79 lines
2.2 KiB
Python
79 lines
2.2 KiB
Python
"""趋势统计服务 — 按日论文数量、热门标签、Upvotes 分布、总结完成率。"""
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from __future__ import annotations
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from datetime import date, timedelta
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from sqlalchemy import text
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from sqlalchemy.orm import Session
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def get_trends_data(db: Session) -> dict:
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"""从 DB 聚合趋势数据。"""
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thirty_days_ago = (date.today() - timedelta(days=30)).isoformat()
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# 1. 按日论文数量（近 30 天）
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daily_rows = db.execute(
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text("""
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SELECT paper_date, COUNT(*) as cnt
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FROM papers
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WHERE paper_date >= :start_date
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GROUP BY paper_date
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ORDER BY paper_date ASC
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"""),
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{"start_date": thirty_days_ago},
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).fetchall()
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daily_counts = [{"date": str(row[0]), "count": row[1]} for row in daily_rows]
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# 2. 热门标签 Top 20
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tag_rows = db.execute(
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text("""
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SELECT tag, COUNT(*) as cnt
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FROM paper_tags
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GROUP BY tag
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ORDER BY cnt DESC
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LIMIT 20
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""")
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).fetchall()
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top_tags = [{"tag": row[0], "count": row[1]} for row in tag_rows]
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# 3. Upvotes 分布
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upvote_rows = db.execute(
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text("""
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SELECT
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CASE
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WHEN upvotes >= 100 THEN '100+'
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WHEN upvotes >= 50 THEN '50-99'
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WHEN upvotes >= 20 THEN '20-49'
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WHEN upvotes >= 10 THEN '10-19'
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WHEN upvotes >= 5 THEN '5-9'
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ELSE '0-4'
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END as bucket,
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COUNT(*) as cnt
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FROM papers
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GROUP BY bucket
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ORDER BY MIN(upvotes) DESC
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""")
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).fetchall()
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upvotes_dist = [{"range": row[0], "count": row[1]} for row in upvote_rows]
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# 4. 总结完成率
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summary_rows = db.execute(
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text("""
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SELECT
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COALESCE(ss.status, 'none') as status,
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COUNT(*) as cnt
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FROM papers p
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LEFT JOIN summary_status ss ON ss.paper_id = p.id
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GROUP BY status
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""")
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).fetchall()
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summary_completion = [{"status": row[0], "count": row[1]} for row in summary_rows]
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return {
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"daily_counts": daily_counts,
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"top_tags": top_tags,
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"upvotes_dist": upvotes_dist,
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"summary_completion": summary_completion,
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}
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