feat: track user interest in paid plans and enhance admin analytics
- Added tracking for user interest in the pro plan on the PricingPage. - Implemented auto-retry for CSRF token failures in API response interceptor. - Introduced new interfaces and API functions for enhanced admin analytics, including ratings, tool usage, user stats, plan interest, and system health.
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@@ -285,4 +285,382 @@ def list_admin_contacts(page: int = 1, per_page: int = 20) -> dict:
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def mark_admin_contact_read(message_id: int) -> bool:
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return mark_read(message_id)
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return mark_read(message_id)
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# ---------------------------------------------------------------------------
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# Enhanced admin analytics
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# ---------------------------------------------------------------------------
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def _ensure_plan_interest_table():
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"""Create plan_interest_clicks table if it does not exist."""
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with _connect() as conn:
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conn.execute(
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"""
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CREATE TABLE IF NOT EXISTS plan_interest_clicks (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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user_id INTEGER,
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plan TEXT NOT NULL,
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billing TEXT NOT NULL DEFAULT 'monthly',
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created_at TEXT NOT NULL
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)
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"""
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)
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conn.execute(
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"CREATE INDEX IF NOT EXISTS idx_plan_interest_created ON plan_interest_clicks(created_at)"
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)
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def record_plan_interest_click(user_id: int | None, plan: str, billing: str = "monthly") -> None:
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"""Record a click on a pricing plan button."""
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_ensure_plan_interest_table()
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now = datetime.now(timezone.utc).isoformat()
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with _connect() as conn:
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conn.execute(
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"INSERT INTO plan_interest_clicks (user_id, plan, billing, created_at) VALUES (?, ?, ?, ?)",
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(user_id, plan, billing, now),
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)
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def get_plan_interest_summary() -> dict:
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"""Return summary of paid plan button clicks."""
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_ensure_plan_interest_table()
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cutoff_7d = (datetime.now(timezone.utc) - timedelta(days=7)).isoformat()
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cutoff_30d = (datetime.now(timezone.utc) - timedelta(days=30)).isoformat()
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with _connect() as conn:
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total_row = conn.execute(
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"""
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SELECT
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COUNT(*) AS total_clicks,
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COUNT(DISTINCT user_id) AS unique_users,
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COALESCE(SUM(CASE WHEN created_at >= ? THEN 1 ELSE 0 END), 0) AS clicks_last_7d,
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COALESCE(SUM(CASE WHEN created_at >= ? THEN 1 ELSE 0 END), 0) AS clicks_last_30d
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FROM plan_interest_clicks
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""",
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(cutoff_7d, cutoff_30d),
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).fetchone()
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by_plan_rows = conn.execute(
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"""
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SELECT plan, billing, COUNT(*) AS clicks
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FROM plan_interest_clicks
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GROUP BY plan, billing
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ORDER BY clicks DESC
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"""
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).fetchall()
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recent_rows = conn.execute(
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"""
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SELECT
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plan_interest_clicks.id,
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plan_interest_clicks.user_id,
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plan_interest_clicks.plan,
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plan_interest_clicks.billing,
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plan_interest_clicks.created_at,
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users.email
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FROM plan_interest_clicks
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LEFT JOIN users ON users.id = plan_interest_clicks.user_id
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ORDER BY plan_interest_clicks.created_at DESC
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LIMIT 20
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"""
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).fetchall()
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return {
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"total_clicks": int(total_row["total_clicks"]) if total_row else 0,
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"unique_users": int(total_row["unique_users"]) if total_row else 0,
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"clicks_last_7d": int(total_row["clicks_last_7d"]) if total_row else 0,
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"clicks_last_30d": int(total_row["clicks_last_30d"]) if total_row else 0,
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"by_plan": [
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{"plan": row["plan"], "billing": row["billing"], "clicks": int(row["clicks"])}
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for row in by_plan_rows
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],
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"recent": [
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{
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"id": row["id"],
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"user_id": row["user_id"],
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"email": row["email"],
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"plan": row["plan"],
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"billing": row["billing"],
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"created_at": row["created_at"],
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}
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for row in recent_rows
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],
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}
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def get_admin_ratings_detail(page: int = 1, per_page: int = 20, tool_filter: str = "") -> dict:
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"""Return detailed ratings list with feedback for the admin dashboard."""
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safe_page = max(1, page)
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safe_per_page = max(1, min(per_page, 100))
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offset = (safe_page - 1) * safe_per_page
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with _connect() as conn:
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# Total count
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count_sql = "SELECT COUNT(*) AS total FROM tool_ratings"
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count_params: list[object] = []
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if tool_filter:
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count_sql += " WHERE tool = ?"
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count_params.append(tool_filter)
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total_row = conn.execute(count_sql, tuple(count_params)).fetchone()
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# Paginated ratings
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sql = """
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SELECT id, tool, rating, feedback, tag, fingerprint, created_at
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FROM tool_ratings
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"""
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params: list[object] = []
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if tool_filter:
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sql += " WHERE tool = ?"
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params.append(tool_filter)
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sql += " ORDER BY created_at DESC LIMIT ? OFFSET ?"
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params.extend([safe_per_page, offset])
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rows = conn.execute(sql, tuple(params)).fetchall()
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# Per-tool summary
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summary_rows = conn.execute(
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"""
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SELECT
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tool,
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COUNT(*) AS count,
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COALESCE(AVG(rating), 0) AS average,
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COALESCE(SUM(CASE WHEN rating >= 4 THEN 1 ELSE 0 END), 0) AS positive,
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COALESCE(SUM(CASE WHEN rating <= 2 THEN 1 ELSE 0 END), 0) AS negative
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FROM tool_ratings
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GROUP BY tool
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ORDER BY count DESC
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"""
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).fetchall()
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return {
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"items": [
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{
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"id": row["id"],
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"tool": row["tool"],
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"rating": int(row["rating"]),
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"feedback": row["feedback"] or "",
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"tag": row["tag"] or "",
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"created_at": row["created_at"],
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}
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for row in rows
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],
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"page": safe_page,
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"per_page": safe_per_page,
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"total": int(total_row["total"]) if total_row else 0,
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"tool_summaries": [
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{
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"tool": row["tool"],
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"count": int(row["count"]),
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"average": round(row["average"], 1),
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"positive": int(row["positive"]),
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"negative": int(row["negative"]),
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}
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for row in summary_rows
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],
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}
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def get_admin_tool_analytics() -> dict:
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"""Return detailed per-tool usage analytics for the admin dashboard."""
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cutoff_24h = (datetime.now(timezone.utc) - timedelta(days=1)).isoformat()
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cutoff_7d = (datetime.now(timezone.utc) - timedelta(days=7)).isoformat()
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cutoff_30d = (datetime.now(timezone.utc) - timedelta(days=30)).isoformat()
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with _connect() as conn:
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# Per-tool detailed stats
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tool_rows = conn.execute(
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"""
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SELECT
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tool,
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COUNT(*) AS total_runs,
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COALESCE(SUM(CASE WHEN status = 'completed' THEN 1 ELSE 0 END), 0) AS completed,
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COALESCE(SUM(CASE WHEN status = 'failed' THEN 1 ELSE 0 END), 0) AS failed,
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COALESCE(SUM(CASE WHEN created_at >= ? THEN 1 ELSE 0 END), 0) AS runs_24h,
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COALESCE(SUM(CASE WHEN created_at >= ? THEN 1 ELSE 0 END), 0) AS runs_7d,
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COALESCE(SUM(CASE WHEN created_at >= ? THEN 1 ELSE 0 END), 0) AS runs_30d,
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COUNT(DISTINCT user_id) AS unique_users
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FROM file_history
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GROUP BY tool
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ORDER BY total_runs DESC
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""",
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(cutoff_24h, cutoff_7d, cutoff_30d),
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).fetchall()
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# Daily usage for the last 30 days
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daily_rows = conn.execute(
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"""
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SELECT
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DATE(created_at) AS day,
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COUNT(*) AS total,
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COALESCE(SUM(CASE WHEN status = 'completed' THEN 1 ELSE 0 END), 0) AS completed,
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COALESCE(SUM(CASE WHEN status = 'failed' THEN 1 ELSE 0 END), 0) AS failed
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FROM file_history
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WHERE created_at >= ?
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GROUP BY DATE(created_at)
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ORDER BY day ASC
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""",
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(cutoff_30d,),
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).fetchall()
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# Most common errors
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error_rows = conn.execute(
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"""
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SELECT
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tool,
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metadata_json,
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COUNT(*) AS occurrences
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FROM file_history
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WHERE status = 'failed' AND created_at >= ?
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GROUP BY tool, metadata_json
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ORDER BY occurrences DESC
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LIMIT 15
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""",
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(cutoff_30d,),
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).fetchall()
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return {
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"tools": [
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{
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"tool": row["tool"],
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"total_runs": int(row["total_runs"]),
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"completed": int(row["completed"]),
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"failed": int(row["failed"]),
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"success_rate": round((int(row["completed"]) / int(row["total_runs"])) * 100, 1) if int(row["total_runs"]) > 0 else 0,
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"runs_24h": int(row["runs_24h"]),
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"runs_7d": int(row["runs_7d"]),
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"runs_30d": int(row["runs_30d"]),
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"unique_users": int(row["unique_users"]),
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}
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for row in tool_rows
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],
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"daily_usage": [
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{
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"day": row["day"],
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"total": int(row["total"]),
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"completed": int(row["completed"]),
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"failed": int(row["failed"]),
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}
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for row in daily_rows
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],
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"common_errors": [
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{
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"tool": row["tool"],
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"error": _parse_metadata(row["metadata_json"]).get("error", "Unknown error"),
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"occurrences": int(row["occurrences"]),
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}
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for row in error_rows
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],
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}
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def get_admin_user_registration_stats() -> dict:
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"""Return user registration trends and breakdown."""
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cutoff_7d = (datetime.now(timezone.utc) - timedelta(days=7)).isoformat()
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cutoff_30d = (datetime.now(timezone.utc) - timedelta(days=30)).isoformat()
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with _connect() as conn:
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totals_row = conn.execute(
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"""
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SELECT
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COUNT(*) AS total,
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COALESCE(SUM(CASE WHEN created_at >= ? THEN 1 ELSE 0 END), 0) AS last_7d,
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COALESCE(SUM(CASE WHEN created_at >= ? THEN 1 ELSE 0 END), 0) AS last_30d,
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COALESCE(SUM(CASE WHEN plan = 'pro' THEN 1 ELSE 0 END), 0) AS pro_count,
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COALESCE(SUM(CASE WHEN plan = 'free' THEN 1 ELSE 0 END), 0) AS free_count
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FROM users
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""",
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(cutoff_7d, cutoff_30d),
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).fetchone()
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# Daily registrations for the last 30 days
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daily_rows = conn.execute(
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"""
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SELECT DATE(created_at) AS day, COUNT(*) AS registrations
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FROM users
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WHERE created_at >= ?
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GROUP BY DATE(created_at)
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ORDER BY day ASC
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""",
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(cutoff_30d,),
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).fetchall()
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# Most active users (by task count)
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active_rows = conn.execute(
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"""
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SELECT
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users.id,
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users.email,
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users.plan,
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users.created_at,
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COUNT(file_history.id) AS total_tasks
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FROM users
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JOIN file_history ON file_history.user_id = users.id
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GROUP BY users.id
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ORDER BY total_tasks DESC
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LIMIT 10
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"""
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).fetchall()
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return {
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"total_users": int(totals_row["total"]) if totals_row else 0,
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"new_last_7d": int(totals_row["last_7d"]) if totals_row else 0,
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"new_last_30d": int(totals_row["last_30d"]) if totals_row else 0,
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"pro_users": int(totals_row["pro_count"]) if totals_row else 0,
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"free_users": int(totals_row["free_count"]) if totals_row else 0,
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"daily_registrations": [
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{"day": row["day"], "count": int(row["registrations"])}
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for row in daily_rows
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],
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"most_active_users": [
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{
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"id": row["id"],
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"email": row["email"],
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"plan": row["plan"],
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"created_at": row["created_at"],
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"total_tasks": int(row["total_tasks"]),
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}
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for row in active_rows
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],
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}
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def get_admin_system_health() -> dict:
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"""Return system health indicators for the admin dashboard."""
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from app.services.openrouter_config_service import get_openrouter_settings
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ai_cost_summary = get_monthly_spend()
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settings = get_openrouter_settings()
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with _connect() as conn:
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# Recent error rate (last 1h)
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cutoff_1h = (datetime.now(timezone.utc) - timedelta(hours=1)).isoformat()
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error_row = conn.execute(
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"""
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SELECT
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COUNT(*) AS total,
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COALESCE(SUM(CASE WHEN status = 'failed' THEN 1 ELSE 0 END), 0) AS failed
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FROM file_history
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WHERE created_at >= ?
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""",
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(cutoff_1h,),
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).fetchone()
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# DB size
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db_path = current_app.config["DATABASE_PATH"]
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db_size_mb = round(os.path.getsize(db_path) / (1024 * 1024), 2) if os.path.exists(db_path) else 0
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error_total = int(error_row["total"]) if error_row else 0
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error_failed = int(error_row["failed"]) if error_row else 0
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return {
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"ai_configured": bool(settings.api_key),
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"ai_model": settings.model,
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"ai_budget_used_percent": ai_cost_summary["budget_used_percent"],
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"error_rate_1h": round((error_failed / error_total) * 100, 1) if error_total > 0 else 0,
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"tasks_last_1h": error_total,
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"failures_last_1h": error_failed,
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"database_size_mb": db_size_mb,
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}
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