Update app.py

app.py

@@ -1,38 +1,39 @@
 import gradio as gr
 from transformers import pipeline
-from PIL import Image, ImageDraw
+from PIL import Image, ImageDraw, ImageFont
 
 # Load object detection pipeline
 model_pipeline = pipeline(
     task="object-detection", 
-    model="bortle/autotrain-ap-obj-detector-1"
+    model="bortle/autotrain-ap-obj-detector-2"
 )
 
 def predict(image):
-    # Resize the image to width 1080, maintaining aspect ratio
     width = 1080
     ratio = width / image.width
     height = int(image.height * ratio)
-    resized_image = image.resize((width, height))
+    image = image.resize((width, height))
 
-    # Run object detection
-    detections = model_pipeline(resized_image)
+    detections = model_pipeline(image)
 
-    # Draw detections on image
-    draw = ImageDraw.Draw(resized_image)
+    # Draw boxes
+    draw = ImageDraw.Draw(image)
     for det in detections:
         box = det["box"]
-        label = f'{det["label"]}: {det["score"]:.2f}'
+        label = f'{det["label"]} ({det["score"]:.2f})'
+        print(f"Drawing box: {box} Label: {label}")  # Debug
+
+        # Draw rectangle
         draw.rectangle(
             [(box["xmin"], box["ymin"]), (box["xmax"], box["ymax"])],
             outline="red",
             width=3
         )
-        draw.text((box["xmin"], box["ymin"] - 10), label, fill="red")
+        # Optional: draw label
+        draw.text((box["xmin"] + 2, box["ymin"] - 10), label, fill="red")
 
-    return resized_image
+    return image
 
-# Gradio Interface
 gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil", label="Upload Astrophotography Image"),