feat: 提交热榜评论分析工具 MVP 基线

app/services/report_service.py

@@ -0,0 +1,162 @@
+import json
+from collections import Counter, defaultdict
+from collections.abc import Callable
+
+from sqlalchemy import func, select
+from sqlalchemy.orm import Session
+
+from app.models import Comment, ContentItem, Hotspot, Report
+
+
+DEFAULT_SUMMARY = "总结生成失败，请查看上方统计数据。"
+
+
+def _labels(value: str | None) -> list[str]:
+    try:
+        parsed = json.loads(value or "[]")
+    except json.JSONDecodeError:
+        return []
+    return [str(label) for label in parsed] if isinstance(parsed, list) else []
+
+
+def build_comment_metrics(comments: list[Comment]) -> dict:
+    sentiment_counts = Counter(comment.sentiment or "unknown" for comment in comments)
+    label_counts = Counter(label for comment in comments for label in _labels(comment.labels))
+    total = len(comments)
+
+    def sentiment_entry(name: str) -> dict:
+        count = sentiment_counts.get(name, 0)
+        return {"count": count, "pct": round((count / total * 100) if total else 0, 2)}
+
+    return {
+        "sample_count": total,
+        "sentiment": {
+            "positive": sentiment_entry("positive"),
+            "negative": sentiment_entry("negative"),
+            "neutral": sentiment_entry("neutral"),
+            "unknown": sentiment_entry("unknown"),
+        },
+        "top_labels": [{"name": name, "count": count} for name, count in label_counts.most_common(5)],
+    }
+
+
+def select_typical_comments(comments: list[Comment], *, per_sentiment: int = 2) -> dict[str, list[dict]]:
+    buckets: dict[str, list[Comment]] = defaultdict(list)
+    for comment in comments:
+        buckets[comment.sentiment or "unknown"].append(comment)
+
+    result = {}
+    for sentiment, values in buckets.items():
+        sorted_values = sorted(values, key=lambda c: (c.like_count or 0, c.comment_time or c.created_at), reverse=True)
+        result[sentiment] = [
+            {"content": comment.content, "like_count": comment.like_count or 0, "comment_id": comment.source_comment_id}
+            for comment in sorted_values[:per_sentiment]
+        ]
+    return result
+
+
+def generate_item_report(
+    session: Session,
+    content_item_id: str,
+    *,
+    summary_provider: Callable[[dict, dict], str] | None = None,
+) -> Report:
+    item = session.get(ContentItem, content_item_id)
+    if item is None:
+        raise ValueError("content item not found")
+    hotspot = session.get(Hotspot, item.hotspot_id)
+    comments = list(session.scalars(select(Comment).where(Comment.content_item_id == item.id)))
+    metrics = build_comment_metrics(comments)
+    typical = select_typical_comments(comments)
+    summary = _safe_summary(summary_provider, metrics, typical, limit=200)
+    markdown = _build_markdown(title=item.title or item.source_item_id, metrics=metrics, typical=typical, summary=summary, hotspot_title=hotspot.title if hotspot else "")
+    report = Report(
+        task_id=item.task_id,
+        hotspot_id=item.hotspot_id,
+        content_item_id=item.id,
+        report_type="item",
+        title=item.title or item.source_item_id,
+        data=json.dumps(metrics, ensure_ascii=False),
+        markdown=markdown,
+        metrics_json=json.dumps(metrics, ensure_ascii=False),
+        typical_comments_json=json.dumps(typical, ensure_ascii=False),
+        summary=summary,
+        markdown_content=markdown,
+    )
+    session.add(report)
+    session.commit()
+    session.refresh(report)
+    return report
+
+
+def generate_hotspot_report(
+    session: Session,
+    hotspot_id: str,
+    *,
+    summary_provider: Callable[[dict, dict], str] | None = None,
+) -> Report:
+    hotspot = session.get(Hotspot, hotspot_id)
+    if hotspot is None:
+        raise ValueError("hotspot not found")
+    comments = list(session.scalars(select(Comment).where(Comment.hotspot_id == hotspot.id)))
+    item_count = session.scalar(select(func.count(ContentItem.id)).where(ContentItem.hotspot_id == hotspot.id)) or 0
+    metrics = build_comment_metrics(comments)
+    metrics["item_count"] = item_count
+    typical = select_typical_comments(comments)
+    summary = _safe_summary(summary_provider, metrics, typical, limit=300)
+    markdown = _build_markdown(title=hotspot.title, metrics=metrics, typical=typical, summary=summary)
+    report = Report(
+        task_id=hotspot.task_id,
+        hotspot_id=hotspot.id,
+        content_item_id=None,
+        report_type="hotspot",
+        title=hotspot.title,
+        data=json.dumps(metrics, ensure_ascii=False),
+        markdown=markdown,
+        metrics_json=json.dumps(metrics, ensure_ascii=False),
+        typical_comments_json=json.dumps(typical, ensure_ascii=False),
+        summary=summary,
+        markdown_content=markdown,
+    )
+    session.add(report)
+    session.commit()
+    session.refresh(report)
+    return report
+
+
+def _safe_summary(summary_provider, metrics: dict, typical: dict, *, limit: int) -> str:
+    if summary_provider is None:
+        return DEFAULT_SUMMARY
+    try:
+        summary = summary_provider(metrics, typical)
+    except Exception:
+        return DEFAULT_SUMMARY
+    return str(summary)[:limit]
+
+
+def _build_markdown(*, title: str, metrics: dict, typical: dict, summary: str, hotspot_title: str = "") -> str:
+    lines = [
+        f"# {title}",
+        "",
+    ]
+    if hotspot_title:
+        lines.extend([f"- 所属热点：{hotspot_title}", ""])
+    lines.extend(
+        [
+            f"- 样本评论数量：{metrics['sample_count']}",
+            "",
+            "## 情绪分布",
+        ]
+    )
+    for name in ("positive", "neutral", "negative", "unknown"):
+        item = metrics["sentiment"][name]
+        lines.append(f"- {name}: {item['count']} ({item['pct']}%)")
+    lines.extend(["", "## Top 标签"])
+    for label in metrics["top_labels"]:
+        lines.append(f"- {label['name']}: {label['count']}")
+    lines.extend(["", "## 典型评论"])
+    for sentiment, comments in typical.items():
+        for comment in comments:
+            lines.append(f"- [{sentiment}] {comment['content']}")
+    lines.extend(["", "## 总结", summary])
+    return "\n".join(lines)