危険度判定の正確な実装

app.py

@@ -984,7 +984,13 @@ async def mosaic_face(file: UploadFile = File(...)):
 
 
 
-
+async def classify_image_llm2(file: UploadFile = File(...)):
+    image_path = "./temp_image.jpg"
+    # アップロードされた画像を保存
+    with open(image_path, "wb") as buffer:
+        buffer.write(await file.read())
+    danger_level = ObjectDetector().detect_danger_level(image_path)
+    return {"danger":danger_level}
 
 
 
@@ -1113,9 +1119,11 @@ async def analyze(
         unique_id = uuid.uuid4().hex
         input_path = save_image(file.file, f"input_{timestamp}_{unique_id}.jpg")
         if mode== 'auto':
-            mask_path,response =await llm_to_process_image_simple_auto(risk_level, input_path, point1, point2,thresholds=thresholds)
-        else:
+            mask_path,response =await llm_to_process_image_simple_auto(risk_level, input_path, point1, point2,thresholds=thresholds,scene=scene)
+            response = response['risk_level']  # レスポンスから必要な情報を抽出
+        elif mode == 'fast':
             mask_path = llm_to_process_image_simple(risk_level, input_path, point1, point2, thresholds=thresholds)
+            response=await classify_image_llm2(file)
         output_path = f"./output_simple_lama_{timestamp}_{unique_id}.jpg"
         print(f'point1,point2: {point1},{point2}')  # 消去したくない範囲
 
@@ -1125,7 +1133,7 @@ async def analyze(
         return FileResponse(
             output_path,
             media_type="image/jpeg",
-            headers={"x-danger": "0"}  # 実際の処理では危険度を適切に設定
+            headers={"x-danger": str(response)}  # 実際の処理では危険度を適切に設定
         )
 
     except Exception as e: