Sanitize MSR analysis prompt echo

ltx_director.py

@@ -512,6 +512,58 @@ def _analysis_text_is_usable(text: str) -> bool:
     return True
 
 
+def _sanitize_analysis_text(text: str) -> str:
+    text = (text or "").strip()
+    if not text:
+        return ""
+
+    if "<think>" in text:
+        text = text.split("</think>")[-1].strip()
+
+    cleaned_lines = []
+    for raw_line in text.splitlines():
+        line = raw_line.strip().replace("**", "")
+        if not line:
+            continue
+        lower = line.lower()
+        if lower.startswith("the user wants"):
+            continue
+        if lower.startswith("key elements to include"):
+            continue
+        if lower.startswith("drafting the sentences"):
+            continue
+        if lower.startswith("sentence 1"):
+            continue
+        if lower.startswith("sentence 2"):
+            continue
+        if line.startswith("*"):
+            continue
+        if re.match(r"^\d+\.\s", line):
+            continue
+        cleaned_lines.append(line)
+
+    text = " ".join(cleaned_lines).strip()
+    text = re.sub(r"\s+", " ", text)
+    if not text:
+        return ""
+
+    sentences = re.split(r"(?<=[.!?])\s+", text)
+    descriptive = []
+    for sentence in sentences:
+        sentence = sentence.strip()
+        if not sentence:
+            continue
+        lower = sentence.lower()
+        if "the user wants" in lower or "key elements" in lower or "drafting the sentences" in lower:
+            continue
+        descriptive.append(sentence)
+
+    if descriptive:
+        return " ".join(descriptive[-2:]).strip()
+
+    return text
+
+
 def _compress_analysis_image_b64(b64_payload: str, max_dim: int = 768, quality: int = 82) -> str:
     """Shrink analysis images so multimodal providers do not burn their full context on pixels."""
     try:
@@ -674,8 +726,7 @@ async def analyze_character_endpoint(request):
                 "message": f"Could not connect to {provider} at {base_url}. Make sure the server is running and reachable.",
             })
 
-        if "<think>" in generated_text:
-            generated_text = generated_text.split("</think>")[-1].strip()
+        generated_text = _sanitize_analysis_text(generated_text)
 
         log.info("[LTXDirector] Analysis complete: %s", generated_text)
         return web.json_response({"status": "success", "description": generated_text})