103 lines
3.2 KiB
Python
103 lines
3.2 KiB
Python
"""
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GLM AI 服务
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使用智谱 GLM-4.7-Flash 生成标题和标签
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"""
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import json
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import re
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import httpx
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from loguru import logger
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class GLMService:
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"""GLM AI 服务"""
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API_URL = "https://open.bigmodel.cn/api/paas/v4/chat/completions"
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API_KEY = "5915240ea48d4e93b454bc2412d1cc54.e054ej4pPqi9G6rc"
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async def generate_title_tags(self, text: str) -> dict:
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"""
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根据口播文案生成标题和标签
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Args:
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text: 口播文案
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Returns:
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{"title": "标题", "tags": ["标签1", "标签2", ...]}
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"""
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prompt = f"""根据以下口播文案，生成一个吸引人的短视频标题和3个相关标签。
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口播文案：
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{text}
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要求：
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1. 标题要简洁有力，能吸引观众点击，不超过10个字
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2. 标签要与内容相关，便于搜索和推荐，只要3个
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请严格按以下JSON格式返回（不要包含其他内容）：
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{{"title": "标题", "tags": ["标签1", "标签2", "标签3"]}}"""
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try:
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async with httpx.AsyncClient(timeout=30.0) as client:
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response = await client.post(
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self.API_URL,
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.API_KEY}"
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},
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json={
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"model": "glm-4-flash",
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"messages": [{"role": "user", "content": prompt}],
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"max_tokens": 500,
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"temperature": 0.7
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}
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)
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response.raise_for_status()
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data = response.json()
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# 提取生成的内容
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content = data["choices"][0]["message"]["content"]
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logger.info(f"GLM response: {content}")
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# 解析 JSON
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result = self._parse_json_response(content)
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return result
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except httpx.HTTPError as e:
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logger.error(f"GLM API request failed: {e}")
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raise Exception(f"AI 服务请求失败: {str(e)}")
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except Exception as e:
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logger.error(f"GLM service error: {e}")
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raise Exception(f"AI 生成失败: {str(e)}")
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def _parse_json_response(self, content: str) -> dict:
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"""解析 GLM 返回的 JSON 内容"""
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# 尝试直接解析
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try:
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return json.loads(content)
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except json.JSONDecodeError:
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pass
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# 尝试提取 JSON 块
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json_match = re.search(r'\{[^{}]*"title"[^{}]*"tags"[^{}]*\}', content, re.DOTALL)
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if json_match:
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try:
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return json.loads(json_match.group())
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except json.JSONDecodeError:
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pass
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# 尝试提取 ```json 代码块
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code_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', content, re.DOTALL)
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if code_match:
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try:
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return json.loads(code_match.group(1))
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except json.JSONDecodeError:
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pass
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logger.error(f"Failed to parse GLM response: {content}")
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raise Exception("AI 返回格式解析失败")
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# 全局服务实例
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glm_service = GLMService()
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