.gitignore

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

Dockerfile

@@ -1,47 +0,0 @@
-# 使用Python 3.11官方镜像作为基础镜像
-FROM python:3.11-slim
-
-# 设置工作目录
-WORKDIR /app
-
-# 设置环境变量
-ENV PYTHONDONTWRITEBYTECODE=1 \
-    PYTHONUNBUFFERED=1 \
-    PYTHONPATH=/app/src
-
-# 安装系统依赖，包括ffmpeg
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    libpq-dev \
-    curl \
-    ffmpeg \
-    && rm -rf /var/lib/apt/lists/*
-
-# 验证ffmpeg安装
-RUN ffmpeg -version
-
-# 复制requirements文件并安装Python依赖
-COPY requirements.txt .
-RUN pip install --no-cache-dir --upgrade pip && \
-    pip install --no-cache-dir -r requirements.txt
-
-# 复制项目文件
-COPY . .
-
-# 创建日志目录
-RUN mkdir -p /app/logs
-
-# 创建非root用户
-RUN useradd --create-home --shell /bin/bash app && \
-    chown -R app:app /app
-USER app
-
-# 暴露端口
-EXPOSE 8000
-
-# 健康检查
-HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \
-    CMD curl -f http://localhost:8000/docs || exit 1
-
-# 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", "--workers", "4", "--loop", "uvloop"]

env.example

@@ -8,8 +8,6 @@ QINIU_BUCKET_NAME="your_qiniu_bucket_name"
 QINIU_DOMAIN="your_qiniu_cdn_domain"
 
 # AI Models
-# Google API Keys，支持单个或多个key（用逗号分隔），用于处理限流重试
-GOOGLE_API_KEYS="your_google_ai_api_key"
-# 多个key示例: GOOGLE_API_KEYS="key1,key2,key3"
+GOOGLE_API_KEY="your_google_ai_api_key"
 OPENROUTER_API_KEY="your_openrouter_api_key"
 OPENROUTER_BASE_URL="https://openrouter.ai/api/v1"

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, prompt_template: str):
-        return await self.gemini_client.analyze_video(video_url, prompt_template)
+    async def analyze_video(self, video_url: str):
+        return await self.gemini_client.analyze_video(video_url)
 
 
 class StorageServiceImpl(StorageService):

src/api/routes/projects.py

@@ -434,14 +434,8 @@ 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, 
-            request.prompt_template
-        )
+        result = await project_use_cases.replicate_from_video(request.video_url)
         
         project = result["project"]
         assets = result["assets"]

src/application/schemas/project.py

@@ -157,7 +157,6 @@ 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

@@ -590,13 +590,12 @@ 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, prompt_template: str) -> Dict[str, Any]:
+    async def replicate_from_video(self, video_url: 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
@@ -606,7 +605,7 @@ 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, prompt_template)
+            analysis_result = await self.ai_service.analyze_video(video_url)
             if not analysis_result:
                 raise ValueError("视频分析失败")
             

src/domain/services/ai_service.py

@@ -110,13 +110,12 @@ class AIService(ABC):
         pass
     
     @abstractmethod
-    async def analyze_video(self, video_url: str, prompt_template: str) -> Optional[Dict[str, Any]]:
+    async def analyze_video(self, video_url: 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/config.py

@@ -18,7 +18,7 @@ class Settings(BaseSettings):
     qiniu_domain: str
     
     # AI Models
-    google_api_keys: str
+    google_api_key: str
     openrouter_api_key: str
     openrouter_base_url: str = "https://openrouter.ai/api/v1"
     

src/infrastructure/external/gemini_client.py

@@ -10,78 +10,17 @@ from google import genai
 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
 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 
+
+client = genai.Client(api_key=settings.google_api_key)
+
 class GeminiClient:
     """Gemini AI客户端"""
     
-    def __init__(self):
-        self._current_client = None
-        self._refresh_client()
-    
-    def _refresh_client(self):
-        """刷新客户端，使用当前key"""
-        current_key = key_pool_manager.get_current_key()
-        self._current_client = genai.Client(api_key=current_key)
-    
-    def _execute_with_retry(self, func, *args, **kwargs):
-        """
-        执行函数并处理429错误重试
-        
-        Args:
-            func: 要执行的函数
-            *args: 函数参数
-            **kwargs: 函数关键字参数
-            
-        Returns:
-            函数执行结果
-            
-        Raises:
-            Exception: 所有key都尝试过后仍然失败
-        """
-        key_pool_manager.reset_to_first_key()  # 每次请求都从第一个key开始
-        self._refresh_client()
-        
-        last_exception = None
-        tried_keys = 0
-        max_keys = len(key_pool_manager.get_all_keys())
-        
-        while tried_keys < max_keys:
-            try:
-                tried_keys += 1
-                logger.info(f"使用第{tried_keys}个key尝试请求")
-                return func(*args, **kwargs)
-                
-            except Exception as e:
-                last_exception = e
-                error_str = str(e).lower()
-                
-                # 检查是否是429错误
-                if '429' in error_str or 'rate limit' in error_str or 'quota' in error_str:
-                    logger.warning(f"遇到限流错误: {e}")
-                    
-                    # 如果还有更多key可以尝试
-                    if key_pool_manager.switch_to_next_key():
-                        self._refresh_client()
-                        logger.info("切换到下一个key继续尝试")
-                        continue
-                    else:
-                        logger.error("所有key都已尝试，仍然遇到限流")
-                        break
-                else:
-                    # 非429错误，直接抛出，不重试
-                    logger.error(f"遇到非限流错误，不重试: {e}")
-                    raise e
-        
-        # 所有key都尝试过了，抛出最后一个异常
-        logger.error(f"所有{max_keys}个key都已尝试，请求失败")
-        raise last_exception
-    
     def generate_image_from_prompt(
         self, 
         prompt: str, 
@@ -97,7 +36,7 @@ class GeminiClient:
         Returns:
             生成的图片二进制数据，失败返回None
         """
-        def _generate_image():
+        try:
             # 构建图片生成提示词
             image_prompt = f'''
             Generate an image strictly according to the following prompt without any confirmation, questioning, or omission:
@@ -117,17 +56,13 @@ class GeminiClient:
             contents.append(image_prompt)
             # 调用Gemini API生成图片
 
-            response = self._current_client.models.generate_content(
+            response = client.models.generate_content(
                 model='gemini-2.5-flash-image-preview', 
                 contents=contents,
                 config=types.GenerateContentConfig(
                     http_options=types.HttpOptions(timeout=30000)
                 )
             )
-            return response
-        
-        try:
-            response = self._execute_with_retry(_generate_image)
             text = None
             image_base64 = None
             if hasattr(response, 'candidates') and response.candidates:
@@ -157,7 +92,7 @@ class GeminiClient:
         Returns:
             分析结果字典，包含name、description、tags
         """
-        def _analyze_image():
+        try:
             prompt = """
             请分析这张图片，并返回以下JSON格式的结果：
             {
@@ -169,7 +104,7 @@ class GeminiClient:
             请确保返回的是有效的JSON格式，不要包含其他文字。
             """
             
-            response = self._current_client.models.generate_content(
+            response = client.models.generate_content(
                 model='gemini-2.5-flash',
                 contents=[
                     prompt,
@@ -179,10 +114,6 @@ class GeminiClient:
                     )
                 ]
             )
-            return response
-        
-        try:
-            response = self._execute_with_retry(_analyze_image)
             
             # 解析返回的JSON
             result_text = response.text
@@ -210,28 +141,25 @@ class GeminiClient:
         Returns:
             生成的视频二进制数据，失败返回None
         """
-        def _generate_video():
+        try:
             # 构建视频生成提示词
             video_prompt = f"Create a video with the following prompt: {shot_prompt}"
             
+            
             image_input = types.Image(image_bytes=frame_image_bytes, mime_type="image/jpeg")
 
             # 调用Veo-3.0 API生成视频
-            operation = self._current_client.models.generate_videos(
-                model="veo-3.0-generate-preview", #veo-3.0-fast-generate-preview
+            operation = client.models.generate_videos(
+                model="veo-3.0-fast-generate-preview", #veo-3.0-generate-preview
                 prompt=video_prompt,
                 image=image_input
             )
-            return operation
-        
-        try:
-            operation = self._execute_with_retry(_generate_video)
             
             # 轮询操作状态直到视频生成完成
             logger.info("等待视频生成完成...")
             while not operation.done:
                 time.sleep(2)
-                operation = self._current_client.operations.get(operation)
+                operation = client.operations.get(operation)
             
             # 下载生成的视频
             
@@ -244,7 +172,7 @@ class GeminiClient:
                     raise Exception(operation.response.rai_media_filtered_reasons[0])
                 raise Exception("未知错误")
             
-            video_bytes = self._current_client.files.download(file=video.video)
+            video_bytes = client.files.download(file=video.video)
             
             logger.info("Veo-3.0视频生成成功")
             return video_bytes
@@ -253,13 +181,12 @@ 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, prompt_template: str) -> Optional[Dict[str, Any]]:
+    async def analyze_video(self, video_url: 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
@@ -280,54 +207,74 @@ class GeminiClient:
                 temp_video_path = temp_file.name
             
             try:
-                def _upload_and_analyze():
-                    # 上传视频文件到Gemini
-                    logger.info("正在上传视频到Gemini...")
-                    myfile = self._current_client.files.upload(file=temp_video_path)
-                    logger.info(f"视频上传成功，文件ID: {myfile.name}")
+                # 上传视频文件到Gemini
+                logger.info("正在上传视频到Gemini...")
+                myfile = client.files.upload(file=temp_video_path)
+                logger.info(f"视频上传成功，文件ID: {myfile.name}")
                 
-                    # 等待文件变为ACTIVE状态
-                    logger.info("等待文件处理完成...")
-                    max_wait_time = 300  # 最多等待5分钟
-                    wait_interval = 5    # 每5秒检查一次
-                    waited_time = 0
+                # 等待文件变为ACTIVE状态
+                logger.info("等待文件处理完成...")
+                max_wait_time = 300  # 最多等待5分钟
+                wait_interval = 5    # 每5秒检查一次
+                waited_time = 0
                 
-                    while waited_time < max_wait_time:
-                        file_info = self._current_client.files.get(name=myfile.name)
-                        logger.info(f"文件状态: {file_info.state}")
+                while waited_time < max_wait_time:
+                    file_info = client.files.get(name=myfile.name)
+                    logger.info(f"文件状态: {file_info.state}")
                     
-                        if file_info.state == "ACTIVE":
-                            logger.info("文件已准备就绪，开始分析")
-                            break
-                        elif file_info.state == "FAILED":
-                            raise Exception("文件处理失败")
+                    if file_info.state == "ACTIVE":
+                        logger.info("文件已准备就绪，开始分析")
+                        break
+                    elif file_info.state == "FAILED":
+                        raise Exception("文件处理失败")
                     
-                        time.sleep(wait_interval)
-                        waited_time += wait_interval
+                    time.sleep(wait_interval)
+                    waited_time += wait_interval
                 
-                    if waited_time >= max_wait_time:
-                        raise Exception("文件处理超时")
+                if waited_time >= max_wait_time:
+                    raise Exception("文件处理超时")
                 
-                    return myfile
+                # 构建分析提示词
+                analysis_prompt = """
+                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"
+                        }
+                    ]
+                }
                 
-                # 直接使用提示词模板作为分析提示词
-                analysis_prompt = prompt_template
+                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)
-                
-                def _analyze_video_content():
-                    # 调用Gemini分析视频
-                    logger.info("正在分析视频内容...")
-                    response = self._current_client.models.generate_content(
-                        model="gemini-2.5-flash",
-                        contents=[myfile, analysis_prompt],
-                        config=types.GenerateContentConfig(
-                            http_options=types.HttpOptions(timeout=120000)  # 2分钟超时
-                        )
+                # 调用Gemini分析视频
+                logger.info("正在分析视频内容...")
+                response = client.models.generate_content(
+                    model="gemini-2.5-flash",
+                    contents=[myfile, analysis_prompt],
+                    config=types.GenerateContentConfig(
+                        http_options=types.HttpOptions(timeout=120000)  # 2分钟超时
                     )
-                    return response
-                
-                response = self._execute_with_retry(_analyze_video_content)
+                )
                 
                 # 解析返回的JSON
                 result_text = response.text

src/infrastructure/external/key_pool_manager.py

@@ -1,61 +0,0 @@
-from typing import List, Optional, Iterator
-from loguru import logger
-import threading
-from ..config import settings
-
-
-class KeyPoolManager:
-    """Google API Key池管理器，用于处理限流重试"""
-    
-    def __init__(self):
-        self._keys: List[str] = []
-        self._current_index = 0
-        self._lock = threading.Lock()
-        self._initialize_keys()
-    
-    def _initialize_keys(self):
-        """初始化key池"""
-        # 解析key字符串，支持单个或多个key
-        self._keys = [key.strip() for key in settings.google_api_keys.split(',') if key.strip()]
-        if not self._keys:
-            raise ValueError("GOOGLE_API_KEYS不能为空")
-        logger.info(f"初始化Google API Key池，共{len(self._keys)}个key")
-    
-    def get_current_key(self) -> str:
-        """获取当前key"""
-        with self._lock:
-            if not self._keys:
-                raise ValueError("没有可用的Google API Key")
-            return self._keys[self._current_index]
-    
-    def switch_to_next_key(self) -> bool:
-        """切换到下一个key
-        
-        Returns:
-            bool: 如果还有下一个key返回True，否则返回False
-        """
-        with self._lock:
-            if len(self._keys) <= 1:
-                return False
-            
-            self._current_index = (self._current_index + 1) % len(self._keys)
-            logger.info(f"切换到下一个Google API Key，当前索引: {self._current_index}")
-            return True
-    
-    def reset_to_first_key(self):
-        """重置到第一个key"""
-        with self._lock:
-            self._current_index = 0
-            logger.info("重置到第一个Google API Key")
-    
-    def get_all_keys(self) -> List[str]:
-        """获取所有key（用于测试）"""
-        return self._keys.copy()
-    
-    def has_multiple_keys(self) -> bool:
-        """是否有多个key"""
-        return len(self._keys) > 1
-
-
-# 全局key池管理器实例
-key_pool_manager = KeyPoolManager()

src/infrastructure/external/openrouter_client.py

@@ -21,7 +21,7 @@ class OpenRouterClient:
     async def generate_text(
         self, 
         prompt: str, 
-        model: str = "google/gemini-2.5-pro"
+        model: str = "anthropic/claude-3.5-sonnet"
     ) -> Optional[str]:
         """
         生成文本内容
@@ -66,7 +66,7 @@ class OpenRouterClient:
         self, 
         prompt_template: str, 
         script_or_idea: str,
-        model: str = "google/gemini-2.5-pro"
+        model: str = "anthropic/claude-3.5-sonnet"
     ) -> 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 = "google/gemini-2.5-pro"
+        model: str = "anthropic/claude-3.5-sonnet"
     ) -> Optional[str]:
         """
         翻译文本