Initial commit: add Trading Studio voice-over pipeline for quant trading review videos.

Co-authored-by: Cursor <cursoragent@cursor.com>

llm_service.py

@@ -0,0 +1,162 @@
+"""
+远程 Ollama LLM 润色服务
+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。
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Tuple
+
+import requests
+
+from config import MODEL_NAME, OLLAMA_TIMEOUT, OLLAMA_URL, SYSTEM_PROMPT
+
+logger = logging.getLogger(__name__)
+
+
+def _build_payload(raw_text: str) -> dict:
+    """构造 Ollama /api/chat 非流式请求体。"""
+    return {
+        "model": MODEL_NAME,
+        "messages": [
+            {"role": "system", "content": SYSTEM_PROMPT},
+            {
+                "role": "user",
+                "content": (
+                    "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"
+                    f"{raw_text}"
+                ),
+            },
+        ],
+        "stream": False,
+        "options": {
+            "temperature": 0.7,
+            "num_predict": 4096,
+        },
+    }
+
+
+def _extract_content(response_json: dict) -> str:
+    """从 Ollama 响应 JSON 中提取 assistant 文本。"""
+    # /api/chat 标准格式
+    message = response_json.get("message")
+    if isinstance(message, dict):
+        content = message.get("content", "").strip()
+        if content:
+            return content
+
+    # 兼容 /api/generate 格式（部分旧版或代理）
+    if "response" in response_json:
+        content = str(response_json["response"]).strip()
+        if content:
+            return content
+
+    raise ValueError(f"无法从 Ollama 响应中解析文本内容: {response_json}")
+
+
+def polish_text(raw_text: str) -> Tuple[bool, str]:
+    """
+    调用远程 Ollama 对原始转写文本进行润色。
+
+    Args:
+        raw_text: Whisper 转写得到的原始口语文本
+
+    Returns:
+        (success, polished_text_or_error_message)
+    """
+    if not raw_text or not raw_text.strip():
+        return False, "润色输入为空，请先完成语音识别。"
+
+    payload = _build_payload(raw_text.strip())
+
+    try:
+        logger.info("正在请求 Ollama: %s, model=%s", OLLAMA_URL, MODEL_NAME)
+        response = requests.post(
+            OLLAMA_URL,
+            json=payload,
+            timeout=OLLAMA_TIMEOUT,
+        )
+        response.raise_for_status()
+
+        data = response.json()
+        polished = _extract_content(data)
+
+        if not polished:
+            return False, "Ollama 返回内容为空，请检查模型是否正常加载。"
+
+        logger.info("润色完成，输出字数: %d", len(polished))
+        return True, polished
+
+    except requests.exceptions.ConnectTimeout:
+        err = (
+            f"连接 Ollama 超时（>{OLLAMA_TIMEOUT}s）。"
+            f"请确认 {OLLAMA_URL} 可达且 Ollama 服务已启动。"
+        )
+        logger.error(err)
+        return False, err
+
+    except requests.exceptions.ReadTimeout:
+        err = (
+            f"Ollama 响应超时（>{OLLAMA_TIMEOUT}s）。"
+            "模型可能正在加载或生成长度过长，请稍后重试。"
+        )
+        logger.error(err)
+        return False, err
+
+    except requests.exceptions.ConnectionError as exc:
+        err = (
+            f"无法连接到 Ollama 节点 ({OLLAMA_URL})。"
+            "请检查局域网连通性、防火墙及 Ollama 是否监听 0.0.0.0:11434。\n"
+            f"详情: {exc}"
+        )
+        logger.error(err)
+        return False, err
+
+    except requests.exceptions.HTTPError as exc:
+        status = exc.response.status_code if exc.response is not None else "?"
+        body = exc.response.text[:500] if exc.response is not None else ""
+        err = (
+            f"Ollama HTTP 错误 ({status})。"
+            f"请确认模型 `{MODEL_NAME}` 已通过 ollama pull 下载。\n"
+            f"响应片段: {body}"
+        )
+        logger.error(err)
+        return False, err
+
+    except ValueError as exc:
+        logger.error("Ollama 响应解析失败: %s", exc)
+        return False, str(exc)
+
+    except requests.exceptions.RequestException as exc:
+        err = f"Ollama 请求异常: {exc}"
+        logger.exception(err)
+        return False, err
+
+    except Exception as exc:
+        err = f"润色过程发生未知错误: {exc}"
+        logger.exception(err)
+        return False, err
+
+
+def check_ollama_health() -> Tuple[bool, str]:
+    """
+    快速检测 Ollama 节点是否在线（不触发完整推理）。
+
+    Returns:
+        (online, message)
+    """
+    base_url = OLLAMA_URL.rsplit("/api/", 1)[0]
+    try:
+        resp = requests.get(f"{base_url}/api/tags", timeout=10)
+        resp.raise_for_status()
+        tags = resp.json().get("models", [])
+        model_names = [m.get("name", "") for m in tags]
+        if any(MODEL_NAME.split(":")[0] in name for name in model_names):
+            return True, f"Ollama 在线，已检测到模型: {MODEL_NAME}"
+        return True, (
+            f"Ollama 在线，但未找到模型 {MODEL_NAME}，"
+            f"请执行: ollama pull {MODEL_NAME}"
+        )
+    except Exception as exc:
+        return False, f"Ollama 不可达: {exc}"