e13df2cb91"},
-]
-
-[package.dependencies]
-huggingface_hub = ">=0.16.4,<1.0"
-
-[package.extras]
-dev = ["tokenizers[testing]"]
-docs = ["setuptools_rust", "sphinx", "sphinx_rtd_theme"]
-testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
-
 [[package]]
 name = "tomli"
 version = "2.0.1"
@@ -2472,59 +1835,6 @@ files = [
     {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
 ]
 
-[[package]]
-name = "torch"
-version = "2.1.2"
-description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
-optional = false
-python-versions = ">=3.8.0"
-files = [
-    {file = "torch-2.1.2-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:3a871edd6c02dae77ad810335c0833391c1a4ce49af21ea8cf0f6a5d2096eea8"},
-    {file = "torch-2.1.2-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:bef6996c27d8f6e92ea4e13a772d89611da0e103b48790de78131e308cf73076"},
-    {file = "torch-2.1.2-cp310-cp310-win_amd64.whl", hash = "sha256:0e13034fd5fb323cbbc29e56d0637a3791e50dd589616f40c79adfa36a5a35a1"},
-    {file = "torch-2.1.2-cp310-none-macosx_10_9_x86_64.whl", hash = "sha256:d9b535cad0df3d13997dbe8bd68ac33e0e3ae5377639c9881948e40794a61403"},
-    {file = "torch-2.1.2-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:f9a55d55af02826ebfbadf4e9b682f0f27766bc33df8236b48d28d705587868f"},
-    {file = "torch-2.1.2-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:a6ebbe517097ef289cc7952783588c72de071d4b15ce0f8b285093f0916b1162"},
-    {file = "torch-2.1.2-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:8f32ce591616a30304f37a7d5ea80b69ca9e1b94bba7f308184bf616fdaea155"},
-    {file = "torch-2.1.2-cp311-cp311-win_amd64.whl", hash = "sha256:e0ee6cf90c8970e05760f898d58f9ac65821c37ffe8b04269ec787aa70962b69"},
-    {file = "torch-2.1.2-cp311-none-macosx_10_9_x86_64.whl", hash = "sha256:76d37967c31c99548ad2c4d3f2cf191db48476f2e69b35a0937137116da356a1"},
-    {file = "torch-2.1.2-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:e2d83f07b4aac983453ea5bf8f9aa9dacf2278a8d31247f5d9037f37befc60e4"},
-    {file = "torch-2.1.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:f41fe0c7ecbf903a568c73486139a75cfab287a0f6c17ed0698fdea7a1e8641d"},
-    {file = "torch-2.1.2-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:e3225f47d50bb66f756fe9196a768055d1c26b02154eb1f770ce47a2578d3aa7"},
-    {file = "torch-2.1.2-cp38-cp38-win_amd64.whl", hash = "sha256:33d59cd03cb60106857f6c26b36457793637512998666ee3ce17311f217afe2b"},
-    {file = "torch-2.1.2-cp38-none-macosx_10_9_x86_64.whl", hash = "sha256:8e221deccd0def6c2badff6be403e0c53491805ed9915e2c029adbcdb87ab6b5"},
-    {file = "torch-2.1.2-cp38-none-macosx_11_0_arm64.whl", hash = "sha256:05b18594f60a911a0c4f023f38a8bda77131fba5fd741bda626e97dcf5a3dd0a"},
-    {file = "torch-2.1.2-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:9ca96253b761e9aaf8e06fb30a66ee301aecbf15bb5a303097de1969077620b6"},
-    {file = "torch-2.1.2-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:d93ba70f67b08c2ae5598ee711cbc546a1bc8102cef938904b8c85c2089a51a0"},
-    {file = "torch-2.1.2-cp39-cp39-win_amd64.whl", hash = "sha256:255b50bc0608db177e6a3cc118961d77de7e5105f07816585fa6f191f33a9ff3"},
-    {file = "torch-2.1.2-cp39-none-macosx_10_9_x86_64.whl", hash = "sha256:6984cd5057c0c977b3c9757254e989d3f1124f4ce9d07caa6cb637783c71d42a"},
-    {file = "torch-2.1.2-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:bc195d7927feabc0eb7c110e457c955ed2ab616f3c7c28439dd4188cf589699f"},
-]
-
-[package.dependencies]
-filelock = "*"
-fsspec = "*"
-jinja2 = "*"
-networkx = "*"
-nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-nvrtc-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-runtime-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cudnn-cu12 = {version = "8.9.2.26", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nccl-cu12 = {version = "2.18.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-sympy = "*"
-triton = {version = "2.1.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-typing-extensions = "*"
-
-[package.extras]
-dynamo = ["jinja2"]
-opt-einsum = ["opt-einsum (>=3.3)"]
-
 [[package]]
 name = "tqdm"
 version = "4.66.2"
@@ -2545,99 +1855,6 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
-[[package]]
-name = "transformers"
-version = "4.39.1"
-description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
-optional = false
-python-versions = ">=3.8.0"
-files = [
-    {file = "transformers-4.39.1-py3-none-any.whl", hash = "sha256:df167e08b27ab254044a38bb7c439461cd3916332205416e9b6b1592b517a1a5"},
-    {file = "transformers-4.39.1.tar.gz", hash = "sha256:ab9c1e1912843b9976e6cc62b27cd5434284fc0dab465e1b660333acfa81c6bc"},
-]
-
-[package.dependencies]
-filelock = "*"
-huggingface-hub = ">=0.19.3,<1.0"
-numpy = ">=1.17"
-packaging = ">=20.0"
-pyyaml = ">=5.1"
-regex = "!=2019.12.17"
-requests = "*"
-safetensors = ">=0.4.1"
-tokenizers = ">=0.14,<0.19"
-tqdm = ">=4.27"
-
-[package.extras]
-accelerate = ["accelerate (>=0.21.0)"]
-agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch"]
-all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision"]
-audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-codecarbon = ["codecarbon (==1.2.0)"]
-deepspeed = ["accelerate (>=0.21.0)", "deepspeed (>=0.9.3)"]
-deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.21.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
-dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
-dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.14,<0.19)", "urllib3 (<2.0.0)"]
-dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
-docs = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision"]
-docs-specific = ["hf-doc-builder"]
-flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
-flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-ftfy = ["ftfy"]
-integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
-ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
-modelcreation = ["cookiecutter (==1.7.3)"]
-natten = ["natten (>=0.14.6,<0.15.0)"]
-onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
-onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
-optuna = ["optuna"]
-quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"]
-ray = ["ray[tune] (>=2.7.0)"]
-retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
-sagemaker = ["sagemaker (>=2.31.0)"]
-sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
-serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
-sigopt = ["sigopt"]
-sklearn = ["scikit-learn"]
-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"]
-tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
-tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
-tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-timm = ["timm"]
-tokenizers = ["tokenizers (>=0.14,<0.19)"]
-torch = ["accelerate (>=0.21.0)", "torch"]
-torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch", "tqdm (>=4.27)"]
-video = ["av (==9.2.0)", "decord (==0.6.0)"]
-vision = ["Pillow (>=10.0.1,<=15.0)"]
-
-[[package]]
-name = "triton"
-version = "2.1.0"
-description = "A language and compiler for custom Deep Learning operations"
-optional = false
-python-versions = "*"
-files = [
-    {file = "triton-2.1.0-0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:66439923a30d5d48399b08a9eae10370f6c261a5ec864a64983bae63152d39d7"},
-    {file = "triton-2.1.0-0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:919b06453f0033ea52c13eaf7833de0e57db3178d23d4e04f9fc71c4f2c32bf8"},
-    {file = "triton-2.1.0-0-cp37-cp37m-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ae4bb8a91de790e1866405211c4d618379781188f40d5c4c399766914e84cd94"},
-    {file = "triton-2.1.0-0-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:39f6fb6bdccb3e98f3152e3fbea724f1aeae7d749412bbb1fa9c441d474eba26"},
-    {file = "triton-2.1.0-0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:21544e522c02005a626c8ad63d39bdff2f31d41069592919ef281e964ed26446"},
-    {file = "triton-2.1.0-0-pp37-pypy37_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:143582ca31dd89cd982bd3bf53666bab1c7527d41e185f9e3d8a3051ce1b663b"},
-    {file = "triton-2.1.0-0-pp38-pypy38_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:82fc5aeeedf6e36be4e4530cbdcba81a09d65c18e02f52dc298696d45721f3bd"},
-    {file = "triton-2.1.0-0-pp39-pypy39_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:81a96d110a738ff63339fc892ded095b31bd0d205e3aace262af8400d40b6fa8"},
-]
-
-[package.dependencies]
-filelock = "*"
-
-[package.extras]
-build = ["cmake (>=3.18)", "lit"]
-tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
-tutorials = ["matplotlib", "pandas", "tabulate"]
-
 [[package]]
 name = "types-pillow"
 version = "9.5.0.6"
@@ -2793,5 +2010,5 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.9,<3.12"
-content-hash = "37e5b5d42e2c18f1d5741fc2efd9aa4d31dbd4413d09a7d43462e6a1a669531d"
+python-versions = ">=3.9"
+content-hash = "5c57abb9e41b66ee6195875318971f2c2ce858879efca8f5edefb2226284ee6c"
diff --git a/pyproject.toml b/pyproject.toml
index 0670265f..9e37d2c1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "vision-agent"
-version = "0.1.5"
+version = "0.2.0"
 description = "Toolset for Vision Agent"
 authors = ["Landing AI <dev@landing.ai>"]
 readme = "README.md"
@@ -16,16 +16,12 @@ packages = [{include = "vision_agent"}]
 "documentation" = "https://github.com/landing-ai/vision-agent"
 
 [tool.poetry.dependencies]  # main dependency group
-python = ">=3.9,<3.12"
-
+python = ">=3.9"
 numpy = ">=1.21.0,<2.0.0"
 pillow = "10.*"
 requests = "2.*"
 tqdm = ">=4.64.0,<5.0.0"
 pandas = "2.*"
-faiss-cpu = "1.*"
-torch = "2.1.*"  # 2.2 causes sentence-transformers to seg fault
-sentence-transformers = "2.*"
 openai = "1.*"
 typing_extensions = "4.*"
 moviepy = "1.*"
diff --git a/tests/test_data.py b/tests/test_data.py
deleted file mode 100644
index 046fc0fb..00000000
--- a/tests/test_data.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import shutil
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import pytest
-
-from vision_agent.data import DataStore, build_data_store
-
-
-@pytest.fixture(autouse=True)
-def clean_up():
-    yield
-    for p in Path(".").glob("test_save*"):
-        if p.is_dir():
-            shutil.rmtree(p)
-        else:
-            p.unlink()
-
-
-@pytest.fixture
-def small_ds():
-    df = pd.DataFrame({"image_paths": ["path1", "path2"]})
-    ds = DataStore(df)
-    return ds
-
-
-@pytest.fixture
-def small_ds_with_index(small_ds):
-    small_ds.add_embedder(TestEmb())
-    small_ds.add_lmm(TestLMM())
-    small_ds.add_column("test", "test prompt")
-    small_ds.build_index("test")
-    return small_ds
-
-
-class TestLMM:
-    def generate(self, _, **kwargs):
-        return "test"
-
-
-class TestEmb:
-    def embed(self, _):
-        return np.random.randn(128).astype(np.float32)
-
-
-def test_initialize_data_store(small_ds):
-    assert small_ds is not None
-    assert "image_id" in small_ds.df.columns
-    assert "image_paths" in small_ds.df.columns
-
-
-def test_initialize_data_store_with_no_data():
-    df = pd.DataFrame({"x": ["path1"]})
-    with pytest.raises(ValueError):
-        DataStore(df)
-
-
-def test_build_data_store():
-    ds = build_data_store(["path1", "path2"])
-    assert isinstance(ds, DataStore)
-    assert "image_id" in ds.df.columns
-
-
-def test_add_index_no_emb(small_ds):
-    with pytest.raises(ValueError):
-        small_ds.build_index("test")
-
-
-def test_add_column_no_lmm(small_ds):
-    with pytest.raises(ValueError):
-        small_ds.add_column("test", "test prompt")
-
-
-def test_search(small_ds_with_index):
-    results = small_ds_with_index.search("test", top_k=1)
-    assert len(results) == 1
-
-
-def test_save(small_ds_with_index):
-    small_ds_with_index.save("test_save")
-
-    assert Path("test_save").exists()
-    assert Path("test_save/data.csv").exists()
-    assert Path("test_save/data.index").exists()
-
-
-def test_load(small_ds_with_index):
-    small_ds_with_index.save("test_save")
-
-    second_ds = DataStore.load("test_save")
-    assert second_ds.df.equals(small_ds_with_index.df)
-    assert second_ds.index is not None
diff --git a/vision_agent/__init__.py b/vision_agent/__init__.py
index 360b8a78..bf71560f 100644
--- a/vision_agent/__init__.py
+++ b/vision_agent/__init__.py
@@ -1,5 +1,3 @@
 from .agent import Agent
-from .data import DataStore, build_data_store
-from .emb import Embedder, OpenAIEmb, SentenceTransformerEmb, get_embedder
 from .llm import LLM, OpenAILLM
 from .lmm import LMM, LLaVALMM, OpenAILMM, get_lmm
diff --git a/vision_agent/data/__init__.py b/vision_agent/data/__init__.py
deleted file mode 100644
index 050da913..00000000
--- a/vision_agent/data/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .data import DataStore, build_data_store
diff --git a/vision_agent/data/data.py b/vision_agent/data/data.py
deleted file mode 100644
index 54125772..00000000
--- a/vision_agent/data/data.py
+++ /dev/null
@@ -1,142 +0,0 @@
-from __future__ import annotations
-
-import uuid
-from pathlib import Path
-from typing import Callable, Dict, List, Optional, Union, cast
-
-import faiss
-import numpy as np
-import numpy.typing as npt
-import pandas as pd
-from faiss import read_index, write_index
-from tqdm import tqdm
-from typing_extensions import Self
-
-from vision_agent.emb import Embedder
-from vision_agent.lmm import LMM
-
-tqdm.pandas()
-
-
-class DataStore:
-    r"""A class to store and manage image data along with its generated metadata from an LMM."""
-
-    def __init__(self, df: pd.DataFrame):
-        r"""Initializes the DataStore with a DataFrame containing image paths and image
-        IDs. If the image IDs are not present, they are generated using UUID4. The
-        DataFrame must contain an 'image_paths' column.
-
-        Args:
-            df: The DataFrame containing "image_paths" and "image_id" columns.
-        """
-        self.df = df
-        self.lmm: Optional[LMM] = None
-        self.emb: Optional[Embedder] = None
-        self.index: Optional[faiss.IndexFlatIP] = None  # type: ignore
-        if "image_paths" not in self.df.columns:
-            raise ValueError("image_paths column must be present in DataFrame")
-        if "image_id" not in self.df.columns:
-            self.df["image_id"] = [str(uuid.uuid4()) for _ in range(len(df))]
-
-    def add_embedder(self, emb: Embedder) -> Self:
-        self.emb = emb
-        return self
-
-    def add_lmm(self, lmm: LMM) -> Self:
-        self.lmm = lmm
-        return self
-
-    def add_column(
-        self, name: str, prompt: str, func: Optional[Callable[[str], str]] = None
-    ) -> Self:
-        r"""Adds a new column to the DataFrame containing the generated metadata from
-        the LMM.
-
-        Args:
-            name: The name of the column to be added.
-            prompt: The prompt to be used to generate the metadata.
-            func: A Python function to be applied on the output of `lmm.generate`.
-                Defaults to None.
-        """
-        if self.lmm is None:
-            raise ValueError("LMM not set yet")
-
-        self.df[name] = self.df["image_paths"].progress_apply(  # type: ignore
-            lambda x: (
-                func(self.lmm.generate(prompt, images=[x]))
-                if func
-                else self.lmm.generate(prompt, images=[x])
-            )
-        )
-        return self
-
-    def build_index(self, target_col: str) -> Self:
-        r"""This will generate embeddings for the `target_col` and build a searchable
-        index over them, so next time you run search it will search over this index.
-
-        Args:
-            target_col: The column name containing the data to be indexed."""
-        if self.emb is None:
-            raise ValueError("Embedder not set yet")
-
-        embeddings: pd.Series = self.df[target_col].progress_apply(lambda x: self.emb.embed(x))  # type: ignore
-        embeddings_np = np.array(embeddings.tolist()).astype(np.float32)
-        self.index = faiss.IndexFlatIP(embeddings_np.shape[1])
-        self.index.add(embeddings_np)
-        return self
-
-    def get_embeddings(self) -> npt.NDArray[np.float32]:
-        if self.index is None:
-            raise ValueError("Index not built yet")
-
-        ntotal = self.index.ntotal
-        d: int = self.index.d
-        return cast(
-            npt.NDArray[np.float32],
-            faiss.rev_swig_ptr(self.index.get_xb(), ntotal * d).reshape(ntotal, d),
-        )
-
-    def search(self, query: str, top_k: int = 10) -> List[Dict]:
-        r"""Searches the index for the most similar images to the query and returns
-        the top_k results.
-
-        Args:
-            query: The query to search for.
-            top_k: The number of results to return. Defaults to 10."""
-        if self.index is None:
-            raise ValueError("Index not built yet")
-        if self.emb is None:
-            raise ValueError("Embedder not set yet")
-
-        query_embedding: npt.NDArray[np.float32] = self.emb.embed(query)
-        _, idx = self.index.search(query_embedding.reshape(1, -1), top_k)
-        return cast(List[Dict], self.df.iloc[idx[0]].to_dict(orient="records"))
-
-    def save(self, path: Union[str, Path]) -> None:
-        path = Path(path)
-        path.mkdir(parents=True)
-        self.df.to_csv(path / "data.csv")
-        if self.index is not None:
-            write_index(self.index, str(path / "data.index"))
-
-    @classmethod
-    def load(cls, path: Union[str, Path]) -> DataStore:
-        path = Path(path)
-        df = pd.read_csv(path / "data.csv", index_col=0)
-        ds = DataStore(df)
-        if Path(path / "data.index").exists():
-            ds.index = read_index(str(path / "data.index"))
-        return ds
-
-
-def build_data_store(data: Union[str, Path, list[Union[str, Path]]]) -> DataStore:
-    if isinstance(data, Path) or isinstance(data, str):
-        data = Path(data)
-        data_files = list(Path(data).glob("*"))
-    elif isinstance(data, list):
-        data_files = [Path(d) for d in data]
-
-    df = pd.DataFrame()
-    df["image_paths"] = data_files
-    df["image_id"] = [uuid.uuid4() for _ in range(len(data_files))]
-    return DataStore(df)
diff --git a/vision_agent/emb/__init__.py b/vision_agent/emb/__init__.py
deleted file mode 100644
index 729b96f8..00000000
--- a/vision_agent/emb/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .emb import Embedder, OpenAIEmb, SentenceTransformerEmb, get_embedder
diff --git a/vision_agent/emb/emb.py b/vision_agent/emb/emb.py
deleted file mode 100644
index 6186ddb4..00000000
--- a/vision_agent/emb/emb.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import cast
-
-import numpy as np
-import numpy.typing as npt
-
-
-class Embedder(ABC):
-    @abstractmethod
-    def embed(self, text: str) -> npt.NDArray[np.float32]:
-        pass
-
-
-class SentenceTransformerEmb(Embedder):
-    def __init__(self, model_name: str = "BAAI/bge-small-en-v1.5"):
-        from sentence_transformers import SentenceTransformer
-
-        self.model = SentenceTransformer(model_name)
-
-    def embed(self, text: str) -> npt.NDArray[np.float32]:
-        return cast(
-            npt.NDArray[np.float32],
-            self.model.encode([text]).flatten().astype(np.float32),
-        )
-
-
-class OpenAIEmb(Embedder):
-    def __init__(self, model_name: str = "text-embedding-3-small"):
-        from openai import OpenAI
-
-        self.client = OpenAI()
-        self.model_name = model_name
-
-    def embed(self, text: str) -> npt.NDArray[np.float32]:
-        response = self.client.embeddings.create(input=text, model=self.model_name)
-        return np.array(response.data[0].embedding).astype(np.float32)
-
-
-def get_embedder(name: str) -> Embedder:
-    if name == "sentence-transformer":
-        return SentenceTransformerEmb()
-    elif name == "openai":
-        return OpenAIEmb()
-    else:
-        raise ValueError(
-            f"Unknown embedder name: {name}, currently support sentence-transformer, openai."
-        )

From 501b95dc1ab4066bc36f6fb36a268b357c3a46f5 Mon Sep 17 00:00:00 2001
From: GitHub Actions Bot <yazhou.cao@landing.ai>
Date: Mon, 22 Apr 2024 21:17:08 +0000
Subject: [PATCH 5/5] [skip ci] chore(release): vision-agent 0.2.1

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 9e37d2c1..ec52ac3a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "vision-agent"
-version = "0.2.0"
+version = "0.2.1"
 description = "Toolset for Vision Agent"
 authors = ["Landing AI <dev@landing.ai>"]
 readme = "README.md"