调试

google_ai/generativeai_video.py

@@ -164,39 +164,39 @@ async def main(video_path, api_key, prompt, mark, sample_data):
             if isinstance( model, str ):
                 return model, mark
             print("创建缓存模型成功")
-            sample_data = {
-                "一、基础信息": {
-                    "视觉/音乐/文字": "",
-                    "内容选题": "",
-                    "视频主题": ""
-                },
-                "二、主体和场景": {
-                    "视频主体": "",
-                    "视频场景": []
-                },
-                "三、情感与风格": {},
-                "四、视频传播性与观众": {
-                    "片尾引导": {},
-                    "传播性判断": "",
-                    "观众画像": {}
-                },
-                "五、音画细节": {
-                    "音频细节": {},
-                    "视频水印": {},
-                    "视频字幕": {},
-                    "视频口播": ""
-                },
-                "六、人物与场景": {
-                    "知名人物": {},
-                    "人物年龄段": "",
-                    "场景描述": []
-                },
-                "七、时效性与分类": {
-                    "时效性": {},
-                    "视频一级分类": "",
-                    "二级分类": ["品类- 、分数-", "品类- 、分数-", "品类- 、分数-"]
-                }
-            }
+            # sample_data = {
+            #     "一、基础信息": {
+            #         "视觉/音乐/文字": "",
+            #         "内容选题": "",
+            #         "视频主题": ""
+            #     },
+            #     "二、主体和场景": {
+            #         "视频主体": "",
+            #         "视频场景": []
+            #     },
+            #     "三、情感与风格": {},
+            #     "四、视频传播性与观众": {
+            #         "片尾引导": {},
+            #         "传播性判断": "",
+            #         "观众画像": {}
+            #     },
+            #     "五、音画细节": {
+            #         "音频细节": {},
+            #         "视频水印": {},
+            #         "视频字幕": {},
+            #         "视频口播": ""
+            #     },
+            #     "六、人物与场景": {
+            #         "知名人物": {},
+            #         "人物年龄段": "",
+            #         "场景描述": []
+            #     },
+            #     "七、时效性与分类": {
+            #         "时效性": {},
+            #         "视频一级分类": "",
+            #         "二级分类": ["品类- 、分数-", "品类- 、分数-", "品类- 、分数-"]
+            #     }
+            # }
             response =await analyzer.analyze_video( model, prompt, sample_data )
             if isinstance( response, str ):
                 return response, mark

video_processing/video_processing.py

@@ -12,12 +12,45 @@ from common.feishu_data import Material
 class VideoProcessing:
     def get_ai_data(self, video_path):
         mark, prompt = Material.feishu_list()
-
+        sample_data = {
+            "一、基础信息": {
+                "视觉/音乐/文字": "",
+                "内容选题": "",
+                "视频主题": ""
+            },
+            "二、主体和场景": {
+                "视频主体": "",
+                "视频场景": []
+            },
+            "三、情感与风格": {},
+            "四、视频传播性与观众": {
+                "片尾引导": {},
+                "传播性判断": "",
+                "观众画像": {}
+            },
+            "五、音画细节": {
+                "音频细节": {},
+                "视频水印": {},
+                "视频字幕": {},
+                "视频口播": ""
+            },
+            "六、人物与场景": {
+                "知名人物": {},
+                "人物年龄段": "",
+                "场景描述": []
+            },
+            "七、时效性与分类": {
+                "时效性": {},
+                "视频一级分类": "",
+                "二级分类": ["品类- 、分数-", "品类- 、分数-", "品类- 、分数-"]
+            }
+        }
         url = "http://8.219.186.16:8080/process_video/"
         payload = json.dumps( {
             "video_path": video_path,
             "prompt": prompt,
             "mark": str(mark),
+            "sample_data": str(sample_data)
         } )
         headers = {
             'Content-Type': 'application/json'