Update app.py

app.py

@@ -23,8 +23,9 @@ models = {
 
 def identify_objects(input_img):
     global models
-    if input_img is None: return None, "请上传图片"
-    
+    if input_img is None: 
+        return None, json.dumps({"error": "请上传图片"}, ensure_ascii=False)
+        
     try:
         if "object_detector" not in models:
             from transformers import pipeline
@@ -33,12 +34,14 @@ def identify_objects(input_img):
                 model="hustvl/yolos-tiny",
                 device=-1
             )
-        
+            
         img_pil = input_img.convert("RGB")
         results = models["object_detector"](img_pil)
         
         draw = ImageDraw.Draw(img_pil)
-        detection_text = "识别结果如下：\n" # 用于存储输出到文本框的文字
+        
+        # 准备一个列表存储结构化数据
+        detections = []
         
         count = 0
         for res in results:
@@ -48,19 +51,38 @@ def identify_objects(input_img):
                 label = res["label"]
                 score = res["score"]
                 
-                # 1. 绘制逻辑保持不变
+                # 绘制逻辑
                 draw.rectangle([box["xmin"], box["ymin"], box["xmax"], box["ymax"]], outline="red", width=4)
                 
-                # 2. 拼接文本信息
-                detection_text += f"{count}. 目标: {label} | 置信度: {score:.2%}\n"
+                # 将信息添加到列表
+                detections.append({
+                    "id": count,
+                    "label": label,
+                    "confidence": round(score, 4), # 保留4位小数
+                    "box": {
+                        "xmin": int(box["xmin"]),
+                        "ymin": int(box["ymin"]),
+                        "xmax": int(box["xmax"]),
+                        "ymax": int(box["ymax"])
+                    }
+                })
         
-        if count == 0:
-            detection_text = "未识别到显著目标（置信度 > 50%）"
-            
-        return img_pil, detection_text # 返回两个对象
+        # 构造最终的 JSON 响应
+        output_json = {
+            "status": "success",
+            "count": count,
+            "detections": detections
+        }
+        
+        # 使用 json.dumps 转换为字符串，ensure_ascii=False 保证中文不乱码
+        return img_pil, json.dumps(output_json, ensure_ascii=False, indent=4)
 
     except Exception as e:
-        return input_img, f"识别失败: {str(e)}"
+        error_json = {
+            "status": "error",
+            "message": str(e)
+        }
+        return input_img, json.dumps(error_json, ensure_ascii=False)
 
 def process_rembg(input_img):
     global models