34 KiB
34 KiB
""" Gemini AI服务
集成Google Gemini API，提供视频内容分析和分类功能。 """
import asyncio import base64 import io import time import json import os import hashlib from typing import Dict, List, Any, Optional, Union, Callable from pathlib import Path import logging from dataclasses import dataclass, asdict
import requests
try: import google.generativeai as genai from google.generativeai.types import HarmCategory, HarmBlockThreshold GEMINI_AVAILABLE = True except ImportError: GEMINI_AVAILABLE = False genai = None
from PIL import Image import cv2 import numpy as np
from src.core.di import Injectable, Inject, Service
@dataclass class GeminiConfig: """Gemini配置类 - 参考demo.py实现""" # 认证配置 cloudflare_project_id: str = "" cloudflare_gateway_id: str = "" google_project_id: str = "" regions: List[str] = None access_token: str = ""
# API配置
model_name: str = "gemini-2.5-flash"
base_url: str = "https://bowongai-dev--bowong-ai-video-gemini-fastapi-webapp.modal.run"
bearer_token: str = "bowong7777"
timeout: int = 120
# 缓存配置
enable_cache: bool = True
cache_dir: str = ".cache/gemini_analysis"
cache_expiry: int = 7 * 24 * 3600 # 7天
# 上传缓存配置
enable_upload_cache: bool = True
upload_cache_dir: str = ".cache/gemini_uploads"
upload_cache_expiry: int = 24 * 3600 # 1天
# 重试配置
max_retries: int = 3
retry_delay: int = 5
def __post_init__(self):
if self.regions is None:
self.regions = ["us-central1", "us-east1", "europe-west1"]
@dataclass class AnalysisProgress: """分析进度""" step: str progress: int # 0-100 description: str = "" current_file: str = "" stage: str = "upload" # upload, analysis, complete
@dataclass class CacheEntry: """缓存条目""" video_path: str file_uri: str prompt: str result: Dict[str, Any] timestamp: float checksum: str model_name: str
@dataclass class UploadCacheEntry: """上传缓存条目""" video_path: str file_uri: str timestamp: float checksum: str file_size: int
@Service("gemini_service") class GeminiService: """ Gemini AI服务
提供基于Google Gemini API的视频内容分析和分类功能。
支持两种模式：
1. 传统模式：使用google-generativeai库
2. 新模式：使用Cloudflare Gateway + Vertex AI (参考demo.py)
"""
def __init__(self,
config: Dict[str, Any] = Inject("config"),
logger: logging.Logger = Inject("logger")):
self.config = config
self.logger = logger
# 传统Gemini配置
self.gemini_config = config.get("gemini", {})
self.api_key = self.gemini_config.get("api_key", "")
self.model_name = self.gemini_config.get("model", "gemini-2.5-flash")
self.max_tokens = self.gemini_config.get("max_tokens", 1000)
self.temperature = self.gemini_config.get("temperature", 0.1)
# 新模式配置 - 参考demo.py
self.new_mode_config = GeminiConfig(
cloudflare_project_id=self.gemini_config.get("cloudflare_project_id", ""),
cloudflare_gateway_id=self.gemini_config.get("cloudflare_gateway_id", ""),
google_project_id=self.gemini_config.get("google_project_id", ""),
regions=self.gemini_config.get("regions", ["us-central1", "us-east1", "europe-west1"]),
model_name=self.model_name,
base_url=self.gemini_config.get("base_url", "https://bowongai-dev--bowong-ai-video-gemini-fastapi-webapp.modal.run"),
bearer_token=self.gemini_config.get("bearer_token", "bowong7777"),
timeout=self.gemini_config.get("timeout", 120),
enable_cache=self.gemini_config.get("enable_cache", True),
cache_dir=self.gemini_config.get("cache_dir", ".cache/gemini_analysis"),
enable_upload_cache=self.gemini_config.get("enable_upload_cache", True),
upload_cache_dir=self.gemini_config.get("upload_cache_dir", ".cache/gemini_uploads"),
upload_cache_expiry=self.gemini_config.get("upload_cache_expiry", 86400),
max_retries=self.gemini_config.get("max_retries", 3),
retry_delay=self.gemini_config.get("retry_delay", 5)
)
# 检查是否启用新模式
self.use_new_mode = self.gemini_config.get("use_new_mode", False)
# 缓存相关
self._access_token = None
self._token_expires_at = None
# 确保缓存目录存在
if self.new_mode_config.enable_cache:
os.makedirs(self.new_mode_config.cache_dir, exist_ok=True)
# 确保上传缓存目录存在
if self.new_mode_config.enable_upload_cache:
os.makedirs(self.new_mode_config.upload_cache_dir, exist_ok=True)
# 速率限制配置（仅传统模式使用）
self.rate_limit_config = self.gemini_config.get("rate_limit", {})
self.requests_per_minute = self.rate_limit_config.get("requests_per_minute", 60)
self.requests_per_day = self.rate_limit_config.get("requests_per_day", 1500)
# 请求历史记录（用于速率限制）
self.request_history = []
# 初始化客户端（传统模式）
self.model = None
if not self.use_new_mode:
self._initialize_client()
def _initialize_client(self) -> None:
"""初始化Gemini客户端"""
if not GEMINI_AVAILABLE:
self.logger.error("Google Generative AI库未安装，请运行: pip install google-generativeai")
return
if not self.api_key:
self.logger.warning("未配置Gemini API密钥，AI分类功能将不可用")
return
try:
# 配置API密钥
# 创建模型实例
generation_config = {
"temperature": self.temperature,
"top_p": 0.95,
"top_k": 64,
"max_output_tokens": self.max_tokens,
}
safety_settings = {
HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
}
self.logger.info(f"Gemini客户端初始化成功，模型: {self.model_name}")
except Exception as e:
self.logger.error(f"Gemini客户端初始化失败: {e}")
self.model = None
def is_available(self) -> bool:
"""检查Gemini服务是否可用"""
if self.use_new_mode:
# 新模式：检查配置是否完整
return (self.new_mode_config.cloudflare_project_id and
self.new_mode_config.cloudflare_gateway_id and
self.new_mode_config.google_project_id and
self.new_mode_config.bearer_token)
else:
# 传统模式
return GEMINI_AVAILABLE and self.model is not None
async def get_access_token(self) -> str:
"""
获取Google访问令牌，参考demo.py实现
"""
# 检查缓存的令牌是否仍然有效
if (self._access_token and self._token_expires_at and
time.time() < self._token_expires_at - 300): # 提前5分钟刷新
return self._access_token
try:
headers = {
"Authorization": f"Bearer {self.new_mode_config.bearer_token}",
"Content-Type": "application/json"
}
response = requests.get(
f"{self.new_mode_config.base_url}/google/access-token",
headers=headers,
timeout=self.new_mode_config.timeout
)
if response.status_code != 200:
raise Exception(f"获取访问令牌失败: {response.status_code} - {response.text}")
token_data = response.json()
self._access_token = token_data["access_token"]
self._token_expires_at = time.time() + token_data.get("expires_in", 3600)
self.logger.info("✅ 成功获取Google访问令牌")
return self._access_token
except Exception as e:
self.logger.error(f"❌ 获取访问令牌失败: {e}")
raise Exception(f"获取访问令牌失败: {str(e)}")
def _create_gemini_client(self, access_token: str) -> Dict[str, Any]:
"""
创建Gemini客户端配置，参考demo.py实现
"""
import random
# 随机选择区域
region = random.choice(self.new_mode_config.regions)
gateway_url = (
f"https://gateway.ai.cloudflare.com/v1/"
f"{self.new_mode_config.cloudflare_project_id}/"
f"{self.new_mode_config.cloudflare_gateway_id}/"
f"google-vertex-ai/v1/projects/"
f"{self.new_mode_config.google_project_id}/"
f"locations/{region}/publishers/google/models"
)
return {
"gateway_url": gateway_url,
"access_token": access_token,
"headers": {
"Content-Type": "application/json",
"Authorization": f"Bearer {access_token}"
}
}
async def _check_rate_limit(self) -> bool:
"""检查速率限制"""
current_time = time.time()
# 清理过期的请求记录
self.request_history = [
req_time for req_time in self.request_history
if current_time - req_time < 86400 # 24小时
]
# 检查每日限制
if len(self.request_history) >= self.requests_per_day:
self.logger.warning("已达到Gemini API每日请求限制")
return False
# 检查每分钟限制
recent_requests = [
req_time for req_time in self.request_history
if current_time - req_time < 60 # 1分钟
]
if len(recent_requests) >= self.requests_per_minute:
self.logger.warning("已达到Gemini API每分钟请求限制")
return False
return True
def _calculate_file_checksum(self, file_path: str) -> str:
"""计算文件校验和"""
hash_md5 = hashlib.md5()
with open(file_path, "rb") as f:
for chunk in iter(lambda: f.read(4096), b""):
hash_md5.update(chunk)
return hash_md5.hexdigest()
def _generate_cache_key(self, video_path: str, prompt: str, model_name: str) -> str:
"""生成缓存键"""
key_data = f"{video_path}:{prompt}:{model_name}"
return hashlib.md5(key_data.encode()).hexdigest()
def _check_analysis_cache(self, video_path: str, prompt: str) -> Optional[Dict[str, Any]]:
"""检查分析缓存"""
if not self.new_mode_config.enable_cache:
return None
try:
cache_key = self._generate_cache_key(video_path, prompt, self.new_mode_config.model_name)
cache_file = os.path.join(self.new_mode_config.cache_dir, f"{cache_key}.json")
if not os.path.exists(cache_file):
return None
with open(cache_file, 'r', encoding='utf-8') as f:
cache_entry_data = json.load(f)
cache_entry = CacheEntry(**cache_entry_data)
# 检查缓存是否过期
if time.time() - cache_entry.timestamp > self.new_mode_config.cache_expiry:
os.unlink(cache_file)
self.logger.info(f"⏰ 缓存已过期: {Path(video_path).name}")
return None
# 检查文件是否发生变化
current_checksum = self._calculate_file_checksum(video_path)
if current_checksum != cache_entry.checksum:
os.unlink(cache_file)
self.logger.info(f"🔄 文件已变更: {Path(video_path).name}")
return None
self.logger.info(f"🎯 使用缓存的分析结果: {Path(video_path).name}")
return cache_entry.result
except Exception as e:
self.logger.warning(f"检查分析缓存失败: {e}")
return None
def _save_analysis_cache(self, video_path: str, file_uri: str, prompt: str, result: Dict[str, Any]) -> None:
"""保存分析结果到缓存"""
if not self.new_mode_config.enable_cache:
return
try:
cache_key = self._generate_cache_key(video_path, prompt, self.new_mode_config.model_name)
cache_file = os.path.join(self.new_mode_config.cache_dir, f"{cache_key}.json")
checksum = self._calculate_file_checksum(video_path)
cache_entry = CacheEntry(
video_path=video_path,
file_uri=file_uri,
prompt=prompt,
result=result,
timestamp=time.time(),
checksum=checksum,
model_name=self.new_mode_config.model_name
)
with open(cache_file, 'w', encoding='utf-8') as f:
json.dump(asdict(cache_entry), f, ensure_ascii=False, indent=2)
self.logger.info(f"💾 分析结果已缓存: {Path(video_path).name}")
except Exception as e:
self.logger.warning(f"保存分析缓存失败: {e}")
def _generate_upload_cache_key(self, video_path: str) -> str:
"""生成上传缓存键"""
# 使用文件路径和修改时间生成唯一键
file_stat = os.stat(video_path)
key_data = f"{video_path}:{file_stat.st_mtime}:{file_stat.st_size}"
return hashlib.md5(key_data.encode()).hexdigest()
def _check_upload_cache(self, video_path: str) -> Optional[str]:
"""检查上传缓存"""
if not self.new_mode_config.enable_upload_cache:
return None
try:
cache_key = self._generate_upload_cache_key(video_path)
cache_file = os.path.join(self.new_mode_config.upload_cache_dir, f"{cache_key}.json")
if not os.path.exists(cache_file):
return None
with open(cache_file, 'r', encoding='utf-8') as f:
cache_entry_data = json.load(f)
cache_entry = UploadCacheEntry(**cache_entry_data)
# 检查缓存是否过期
if time.time() - cache_entry.timestamp > self.new_mode_config.upload_cache_expiry:
os.unlink(cache_file)
self.logger.info(f"⏰ 上传缓存已过期: {Path(video_path).name}")
return None
# 检查文件是否发生变化
current_checksum = self._calculate_file_checksum(video_path)
current_size = os.path.getsize(video_path)
if (current_checksum != cache_entry.checksum or
current_size != cache_entry.file_size):
os.unlink(cache_file)
self.logger.info(f"🔄 文件已变更，清除上传缓存: {Path(video_path).name}")
return None
self.logger.info(f"🎯 使用缓存的上传URI: {Path(video_path).name} -> {cache_entry.file_uri}")
return cache_entry.file_uri
except Exception as e:
self.logger.warning(f"检查上传缓存失败: {e}")
return None
def _save_upload_cache(self, video_path: str, file_uri: str) -> None:
"""保存上传缓存"""
if not self.new_mode_config.enable_upload_cache:
return
try:
cache_key = self._generate_upload_cache_key(video_path)
cache_file = os.path.join(self.new_mode_config.upload_cache_dir, f"{cache_key}.json")
checksum = self._calculate_file_checksum(video_path)
file_size = os.path.getsize(video_path)
cache_entry = UploadCacheEntry(
video_path=video_path,
file_uri=file_uri,
timestamp=time.time(),
checksum=checksum,
file_size=file_size
)
with open(cache_file, 'w', encoding='utf-8') as f:
json.dump(asdict(cache_entry), f, ensure_ascii=False, indent=2)
self.logger.info(f"💾 上传URI已缓存: {Path(video_path).name} -> {file_uri}")
except Exception as e:
self.logger.warning(f"保存上传缓存失败: {e}")
async def _upload_video_file_new_mode(self, video_path: str) -> str:
"""
上传视频文件到Gemini，参考demo.py实现
支持上传缓存，避免重复上传相同文件
"""
try:
# 检查上传缓存
cached_uri = self._check_upload_cache(video_path)
if cached_uri:
return cached_uri
# 检查文件大小
file_size = os.path.getsize(video_path)
max_size = 100 * 1024 * 1024 # 100MB限制
if file_size > max_size:
raise Exception(f"视频文件过大 ({file_size / 1024 / 1024:.1f}MB)，请使用小于100MB的文件")
# 获取访问令牌
access_token = await self.get_access_token()
# 准备FormData
with open(video_path, 'rb') as f:
video_data = f.read()
# 使用新的上传API
files = {
'file': (Path(video_path).name, video_data, 'video/mp4')
}
headers = {
"Authorization": f"Bearer {access_token}",
"x-google-api-key": access_token,
}
# 上传到Vertex AI
upload_url = f"{self.new_mode_config.base_url}/google/vertex-ai/upload"
params = {
"bucket": "dy-media-storage",
"prefix": "video-analysis"
}
response = requests.post(
upload_url,
files=files,
headers=headers,
params=params,
timeout=self.new_mode_config.timeout
)
if response.status_code != 200:
raise Exception(f"文件上传失败: {response.status_code} - {response.text}")
upload_result = response.json()
file_uri = upload_result.get('urn') or upload_result.get('uri')
if not file_uri:
raise Exception("上传成功但未获取到文件URI")
self.logger.info(f"✅ 视频上传成功: {Path(video_path).name} -> {file_uri}")
# 保存到上传缓存
self._save_upload_cache(video_path, file_uri)
return file_uri
except Exception as e:
self.logger.error(f"❌ 视频上传失败: {e}")
raise Exception(f"视频上传失败: {str(e)}")
async def _generate_content_new_mode(self, file_uri: str, prompt: str) -> Dict[str, Any]:
"""
生成内容分析，参考demo.py实现
"""
try:
# 获取访问令牌
access_token = await self.get_access_token()
# 创建客户端配置
client_config = self._create_gemini_client(access_token)
# 格式化GCS URI
formatted_uri = self._format_gcs_uri(file_uri)
# 准备请求数据，参考demo.py实现
request_data = {
"contents": [
{
"role": "user",
"parts": [
{
"text": prompt
},
{
"fileData": {
"mimeType": "video/mp4",
"fileUri": formatted_uri
}
}
]
}
],
"generationConfig": {
"temperature": self.temperature,
"topK": 32,
"topP": 1,
"maxOutputTokens": self.max_tokens
}
}
# 发送请求到Cloudflare Gateway
generate_url = f"{client_config['gateway_url']}/{self.new_mode_config.model_name}:generateContent"
self.logger.info(f"📤 发送 Gemini API 请求: {formatted_uri}")
# 重试机制
last_exception = None
for attempt in range(self.new_mode_config.max_retries):
try:
response = requests.post(
generate_url,
headers=client_config["headers"],
json=request_data,
timeout=self.new_mode_config.timeout
)
if response.status_code == 200:
result = response.json()
if 'candidates' not in result or not result['candidates']:
raise Exception("API返回结果为空")
self.logger.info("✅ 成功获取Gemini分析结果")
return result
else:
error_msg = f"API请求失败: {response.status_code} - {response.text}"
self.logger.warning(f"⚠️ 尝试 {attempt + 1}/{self.new_mode_config.max_retries}: {error_msg}")
if attempt == self.new_mode_config.max_retries - 1:
raise Exception(error_msg)
await asyncio.sleep(self.new_mode_config.retry_delay)
except requests.exceptions.Timeout as e:
last_exception = e
self.logger.warning(f"⚠️ 请求超时，尝试 {attempt + 1}/{self.new_mode_config.max_retries}")
if attempt < self.new_mode_config.max_retries - 1:
await asyncio.sleep(self.new_mode_config.retry_delay)
except Exception as e:
last_exception = e
self.logger.warning(f"⚠️ 请求失败，尝试 {attempt + 1}/{self.new_mode_config.max_retries}: {e}")
if attempt < self.new_mode_config.max_retries - 1:
await asyncio.sleep(self.new_mode_config.retry_delay)
raise Exception(f"内容生成失败，已重试{self.new_mode_config.max_retries}次: {last_exception}")
except Exception as e:
self.logger.error(f"❌ 内容生成失败: {e}")
raise Exception(f"内容生成失败: {str(e)}")
def _format_gcs_uri(self, file_uri: str) -> str:
"""格式化GCS URI"""
if file_uri.startswith('gs://'):
return file_uri
elif file_uri.startswith('https://storage.googleapis.com/'):
# 转换为gs://格式
path = file_uri.replace('https://storage.googleapis.com/', '')
return f"gs://{path}"
else:
# 假设已经是正确格式
return file_uri
def _parse_analysis_result_new_mode(self, api_result: Dict[str, Any], video_path: str) -> Dict[str, Any]:
"""
解析分析结果，参考demo.py实现
"""
try:
# 提取文本内容
candidates = api_result.get('candidates', [])
if not candidates:
raise Exception("无有效的分析结果")
content = candidates[0].get('content', {})
parts = content.get('parts', [])
if not parts:
raise Exception("分析结果为空")
analysis_text = parts[0].get('text', '')
if not analysis_text:
raise Exception("未获取到分析文本")
self.logger.info(f"✅ 成功获取响应文本，长度: {len(analysis_text)}")
# 尝试解析JSON格式的结果
analysis_data = None
try:
# 清理文本，移除可能的markdown标记
cleaned_text = analysis_text.strip()
if cleaned_text.startswith('```json'):
cleaned_text = cleaned_text[7:]
if cleaned_text.endswith('```'):
cleaned_text = cleaned_text[:-3]
cleaned_text = cleaned_text.strip()
# 直接尝试解析JSON
if cleaned_text.startswith('{') or cleaned_text.startswith('['):
analysis_data = json.loads(cleaned_text)
self.logger.info("✅ 成功解析JSON格式的分析结果")
else:
# 使用正则表达式提取JSON部分
import re
json_match = re.search(r'\{.*\}', cleaned_text, re.DOTALL)
if json_match:
json_str = json_match.group()
analysis_data = json.loads(json_str)
self.logger.info("✅ 成功解析JSON格式的分析结果")
else:
raise json.JSONDecodeError("No JSON found", "", 0)
except json.JSONDecodeError:
# JSON解析失败，使用文本格式
self.logger.info("📝 使用文本格式的分析结果")
analysis_data = {
"content_analysis": {
"summary": analysis_text[:500] + "..." if len(analysis_text) > 500 else analysis_text,
"full_text": analysis_text
}
}
# 提取新增字段
"""
{{
"category": "分类结果",
"confidence": 0.85,
"reasoning": "详细的分类理由，包括商品匹配情况和内容特征",
"features": ["观察到的关键特征1", "关键特征2", "关键特征3"],
"product_match": true/false,
"quality_score": 0.9
}}
"""
product_match = analysis_data.get("product_match", True) # 默认为True保持兼容性
quality_score = analysis_data.get("quality_score", 1.0) # 默认为1.0保持兼容性
# 如果商品不匹配或质量太低，强制分类为废弃素材
category = analysis_data.get("category", "unclassified")
confidence = analysis_data.get("confidence", 0.8)
if not product_match or quality_score < 0.5:
category = "废弃素材"
confidence = max(confidence, 0.8) # 提高废弃素材的置信度
self.logger.info(f"商品不匹配或质量不合格，分类为废弃素材: product_match={product_match}, quality_score={quality_score}")
# 构建标准化结果格式
result = {
"success": True,
"category": category,
"confidence": confidence,
"reasoning": analysis_data.get("reasoning", "AI分析结果"),
"features": analysis_data.get("features", []),
"product_match": product_match,
"quality_score": quality_score,
"video_info": {
"file_name": Path(video_path).name,
"file_path": str(video_path),
"file_size": os.path.getsize(video_path),
"analysis_time": time.strftime("%Y-%m-%d %H:%M:%S"),
"model_used": self.new_mode_config.model_name
},
"analysis_result": analysis_data,
"metadata": {
"response_length": len(analysis_text),
"candidates_count": len(candidates),
"mode": "new_mode"
},
"raw_response": analysis_text
}
return result
except Exception as e:
self.logger.error(f"❌ 结果解析失败: {e}")
return {
"success": False,
"error": str(e),
"category": "unclassified",
"confidence": 0.0
}
async def _make_request(self, prompt: str, images: List[Image.Image] = None) -> Optional[str]:
"""发送请求到Gemini API"""
if not self.is_available():
raise RuntimeError("Gemini服务不可用")
# 检查速率限制
if not await self._check_rate_limit():
raise RuntimeError("已达到API速率限制")
try:
# 准备输入内容
content = [prompt]
if images:
for image in images:
content.append(image)
# 发送请求
response = await asyncio.to_thread(
self.model.generate_content,
content
)
# 记录请求时间
self.request_history.append(time.time())
# 检查响应
if response.candidates and len(response.candidates) > 0:
candidate = response.candidates[0]
if hasattr(candidate, 'content') and candidate.content.parts:
return candidate.content.parts[0].text
self.logger.warning("Gemini API返回空响应")
return None
except Exception as e:
self.logger.error(f"Gemini API请求失败: {e}")
raise
async def analyze_video(self, video_path: str, prompt: str = None, product_title: str = "", **kwargs) -> Dict[str, Any]:
return await self.analyze_video_content(video_path, product_title=product_title, **kwargs)
async def analyze_video_content(self, video_path: str, product_title: str = "", **kwargs) -> Dict[str, Any]:
"""分析视频内容"""
try:
return await self._analyze_video_new_mode(video_path, product_title=product_title, **kwargs)
except Exception as e:
self.logger.error(f"视频分析失败: {e}")
return {
"success": False,
"error": str(e),
"category": "unclassified",
"confidence": 0.0
}
async def _analyze_video_new_mode(self, video_path: str, product_title: str = "", **kwargs) -> Dict[str, Any]:
try:
# 构建分析提示词
prompt = ""
# 检查缓存
cached_result = self._check_analysis_cache(video_path, prompt)
if cached_result:
return cached_result
# 上传视频文件
file_uri = await self._upload_video_file_new_mode(video_path)
# 发送分析请求
result = await self._generate_content_new_mode(file_uri, prompt)
# 解析结果
"""
{
"success": True,
"category": category,
"confidence": confidence,
"reasoning": analysis_data.get("reasoning", "AI分析结果"),
"features": analysis_data.get("features", []),
"product_match": product_match,
"quality_score": quality_score,
"video_info": {
"file_name": Path(video_path).name,
"file_path": str(video_path),
"file_size": os.path.getsize(video_path),
"analysis_time": time.strftime("%Y-%m-%d %H:%M:%S"),
"model_used": self.new_mode_config.model_name
},
"analysis_result": analysis_data,
"metadata": {
"response_length": len(analysis_text),
"candidates_count": len(candidates),
"mode": "new_mode"
},
"raw_response": analysis_text
}
"""
parsed_result = self._parse_analysis_result_new_mode(result, video_path)
# 保存到缓存
self._save_analysis_cache(video_path, file_uri, prompt, parsed_result)
return parsed_result
except Exception as e:
self.logger.error(f"新模式视频分析失败: {e}")
raise
async def batch_analyze_videos(self, video_paths: List[str], **kwargs) -> Dict[str, Dict[str, Any]]:
"""批量分析视频"""
self.logger.info(f"开始批量分析 {len(video_paths)} 个视频")
results = {}
# 控制并发数以避免API限制
max_concurrent = kwargs.get("max_concurrent", 3)
semaphore = asyncio.Semaphore(max_concurrent)
async def analyze_single(video_path: str):
async with semaphore:
try:
result = await self.analyze_video_content(video_path, **kwargs)
return video_path, result
except Exception as e:
self.logger.error(f"分析视频失败 {video_path}: {e}")
return video_path, {
"success": False,
"error": str(e),
"category": "unclassified",
"confidence": 0.0
}
# 创建并发任务
tasks = [analyze_single(video_path) for video_path in video_paths]
# 等待所有任务完成
completed_tasks = await asyncio.gather(*tasks, return_exceptions=True)
# 处理结果
for task_result in completed_tasks:
if isinstance(task_result, Exception):
self.logger.error(f"批量分析任务失败: {task_result}")
else:
video_path, result = task_result
results[video_path] = result
success_count = len([r for r in results.values() if r.get("success", False)])
self.logger.info(f"批量分析完成: 成功 {success_count}/{len(video_paths)}")
return results