クラスの定義

detector.py

@@ -0,0 +1,36 @@
+
+from LLM_package import GeminiInference 
+import json
+class ObjectDetector:
+    def __init__(self, model_path):
+        self.model  = GeminiInference() 
+        self.prompt_objects=None
+        self.text=None
+        self.prompt= f"""
+    Detect all {self.prompt_objects} in the image. The box_2d should be [ymin, xmin, ymax, xmax] normalized to 0-1000.
+    Please provide the response as a JSON array of objects, where each object has a 'label' and 'box_2d' field.
+    Example:
+    [
+        {{"label": "face", "box_2d": [100, 200, 300, 400]}},
+        {{"label": "license_plate", "box_2d": [500, 600, 700, 800]}}
+    ]
+    """
+    def detect_objects(self, image_path):
+        detected_objects_norm_0_1= self.model.parse_response(self.model.get_response(image_path, self.prompt))        return detected_objects_norm_0_1
+    def detect_danger_level(self, image_path):
+        """
+        Detects the danger level of the image.
+        """
+        analysis_prompt = f"""
+            画像の個人情報漏洩リスクを分析し、厳密にJSON形式で返答してください。なおこの時、資料があれば、資料を参考にしてください：
+            {{
+                "risk_level": "high|medium|low",
+                "risk_reason": "リスクの具体的理由",
+                "objects_to_remove": ["消去すべきオブジェクトリスト(英語で、例: 'face', 'license_plate')"]
+            }}
+            <資料>
+            {self.text if self.text else "なし"}
+            </資料>
+            """
+        response = json.loads(self.model.get_response_text(image_path, analysis_prompt))
+        return response