Adding bounding box data to output

app.py

@@ -7,6 +7,7 @@ import uvicorn
 import PIL.Image as Image
 from fastapi import FastAPI, UploadFile, File, HTTPException, Request
 from pydantic import BaseModel
+from typing import List
 from ultralytics import YOLO
 from weld_tiling import detect_tiled_softnms
 
@@ -37,6 +38,7 @@ model = YOLO("best_7-15-25.pt")
 
 class PredictResponse(BaseModel):
     detections: dict
+    bounding_boxes: List[List[float]]
 
 class PredictQuery(BaseModel):
     image_base64: str
@@ -75,7 +77,7 @@ def detect_weld_types(image_bgr: np.ndarray, model) -> dict:
         final_conf=0.38, device=None, imgsz=1280
     )
     counts = normalize_prediction(out)
-    return counts
+    return counts, out['xyxy']
 
 # -----------------------------
 # Endpoints
@@ -105,8 +107,10 @@ async def predict_multipart(file: UploadFile = File(default=None)):
 
     img_rgb = downscale_if_needed(pil_to_numpy_rgb(img))
     img_bgr = numpy_rgb_to_bgr(img_rgb)
-    welds = detect_weld_types(img_bgr, model)
-    return PredictResponse(detections=welds)
+    welds, boxes = detect_weld_types(img_bgr, model)
+    # Convert numpy array to list of lists for JSON serialization
+    boxes_list = boxes.tolist() if isinstance(boxes, np.ndarray) else boxes
+    return PredictResponse(detections=welds, bounding_boxes=boxes_list)
 
 @app.post("/ping")
 async def ping():