Update app.py

app.py

@@ -22,31 +22,54 @@ def predict(img):
     pad_h = max_size - original_size[1]
     
     # 创建方形画布并保持宽高比
-    padded_img = Image.new('RGB', (max_size, max_size), (114, 114, 114))  # 使用灰色填充
+    padded_img = Image.new('RGB', (max_size, max_size), (114, 114, 114))
     padded_img.paste(input_image, (pad_w//2, pad_h//2))
     
     # 转换为numpy数组并进行预测
     img_array = np.array(padded_img)
     
     # 进行预测
-    results = model.predict(
-        img_array
-    )
+    results = model.predict(img_array)
+    result = results[0]
     
     # 获取预测结果
-    result_img = results[0].plot()
+    result_img = result.plot()
     
     # 裁剪回原始尺寸
     if pad_w > 0 or pad_h > 0:
         result_img = result_img[pad_h//2:pad_h//2 + original_size[1], 
                               pad_w//2:pad_w//2 + original_size[0]]
     
-    return result_img
+    # 处理检测信息
+    info = {
+        "detected": len(result.boxes) > 0,
+        "count": len(result.boxes),
+        "confidences": []
+    }
+    
+    if info["detected"]:
+        # 获取每个检测框的置信度
+        for box in result.boxes:
+            conf = float(box.conf[0])
+            info["confidences"].append(f"{conf:.2%}")
+    
+    # 生成输出文本
+    output_text = f"""检测结果:
+- 是否检测到目标: {'是' if info['detected'] else '否'}
+- 检测到的目标数量: {info['count']}"""
+    
+    if info["confidences"]:
+        output_text += f"\n- 置信度: {', '.join(info['confidences'])}"
+    
+    return result_img, output_text
 
 demo = gr.Interface(
     fn=predict,
     inputs=gr.Image(label="输入图片"),
-    outputs=gr.Image(label="检测结果", type="numpy"),
+    outputs=[
+        gr.Image(label="检测结果", type="numpy"),
+        gr.Textbox(label="检测信息")
+    ],
     title="🐉 奶龙杀手 (NailongKiller)",
     description="上传图片来检测奶龙 | Upload an image to detect Nailong",
     examples=[["example1.jpg"]],