Retry truncated Ollama character analysis

ltx_director.py

@@ -466,6 +466,17 @@ def _extract_ollama_generated_text(resp_json: dict) -> str:
     return ""
 
 
+def _analysis_text_is_usable(text: str) -> bool:
+    text = (text or "").strip()
+    if not text:
+        return False
+    if len(text) < 24:
+        return False
+    if len(text.split()) < 6:
+        return False
+    return True
+
+
 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:
@@ -540,6 +551,10 @@ async def analyze_character_endpoint(request):
                         "images": analysis_images,
                         "stream": False,
                         "keep_alive": 0,
+                        "options": {
+                            "temperature": 0.2,
+                            "num_predict": 120,
+                        },
                     }
                     async with session.post(f"{base_url}/api/generate", json=payload, timeout=300) as response:
                         if response.status != 200:
@@ -558,6 +573,28 @@ async def analyze_character_endpoint(request):
                         else:
                             resp_json = await response.json()
                             generated_text = _extract_ollama_generated_text(resp_json)
+
+                    if not _analysis_text_is_usable(generated_text):
+                        chat_payload = {
+                            "model": model_name,
+                            "messages": [{
+                                "role": "user",
+                                "content": _ANALYZE_PROMPT,
+                                "images": analysis_images,
+                            }],
+                            "stream": False,
+                            "keep_alive": 0,
+                            "options": {
+                                "temperature": 0.2,
+                                "num_predict": 120,
+                            },
+                        }
+                        async with session.post(f"{base_url}/api/chat", json=chat_payload, timeout=300) as response:
+                            if response.status == 200:
+                                resp_json = await response.json()
+                                chat_text = _extract_ollama_generated_text(resp_json)
+                                if _analysis_text_is_usable(chat_text):
+                                    generated_text = chat_text
                 else:
                     # OpenAI-compatible vision chat (LM Studio / Custom).
                     content = [{"type": "text", "text": _ANALYZE_PROMPT}]