fix: parse Claude.ai privacy export with messages key and sender field (#677) (#685)

* fix: parse Claude.ai privacy export with messages key and sender field (#677)

The privacy-export branch in _try_claude_ai_json only checked for the
"chat_messages" key, missing exports that use "messages" instead.  It
also only read the "role" field while real privacy exports use "sender".
Both gaps caused the file to fall through to plain-text, producing a
single giant drawer.

Changes:
- Accept "messages" alongside "chat_messages" in the conversation-object
  guard and inner extraction.
- Accept "sender" alongside "role" as the author field.
- Fall back to a top-level "text" key when content blocks are empty.
- Produce one transcript per conversation instead of concatenating all
  conversations into a single blob.
- Extract shared logic into _collect_claude_messages helper.
- Add 6 regression tests covering each variant.

* style: apply ruff format to normalize.py

* fix: guard against null text field in Claude.ai export parsing

item.get("text", "").strip() crashes when "text" is explicitly null
in the JSON (legal and observed in some exports). Use
(item.get("text") or "").strip() and add a regression test.

---------

Co-authored-by: Igor Lins e Silva <4753812+igorls@users.noreply.github.com>

mempalace/normalize.py

@@ -30,7 +30,7 @@ def normalize(filepath: str) -> str:
     except OSError as e:
         raise IOError(f"Could not read {filepath}: {e}")
     if file_size > 500 * 1024 * 1024:  # 500 MB safety limit
-        raise IOError(f"File too large ({file_size // (1024*1024)} MB): {filepath}")
+        raise IOError(f"File too large ({file_size // (1024 * 1024)} MB): {filepath}")
     try:
         with open(filepath, "r", encoding="utf-8", errors="replace") as f:
             content = f.read()
@@ -190,40 +190,46 @@ def _try_claude_ai_json(data) -> Optional[str]:
     if not isinstance(data, list):
         return None
 
-    # Privacy export: array of conversation objects with chat_messages inside each
-    if data and isinstance(data[0], dict) and "chat_messages" in data[0]:
-        all_messages = []
+    # Privacy export: array of conversation objects, each containing its own
+    # message list under "chat_messages" or "messages" (both variants seen in the wild).
+    if data and isinstance(data[0], dict) and ("chat_messages" in data[0] or "messages" in data[0]):
+        transcripts = []
         for convo in data:
             if not isinstance(convo, dict):
                 continue
-            chat_msgs = convo.get("chat_messages", [])
-            for item in chat_msgs:
-                if not isinstance(item, dict):
-                    continue
-                role = item.get("role", "")
-                text = _extract_content(item.get("content", ""))
-                if role in ("user", "human") and text:
-                    all_messages.append(("user", text))
-                elif role in ("assistant", "ai") and text:
-                    all_messages.append(("assistant", text))
-        if len(all_messages) >= 2:
-            return _messages_to_transcript(all_messages)
+            chat_msgs = convo.get("chat_messages") or convo.get("messages", [])
+            messages = _collect_claude_messages(chat_msgs)
+            if len(messages) >= 2:
+                transcripts.append(_messages_to_transcript(messages))
+        if transcripts:
+            return "\n\n".join(transcripts)
         return None
 
     # Flat messages list
+    messages = _collect_claude_messages(data)
+    if len(messages) >= 2:
+        return _messages_to_transcript(messages)
+    return None
+
+
+def _collect_claude_messages(items) -> list:
+    """Extract (role, text) pairs from a Claude.ai message list.
+
+    Accepts both ``role`` (API format) and ``sender`` (privacy export) as the
+    author field, and falls back to a top-level ``text`` key when the
+    ``content`` blocks are empty or absent.
+    """
     messages = []
-    for item in data:
+    for item in items:
         if not isinstance(item, dict):
             continue
-        role = item.get("role", "")
-        text = _extract_content(item.get("content", ""))
+        role = item.get("role") or item.get("sender", "")
+        text = _extract_content(item.get("content", "")) or (item.get("text") or "").strip()
         if role in ("user", "human") and text:
             messages.append(("user", text))
         elif role in ("assistant", "ai") and text:
             messages.append(("assistant", text))
-    if len(messages) >= 2:
-        return _messages_to_transcript(messages)
-    return None
+    return messages
 
 
 def _try_chatgpt_json(data) -> Optional[str]: