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from typing import Dict, List, Optional, Tuple, Type, Any
from pathlib import Path
import uuid
import tempfile
import matplotlib.pyplot as plt
import torch
from PIL import Image
from pydantic import BaseModel, Field
from transformers import AutoModelForCausalLM, AutoProcessor, BitsAndBytesConfig
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
class XRayPhraseGroundingInput(BaseModel):
"""Input schema for the XRay Phrase Grounding Tool. Only supports JPG or PNG images."""
image_path: str = Field(
...,
description="Path to the frontal chest X-ray image file, only supports JPG or PNG images",
)
phrase: str = Field(
...,
description="Medical finding or condition to locate in the image (e.g., 'Pleural effusion')",
)
max_new_tokens: int = Field(default=300, description="Maximum number of new tokens to generate")
class XRayPhraseGroundingTool(BaseTool):
"""Tool for grounding medical findings in chest X-ray images using the MAIRA-2 model.
This tool processes chest X-ray images and locates specific medical findings mentioned
in the input phrase. It returns both the bounding box coordinates and a visualization
of the finding's location in the image.
"""
name: str = "xray_phrase_grounding"
description: str = (
"Locates and visualizes specific medical findings in chest X-ray images. "
"Takes a chest X-ray image and medical phrase to locate (e.g., 'Pleural effusion', 'Cardiomegaly'). "
"Returns bounding box coordinates in format [x_topleft, y_topleft, x_bottomright, y_bottomright] "
"where each value is between 0-1 representing relative position in the image, "
"a visualization of the finding's location, and confidence metadata. "
"Example input: {'image_path': '/path/to/xray.png', 'phrase': 'Pleural effusion', 'max_new_tokens': 300}"
)
args_schema: Type[BaseModel] = XRayPhraseGroundingInput
model: Any = None
processor: Any = None
device: str = "cuda"
temp_dir: Path = None
def __init__(
self,
model_path: str = "microsoft/maira-2",
cache_dir: Optional[str] = None,
temp_dir: Optional[str] = None,
load_in_4bit: bool = False,
load_in_8bit: bool = False,
device: Optional[str] = "cuda",
):
"""Initialize the XRay Phrase Grounding Tool."""
super().__init__()
self.device = torch.device(device) if device else "cuda"
# Setup quantization config
if load_in_4bit:
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
)
elif load_in_8bit:
quantization_config = BitsAndBytesConfig(
load_in_8bit=True,
)
else:
quantization_config = None
# Load model
self.model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map=self.device,
cache_dir=cache_dir,
trust_remote_code=True,
quantization_config=quantization_config,
)
self.processor = AutoProcessor.from_pretrained(
model_path, cache_dir=cache_dir, trust_remote_code=True
)
self.model = self.model.eval()
self.temp_dir = Path(temp_dir if temp_dir else tempfile.mkdtemp())
self.temp_dir.mkdir(exist_ok=True)
def _visualize_bboxes(
self, image: Image.Image, bboxes: List[Tuple[float, float, float, float]], phrase: str
) -> str:
"""Create and save visualization of multiple bounding boxes on the image."""
plt.figure(figsize=(12, 12))
plt.imshow(image, cmap="gray")
for bbox in bboxes:
x1, y1, x2, y2 = bbox
width = x2 - x1
height = y2 - y1
plt.gca().add_patch(
plt.Rectangle(
(x1 * image.width, y1 * image.height),
width * image.width,
height * image.height,
fill=False,
color="red",
linewidth=2,
)
)
plt.title(f"Located: {phrase}", pad=20)
plt.axis("off")
viz_path = self.temp_dir / f"grounding_{uuid.uuid4().hex[:8]}.png"
plt.savefig(viz_path, bbox_inches="tight", dpi=150)
plt.close()
return str(viz_path)
def _run(
self,
image_path: str,
phrase: str,
max_new_tokens: int = 300,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> Tuple[Dict[str, Any], Dict]:
"""Ground a medical finding phrase in an X-ray image.
Args:
image_path: Path to the chest X-ray image file
phrase: Medical finding to locate in the image
max_new_tokens: Maximum number of new tokens to generate
run_manager: Optional callback manager
Returns:
Tuple[Dict, Dict]: Output dictionary and metadata dictionary
"""
try:
image = Image.open(image_path)
if image.mode != "RGB":
image = image.convert("RGB")
inputs = self.processor.format_and_preprocess_phrase_grounding_input(
frontal_image=image, phrase=phrase, return_tensors="pt"
)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
with torch.no_grad():
output = self.model.generate(
**inputs,
max_new_tokens=max_new_tokens,
use_cache=True,
)
prompt_length = inputs["input_ids"].shape[-1]
decoded_text = self.processor.decode(
output[0][prompt_length:], skip_special_tokens=True
)
predictions = self.processor.convert_output_to_plaintext_or_grounded_sequence(
decoded_text
)
metadata = {
"image_path": image_path,
"original_size": image.size,
"model_input_size": tuple(inputs["pixel_values"].shape[-2:]),
"device": str(self.device),
"analysis_status": "completed",
}
if not predictions:
output = {
"predictions": [],
"visualization_path": None,
}
metadata["analysis_status"] = "completed_no_finding"
return output, metadata
# Process multiple predictions
processed_predictions = []
for pred_phrase, pred_bboxes in predictions:
if not pred_bboxes: # Skip if no bounding boxes
continue
# Convert model bboxes to list format and get original image bboxes
model_bboxes = [list(bbox) for bbox in pred_bboxes]
original_bboxes = [
self.processor.adjust_box_for_original_image_size(
bbox, width=image.size[0], height=image.size[1]
)
for bbox in model_bboxes
]
processed_predictions.append(
{
"phrase": pred_phrase,
"bounding_boxes": {
"model_coordinates": model_bboxes,
"image_coordinates": original_bboxes,
},
}
)
# Create visualization with all bounding boxes
if processed_predictions:
all_bboxes = []
for pred in processed_predictions:
all_bboxes.extend(pred["bounding_boxes"]["image_coordinates"])
viz_path = self._visualize_bboxes(image, all_bboxes, phrase)
else:
viz_path = None
metadata["analysis_status"] = "completed_no_finding"
output = {
"predictions": processed_predictions,
"visualization_path": viz_path,
}
return output, metadata
except Exception as e:
output = {"error": str(e)}
metadata = {
"image_path": image_path,
"analysis_status": "failed",
"error_details": str(e),
}
return output, metadata
async def _arun(
self,
image_path: str,
phrase: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> Tuple[Dict[str, Any], Dict]:
"""Asynchronous version of _run."""
return self._run(image_path, phrase, run_manager)
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