Optimize tablet load: defer health check, lighten service worker, drop Google Fonts.
Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
dekun
+198
-162
@@ -1,162 +1,198 @@
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"""
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远程 Ollama LLM 润色服务
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通过局域网 HTTP 请求 Gemma4 模型，对交易复盘转写稿进行纪律审判式润色。
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"""
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from __future__ import annotations
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import logging
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from typing import Tuple
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import requests
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from config import MODEL_NAME, OLLAMA_TIMEOUT, OLLAMA_URL, SYSTEM_PROMPT
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logger = logging.getLogger(__name__)
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def _build_payload(raw_text: str) -> dict:
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"""构造 Ollama /api/chat 非流式请求体。"""
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return {
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"model": MODEL_NAME,
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"messages": [
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{"role": "system", "content": SYSTEM_PROMPT},
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{
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"role": "user",
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"content": (
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"以下是我的交易复盘录音转写原文，请严格按系统要求润色：\n\n"
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f"{raw_text}"
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),
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},
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],
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"stream": False,
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"options": {
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"temperature": 0.7,
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"num_predict": 4096,
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},
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}
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def _extract_content(response_json: dict) -> str:
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"""从 Ollama 响应 JSON 中提取 assistant 文本。"""
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# /api/chat 标准格式
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message = response_json.get("message")
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if isinstance(message, dict):
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content = message.get("content", "").strip()
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if content:
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return content
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# 兼容 /api/generate 格式（部分旧版或代理）
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if "response" in response_json:
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content = str(response_json["response"]).strip()
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if content:
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return content
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raise ValueError(f"无法从 Ollama 响应中解析文本内容: {response_json}")
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def polish_text(raw_text: str) -> Tuple[bool, str]:
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"""
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调用远程 Ollama 对原始转写文本进行润色。
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Args:
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raw_text: Whisper 转写得到的原始口语文本
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Returns:
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(success, polished_text_or_error_message)
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"""
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if not raw_text or not raw_text.strip():
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return False, "润色输入为空，请先完成语音识别。"
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payload = _build_payload(raw_text.strip())
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try:
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logger.info("正在请求 Ollama: %s, model=%s", OLLAMA_URL, MODEL_NAME)
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response = requests.post(
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OLLAMA_URL,
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json=payload,
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timeout=OLLAMA_TIMEOUT,
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)
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response.raise_for_status()
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data = response.json()
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polished = _extract_content(data)
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if not polished:
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return False, "Ollama 返回内容为空，请检查模型是否正常加载。"
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logger.info("润色完成，输出字数: %d", len(polished))
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return True, polished
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except requests.exceptions.ConnectTimeout:
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err = (
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f"连接 Ollama 超时（>{OLLAMA_TIMEOUT}s）。"
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f"请确认 {OLLAMA_URL} 可达且 Ollama 服务已启动。"
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)
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logger.error(err)
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return False, err
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except requests.exceptions.ReadTimeout:
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err = (
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f"Ollama 响应超时（>{OLLAMA_TIMEOUT}s）。"
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"模型可能正在加载或生成长度过长，请稍后重试。"
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)
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logger.error(err)
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return False, err
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except requests.exceptions.ConnectionError as exc:
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err = (
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f"无法连接到 Ollama 节点 ({OLLAMA_URL})。"
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"请检查局域网连通性、防火墙及 Ollama 是否监听 0.0.0.0:11434。\n"
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f"详情: {exc}"
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)
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logger.error(err)
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return False, err
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except requests.exceptions.HTTPError as exc:
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status = exc.response.status_code if exc.response is not None else "?"
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body = exc.response.text[:500] if exc.response is not None else ""
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err = (
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f"Ollama HTTP 错误 ({status})。"
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f"请确认模型 `{MODEL_NAME}` 已通过 ollama pull 下载。\n"
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f"响应片段: {body}"
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)
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logger.error(err)
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return False, err
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except ValueError as exc:
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logger.error("Ollama 响应解析失败: %s", exc)
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return False, str(exc)
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except requests.exceptions.RequestException as exc:
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err = f"Ollama 请求异常: {exc}"
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logger.exception(err)
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return False, err
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except Exception as exc:
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err = f"润色过程发生未知错误: {exc}"
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logger.exception(err)
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return False, err
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def check_ollama_health() -> Tuple[bool, str]:
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"""
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快速检测 Ollama 节点是否在线（不触发完整推理）。
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Returns:
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(online, message)
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"""
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base_url = OLLAMA_URL.rsplit("/api/", 1)[0]
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try:
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resp = requests.get(f"{base_url}/api/tags", timeout=10)
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resp.raise_for_status()
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tags = resp.json().get("models", [])
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model_names = [m.get("name", "") for m in tags]
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if any(MODEL_NAME.split(":")[0] in name for name in model_names):
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return True, f"Ollama 在线，已检测到模型: {MODEL_NAME}"
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return True, (
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f"Ollama 在线，但未找到模型 {MODEL_NAME}，"
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f"请执行: ollama pull {MODEL_NAME}"
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)
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except Exception as exc:
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return False, f"Ollama 不可达: {exc}"
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"""
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远程 Ollama LLM 润色服务
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通过局域网 HTTP 请求 Gemma4 模型，对交易复盘转写稿进行纪律审判式润色。
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"""
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from __future__ import annotations
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import logging
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import time
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from typing import Tuple
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import requests
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from config import (
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HEALTH_CHECK_CACHE_SECONDS,
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HEALTH_CHECK_CONNECT_TIMEOUT,
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HEALTH_CHECK_READ_TIMEOUT,
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MODEL_NAME,
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OLLAMA_TIMEOUT,
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OLLAMA_URL,
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SYSTEM_PROMPT,
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)
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logger = logging.getLogger(__name__)
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# 健康检查短时缓存，避免平板/手机反复打开页面时重复等待
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_health_cache: dict = {"ts": 0.0, "ok": False, "msg": ""}
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def _build_payload(raw_text: str) -> dict:
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"""构造 Ollama /api/chat 非流式请求体。"""
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return {
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"model": MODEL_NAME,
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"messages": [
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{"role": "system", "content": SYSTEM_PROMPT},
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{
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"role": "user",
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"content": (
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"以下是我的交易复盘录音转写原文，请严格按系统要求润色：\n\n"
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f"{raw_text}"
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),
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},
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],
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"stream": False,
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"options": {
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"temperature": 0.7,
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"num_predict": 4096,
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},
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}
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def _extract_content(response_json: dict) -> str:
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"""从 Ollama 响应 JSON 中提取 assistant 文本。"""
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# /api/chat 标准格式
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message = response_json.get("message")
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if isinstance(message, dict):
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content = message.get("content", "").strip()
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if content:
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return content
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# 兼容 /api/generate 格式（部分旧版或代理）
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if "response" in response_json:
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content = str(response_json["response"]).strip()
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if content:
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return content
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raise ValueError(f"无法从 Ollama 响应中解析文本内容: {response_json}")
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def polish_text(raw_text: str) -> Tuple[bool, str]:
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"""
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调用远程 Ollama 对原始转写文本进行润色。
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Args:
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raw_text: Whisper 转写得到的原始口语文本
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Returns:
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(success, polished_text_or_error_message)
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"""
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if not raw_text or not raw_text.strip():
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return False, "润色输入为空，请先完成语音识别。"
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payload = _build_payload(raw_text.strip())
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try:
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logger.info("正在请求 Ollama: %s, model=%s", OLLAMA_URL, MODEL_NAME)
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response = requests.post(
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OLLAMA_URL,
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json=payload,
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timeout=OLLAMA_TIMEOUT,
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)
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response.raise_for_status()
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data = response.json()
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polished = _extract_content(data)
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if not polished:
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return False, "Ollama 返回内容为空，请检查模型是否正常加载。"
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logger.info("润色完成，输出字数: %d", len(polished))
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return True, polished
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except requests.exceptions.ConnectTimeout:
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err = (
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f"连接 Ollama 超时（>{OLLAMA_TIMEOUT}s）。"
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f"请确认 {OLLAMA_URL} 可达且 Ollama 服务已启动。"
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)
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logger.error(err)
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return False, err
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except requests.exceptions.ReadTimeout:
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err = (
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f"Ollama 响应超时（>{OLLAMA_TIMEOUT}s）。"
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"模型可能正在加载或生成长度过长，请稍后重试。"
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)
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logger.error(err)
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return False, err
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except requests.exceptions.ConnectionError as exc:
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err = (
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f"无法连接到 Ollama 节点 ({OLLAMA_URL})。"
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"请检查局域网连通性、防火墙及 Ollama 是否监听 0.0.0.0:11434。\n"
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f"详情: {exc}"
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)
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logger.error(err)
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return False, err
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except requests.exceptions.HTTPError as exc:
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status = exc.response.status_code if exc.response is not None else "?"
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body = exc.response.text[:500] if exc.response is not None else ""
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err = (
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f"Ollama HTTP 错误 ({status})。"
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f"请确认模型 `{MODEL_NAME}` 已通过 ollama pull 下载。\n"
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f"响应片段: {body}"
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)
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logger.error(err)
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return False, err
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except ValueError as exc:
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logger.error("Ollama 响应解析失败: %s", exc)
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return False, str(exc)
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except requests.exceptions.RequestException as exc:
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err = f"Ollama 请求异常: {exc}"
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logger.exception(err)
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return False, err
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except Exception as exc:
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err = f"润色过程发生未知错误: {exc}"
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logger.exception(err)
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return False, err
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def check_ollama_health(force: bool = False) -> Tuple[bool, str]:
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"""
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快速检测 Ollama 节点是否在线（不触发完整推理）。
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默认 2+3 秒超时，结果缓存 30 秒，避免平板首屏长时间白屏。
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Returns:
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(online, message)
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"""
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global _health_cache
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now = time.time()
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if (
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not force
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and _health_cache["msg"]
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and (now - _health_cache["ts"]) < HEALTH_CHECK_CACHE_SECONDS
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):
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return _health_cache["ok"], _health_cache["msg"]
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base_url = OLLAMA_URL.rsplit("/api/", 1)[0]
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timeout = (HEALTH_CHECK_CONNECT_TIMEOUT, HEALTH_CHECK_READ_TIMEOUT)
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try:
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resp = requests.get(f"{base_url}/api/tags", timeout=timeout)
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resp.raise_for_status()
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tags = resp.json().get("models", [])
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model_names = [m.get("name", "") for m in tags]
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if any(MODEL_NAME.split(":")[0] in name for name in model_names):
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msg = f"Ollama 在线，已检测到模型: {MODEL_NAME}"
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ok = True
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else:
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ok = True
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msg = (
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f"Ollama 在线，但未找到模型 {MODEL_NAME}，"
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f"请执行: ollama pull {MODEL_NAME}"
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)
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except requests.exceptions.Timeout:
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ok, msg = False, (
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f"Ollama 检测超时（>{HEALTH_CHECK_READ_TIMEOUT}s）。"
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"页面已加载，可稍后点击「刷新状态」重试。"
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)
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except Exception as exc:
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ok, msg = False, f"Ollama 不可达: {exc}"
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_health_cache.update({"ts": now, "ok": ok, "msg": msg})
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return ok, msg
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