feat: 更新视频分析功能，支持提示词模板，提升复刻项目的灵活性和性能

.gitignore

@@ -52,3 +52,4 @@ Thumbs.db
 # Temporary files
 *.tmp
 *.temp
+.qiniu_pythonsdk_hostscache.json

Dockerfile

@@ -43,5 +43,5 @@ EXPOSE 8000
 HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \
     CMD curl -f http://localhost:8000/docs || exit 1
 
-# 启动命令
+# 启动命令 - 使用多worker提升并发性能
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000", "--app-dir", "src"]
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000", "--app-dir", "src", "--workers", "4", "--loop", "uvloop"]

src/api/dependencies.py

@@ -90,8 +90,8 @@ class AIServiceImpl(AIService):
     async def generate_video(self, frame_image_bytes: bytes, shot_prompt: str):
         return await self.gemini_client.generate_video(frame_image_bytes, shot_prompt)
     
-    async def analyze_video(self, video_url: str):
+    async def analyze_video(self, video_url: str, prompt_template: str):
-        return await self.gemini_client.analyze_video(video_url)
+        return await self.gemini_client.analyze_video(video_url, prompt_template)
 
 
 class StorageServiceImpl(StorageService):

src/api/routes/projects.py

@@ -434,8 +434,14 @@ async def replicate_from_video(
 ):
     """一键复刻：从视频URL生成项目、素材和分镜"""
     try:
+        # 视频分析提示词模板不需要特定的占位符变量，因为视频内容直接传给AI模型
+        # 这里可以添加其他模板格式验证逻辑，如果需要的话
+        
         # 调用业务逻辑
-        result = await project_use_cases.replicate_from_video(request.video_url)
+        result = await project_use_cases.replicate_from_video(
+            request.video_url, 
+            request.prompt_template
+        )
         
         project = result["project"]
         assets = result["assets"]

src/application/schemas/project.py

@@ -157,6 +157,7 @@ class ComposeVideoResponse(BaseModel):
 class VideoReplicateRequest(BaseModel):
     """一键复刻请求模式"""
     video_url: str = Field(..., description="要复刻的视频URL")
+    prompt_template: str = Field(..., description="视频分析提示词模板")
 
 
 # 更新引用

src/application/use_cases/project_use_cases.py

@@ -590,12 +590,13 @@ class ProjectUseCases:
                 logger.error(f"生成视频失败: {e}")
                 raise ValueError(f"生成视频失败: {e}")
     
-    async def replicate_from_video(self, video_url: str) -> Dict[str, Any]:
+    async def replicate_from_video(self, video_url: str, prompt_template: str) -> Dict[str, Any]:
         """
         一键复刻：从视频URL生成项目、素材和分镜
         
         Args:
             video_url: 要复刻的视频URL
+            prompt_template: 视频分析提示词模板
             
         Returns:
             包含project、assets、storyboards的字典
@@ -605,7 +606,7 @@ class ProjectUseCases:
             
             # 1. 使用Gemini分析视频内容
             logger.info("正在分析视频内容...")
-            analysis_result = await self.ai_service.analyze_video(video_url)
+            analysis_result = await self.ai_service.analyze_video(video_url, prompt_template)
             if not analysis_result:
                 raise ValueError("视频分析失败")
             

src/domain/services/ai_service.py

@@ -110,12 +110,13 @@ class AIService(ABC):
         pass
     
     @abstractmethod
-    async def analyze_video(self, video_url: str) -> Optional[Dict[str, Any]]:
+    async def analyze_video(self, video_url: str, prompt_template: str) -> Optional[Dict[str, Any]]:
         """
         分析视频内容，提取关键素材帧和分镜关键帧
         
         Args:
             video_url: 视频URL
+            prompt_template: 视频分析提示词模板
             
         Returns:
             分析结果字典，包含key_assets_frames和key_storyboard_frames

src/infrastructure/external/gemini_client.py

@@ -11,6 +11,7 @@ from google.genai import types
 from ..config import settings
 from ..utils import safe_json_loads
 from .key_pool_manager import key_pool_manager
+from ..services.template_service import TemplateService
 from loguru import logger
 import ssl
 import urllib3
@@ -217,7 +218,7 @@ class GeminiClient:
 
             # 调用Veo-3.0 API生成视频
             operation = self._current_client.models.generate_videos(
-                model="veo-3.0-fast-generate-preview", #veo-3.0-generate-preview
+                model="veo-3.0-generate-preview", #veo-3.0-fast-generate-preview
                 prompt=video_prompt,
                 image=image_input
             )
@@ -252,12 +253,13 @@ class GeminiClient:
             logger.error(f"Veo-3.0视频生成失败: {e}")
             raise e
     
-    async def analyze_video(self, video_url: str) -> Optional[Dict[str, Any]]:
+    async def analyze_video(self, video_url: str, prompt_template: str) -> Optional[Dict[str, Any]]:
         """
         分析视频内容，提取关键素材帧和分镜关键帧
         
         Args:
             video_url: 视频URL
+            prompt_template: 视频分析提示词模板
             
         Returns:
             分析结果字典，包含key_assets_frames和key_storyboard_frames
@@ -308,37 +310,8 @@ class GeminiClient:
                     
                     return myfile
                 
-                # 构建分析提示词
+                # 直接使用提示词模板作为分析提示词
-                analysis_prompt = """
+                analysis_prompt = prompt_template
-                请仔细分析这个视频，并返回以下JSON格式的结果：
-                {
-                    "title": "为这个视频生成一个简洁有吸引力的标题，不超过20个字符",
-                    "script": "根据视频内容生成的完整剧本，包含对话、动作、场景描述等",
-                    "key_assets_frames": [
-                        {
-                            "timestamp": "HH:MM:SS",
-                            "name": "素材名称",
-                            "description": "素材描述",
-                            "tags": ["标签1", "标签2"]
-                        }
-                    ],
-                    "key_storyboard_frames": [
-                        {
-                            "timestamp": "HH:MM:SS",
-                            "frame_prompt": "该帧画面描述",
-                            "shot_prompt": "该关键帧到下一关键帧之间的剧情描述"
-                        }
-                    ]
-                }
-                
-                要求：
-                1. title: 根据视频主题和内容生成简洁有吸引力的标题，要能概括视频核心内容，不超过20个字符
-                2. script: 根据视频内容生成完整的剧本，包含场景描述、角色对话、动作指导等，要生动详细
-                3. key_assets_frames: 提取3-5个关键素材帧，包含视觉元素如角色、场景、道具、动物等
-                4. key_storyboard_frames: 提取分镜关键帧，约每8秒一帧
-                5. timestamp格式必须是HH:MM:SS
-                6. 确保返回的是有效的JSON格式，不要包含其他文字
-                """
                 
                 myfile = self._execute_with_retry(_upload_and_analyze)
                 

src/infrastructure/external/openrouter_client.py

@@ -21,7 +21,7 @@ class OpenRouterClient:
     async def generate_text(
         self, 
         prompt: str, 
-        model: str = "anthropic/claude-3.5-sonnet"
+        model: str = "google/gemini-2.5-pro"
     ) -> Optional[str]:
         """
         生成文本内容
@@ -66,7 +66,7 @@ class OpenRouterClient:
         self, 
         prompt_template: str, 
         script_or_idea: str,
-        model: str = "anthropic/claude-3.5-sonnet"
+        model: str = "google/gemini-2.5-pro"
     ) -> Optional[Dict[str, Any]]:
         """
         生成完整剧本和素材信息
@@ -107,7 +107,7 @@ class OpenRouterClient:
         text: str, 
         source_lang: str = "zh", 
         target_lang: str = "en",
-        model: str = "anthropic/claude-3.5-sonnet"
+        model: str = "google/gemini-2.5-pro"
     ) -> Optional[str]:
         """
         翻译文本