Update app.py

app.py

@@ -1,29 +1,27 @@
 import gradio as gr
 import torch
+from ultralytics import YOLO
 from PIL import Image
 import numpy as np
+import sys, os
 
-# Import YOLO
-from ultralytics import YOLO
+# ---- Add yolov12 fork folder to path ----
+sys.path.append(os.path.join(os.path.dirname(__file__), "yolov12-main"))
 
-# ---- Monkey-patch for YOLOv12 fork ----
-# Make sure the yolov12 fork is in the repo folder
-from yolov12.models.modules.attn import Attn  # forked class
+# ---- Import forked Attn and patch YOLO ----
+from yolov12.models.modules.attn import Attn  # inside yolov12-main
 import ultralytics.nn.modules.block as block
-block.AAttn = Attn  # patch original AAttn
+block.AAttn = Attn  # replace original AAttn
 
-# ---- Load your trained model ----
-model = YOLO("best.pt")  # path to your YOLOv12 weights
+# ---- Load model ----
+model = YOLO("best.pt")  # make sure best.pt is in same folder
 
 # ---- Inference function ----
 def detect_objects(image):
-    # Convert PIL image to array
     img = np.array(image)
-    # Run YOLOv12 inference
     results = model(img)
-    # Get annotated image
-    annotated_img = results[0].plot()
-    return Image.fromarray(annotated_img)
+    annotated = results[0].plot()
+    return Image.fromarray(annotated)
 
 # ---- Gradio interface ----
 iface = gr.Interface(
@@ -31,8 +29,7 @@ iface = gr.Interface(
     inputs=gr.Image(type="pil"),
     outputs=gr.Image(type="pil"),
     title="Ear Condition Detection (YOLOv12 Fork)",
-    description="Upload an image of an ear. The model detects conditions using the YOLOv12 fork."
+    description="Uses forked YOLOv12 model to detect ear conditions."
 )
 
-# Launch Gradio app
 iface.launch()