Update app.py

app.py

@@ -4,43 +4,53 @@ import gradio as gr
 from transformers import pipeline
 from PIL import Image, ImageDraw
 
-# Load the DETR object-detection pipeline (CPU)
+# Load DETR object‐detection pipeline (requires timm in requirements)
 detector = pipeline("object-detection", model="facebook/detr-resnet-50", device=-1)
 
 def detect_objects(image: Image.Image):
-    # Run object detection
     outputs = detector(image)
 
-    # Draw bounding boxes
     annotated = image.convert("RGB")
     draw = ImageDraw.Draw(annotated)
     table = []
+
     for obj in outputs:
-        # DETR returns box as [xmin, ymin, xmax, ymax]
-        xmin, ymin, xmax, ymax = obj["box"]
+        box = obj["box"]
+        # DETR pipeline may return box as dict or list
+        if isinstance(box, dict):
+            xmin = int(box.get("xmin", box.get("x", 0)))
+            ymin = int(box.get("ymin", box.get("y", 0)))
+            xmax = int(box.get("xmax", xmin))
+            ymax = int(box.get("ymax", ymin))
+        else:
+            # assume [x, y, w, h]
+            x, y, w, h = box
+            xmin, ymin = int(x), int(y)
+            xmax, ymax = int(x + w), int(y + h)
+
         label = obj["label"]
         score = round(obj["score"], 3)
 
-        # draw box and label
+        # draw box & label
         draw.rectangle([xmin, ymin, xmax, ymax], outline="red", width=2)
-        draw.text((xmin, ymin - 10), f"{label} ({score})", fill="red")
+        draw.text((xmin, max(ymin - 10, 0)), f"{label} ({score})", fill="red")
 
         table.append([label, score])
 
-    # Return the annotated image and a table of detections
     return annotated, table
 
 with gr.Blocks(title="📷✨ Object Detection Demo") as demo:
     gr.Markdown(
         """
         # 📷✨ Object Detection  
-        Upload an image and let DETR (a Transformer-based model) identify objects in real time.
+        Upload an image and let DETR identify objects on CPU.
         """
     )
 
     with gr.Row():
-        img_in  = gr.Image(type="pil", label="Upload Image")
-        detect_btn = gr.Button("Detect Objects 🔍", variant="primary")
+        img_in = gr.Image(type="pil", label="Upload Image")
+        btn    = gr.Button("Detect Objects 🔍", variant="primary")
+
     img_out   = gr.Image(label="Annotated Image")
     table_out = gr.Dataframe(
         headers=["Label", "Score"],
@@ -50,7 +60,7 @@ with gr.Blocks(title="📷✨ Object Detection Demo") as demo:
         label="Detections"
     )
 
-    detect_btn.click(detect_objects, inputs=img_in, outputs=[img_out, table_out])
+    btn.click(detect_objects, inputs=img_in, outputs=[img_out, table_out])
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0")
+    demo.launch(server_name="0.0.0.0")