Odysseus v1.0

src/topic_analyzer.py

@@ -0,0 +1,85 @@
+"""
+Topic analysis for conversations — deduplicated from app.py.
+Used by /api/conversations/topics and /api/memory/extract fallback.
+"""
+
+import re
+from typing import Dict, Any, List
+
+TOPIC_KEYWORDS: Dict[str, List[str]] = {
+    "Technology": ["ai", "machine learning", "python", "code", "programming", "computer", "software", "hardware", "algorithm"],
+    "Science": ["science", "physics", "chemistry", "biology", "math", "mathematics", "research", "experiment"],
+    "Work": ["work", "job", "career", "project", "task", "deadline", "meeting", "colleague", "manager"],
+    "Personal": ["personal", "family", "friend", "relationship", "health", "wellness", "exercise", "diet"],
+    "Learning": ["learn", "study", "education", "course", "tutorial", "guide", "how to", "explain"],
+    "Creativity": ["write", "story", "create", "design", "art", "music", "draw", "paint"],
+    "Planning": ["plan", "schedule", "organize", "arrange", "coordinate", "timeline", "calendar"],
+    "Troubleshooting": ["error", "bug", "fix", "problem", "issue", "debug", "troubleshoot"],
+}
+
+
+def analyze_topics(session_manager, owner: str = None) -> Dict[str, Any]:
+    """
+    Scan non-archived sessions and return topic frequency data.
+    If owner is set, only include sessions belonging to that user.
+
+    Returns dict with "topics" list and "total_topics" count.
+    """
+    topic_counts: Dict[str, int] = {t: 0 for t in TOPIC_KEYWORDS}
+    topic_matches: Dict[str, list] = {t: [] for t in TOPIC_KEYWORDS}
+
+    for session_id, session_data in session_manager.sessions.items():
+        if session_data.get("archived", False):
+            continue
+        # SECURITY: strict ownership — the previous predicate let any
+        # null-owner session feed into another user's topic analysis.
+        if owner:
+            sess_owner = session_data.get("owner") or getattr(session_data, "owner", None)
+            if sess_owner != owner:
+                continue
+
+        for msg in session_data.get("history", []):
+            content_raw = msg.get("content") if isinstance(msg, dict) else getattr(msg, "content", None)
+            if not content_raw:
+                continue
+
+            content = str(content_raw).lower()
+            role = msg.get("role") if isinstance(msg, dict) else getattr(msg, "role", "")
+            session_name = session_data.get("name", f"Session {session_id[:6]}")
+
+            for topic, keywords in TOPIC_KEYWORDS.items():
+                for kw in keywords:
+                    if kw in content:
+                        topic_counts[topic] += 1
+                        sentences = re.split(r'[.!?]', str(content_raw))
+                        for sentence in sentences:
+                            if kw in sentence.lower():
+                                topic_matches[topic].append({
+                                    "session_id": session_id,
+                                    "session_name": session_name,
+                                    "role": role,
+                                    "snippet": sentence.strip(),
+                                    "keyword": kw,
+                                })
+                                break
+
+    results = []
+    for topic, count in topic_counts.items():
+        if count == 0:
+            continue
+        matches = topic_matches[topic]
+        unique, seen = [], set()
+        for m in matches:
+            key = f"{m['session_id']}-{m['snippet'][:50]}"
+            if key not in seen:
+                seen.add(key)
+                unique.append(m)
+        results.append({
+            "topic": topic,
+            "frequency": count,
+            "examples": unique[:5],
+            "session_count": len({m["session_id"] for m in unique}),
+        })
+
+    results.sort(key=lambda x: x["frequency"], reverse=True)
+    return {"topics": results, "total_topics": len(results)}