Platform bundle: marketing publish, IT ops, packaging, agents mesh.

Volledige Foodlinkk Command Center uitbreiding met social automatisering, reclamefolder filters, Proxmox monitoring en documentatie.
2026-06-09 00:41:27 +00:00
commit 5d60d33db1
212 changed files with 30044 additions and 0 deletions
@@ -0,0 +1,80 @@
+from __future__ import annotations
+
+from datetime import datetime, timedelta
+from typing import Optional
+
+from psycopg2.extras import RealDictCursor
+
+from app.db import get_connection
+
+
+def sentiment_score(text: str) -> float:
+    try:
+        from textblob import TextBlob
+
+        blob = TextBlob(text)
+        score = (blob.sentiment.polarity + 1) * 2 + 1
+    except Exception:
+        t = text.lower()
+        neg = sum(1 for w in ("bad", "teleurgest", "klacht", "lang", "duur", "fout") if w in t)
+        pos = sum(1 for w in ("geweldig", "aanrader", "fantast", "mooi", "lekker", "top") if w in t)
+        raw = 3.0 + (pos - neg) * 0.5
+        score = max(1.0, min(5.0, raw))
+    return max(1.0, min(5.0, round(float(score), 2)))
+
+
+def evaluate_agent_rules(mention_id: Optional[int] = None) -> None:
+    with get_connection() as conn:
+        with conn.cursor(cursor_factory=RealDictCursor) as cur:
+            cur.execute("SELECT * FROM agent_rules WHERE is_active = TRUE")
+            rules = cur.fetchall()
+
+            for rule in rules:
+                if rule["condition_type"] == "sentiment_below":
+                    threshold = rule["threshold"] or 2.0
+                    if mention_id:
+                        cur.execute(
+                            "SELECT id, text, sentiment_score FROM social_mentions WHERE id = %s AND sentiment_score < %s",
+                            (mention_id, threshold),
+                        )
+                    else:
+                        cur.execute(
+                            "SELECT id, text, sentiment_score FROM social_mentions WHERE sentiment_score < %s ORDER BY created_at DESC LIMIT 5",
+                            (threshold,),
+                        )
+                    matches = cur.fetchall()
+                    for m in matches:
+                        cur.execute(
+                            "SELECT 1 FROM agent_logs WHERE rule_id = %s AND message LIKE %s",
+                            (rule["id"], f"%mention #{m['id']}%"),
+                        )
+                        if cur.fetchone():
+                            continue
+                        msg = (
+                            f"ALERT [{rule['name']}]: Negatief sentiment ({m['sentiment_score']}/5) "
+                            f"op mention #{m['id']}: {(m['text'] or '')[:120]}"
+                        )
+                        cur.execute(
+                            "INSERT INTO agent_logs (rule_id, message) VALUES (%s, %s)",
+                            (rule["id"], msg),
+                        )
+
+                elif rule["condition_type"] == "mention_spike":
+                    threshold = int(rule["threshold"] or 5)
+                    since = datetime.now() - timedelta(hours=24)
+                    cur.execute(
+                        "SELECT COUNT(*) AS cnt FROM social_mentions WHERE created_at > %s",
+                        (since,),
+                    )
+                    count = cur.fetchone()["cnt"]
+                    if count >= threshold:
+                        msg = f"ALERT [{rule['name']}]: {count} mentions in 24u (drempel: {threshold})"
+                        cur.execute(
+                            "SELECT 1 FROM agent_logs WHERE rule_id = %s AND message = %s AND created_at > %s",
+                            (rule["id"], msg, since),
+                        )
+                        if not cur.fetchone():
+                            cur.execute(
+                                "INSERT INTO agent_logs (rule_id, message) VALUES (%s, %s)",
+                                (rule["id"], msg),
+                            )