Update app.py

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 from transformers import pipeline
-from PIL import Image, ImageDraw, ImageFont
+from PIL import Image, ImageDraw
 
 # Load object detection pipeline
 model_pipeline = pipeline(
@@ -14,30 +14,41 @@ def predict(image):
     height = int(image.height * ratio)
     image = image.resize((width, height))
 
-    detections = model_pipeline(image)
+    detections = model_pipeline(image, threshold=0.1)
 
-    # Draw boxes
     draw = ImageDraw.Draw(image)
+    table_rows = []
+
     for det in detections:
         box = det["box"]
-        label = f'{det["label"]} ({det["score"]:.2f})'
-        print(f"Drawing box: {box} Label: {label}")  # Debug
+        label = det["label"]
+        score = round(det["score"], 4)
+        table_rows.append({
+            "Class": label,
+            "Confidence": f"{score:.2%}",
+            "Xmin": int(box["xmin"]),
+            "Ymin": int(box["ymin"]),
+            "Xmax": int(box["xmax"]),
+            "Ymax": int(box["ymax"]),
+        })
 
-        # Draw rectangle
         draw.rectangle(
             [(box["xmin"], box["ymin"]), (box["xmax"], box["ymax"])],
             outline="red",
             width=3
         )
-        # Optional: draw label
-        draw.text((box["xmin"] + 2, box["ymin"] - 10), label, fill="red")
+        draw.text((box["xmin"] + 4, box["ymin"] - 12), f"{label} ({score:.2f})", fill="red")
 
-    return image
+    return image, table_rows
 
+# Gradio Interface
 gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil", label="Upload Astrophotography Image"),
-    outputs=gr.Image(type="pil", label="Detected Objects"),
+    outputs=[
+        gr.Image(type="pil", label="Detected Objects"),
+        gr.Dataframe(headers=["Class", "Confidence", "Xmin", "Ymin", "Xmax", "Ymax"], label="Detections")
+    ],
     title="Astrophotography Object Detector",
     allow_flagging="manual",
 ).launch()