Update app.py

app.py

@@ -31,21 +31,6 @@ subprocess.run(
 sys.path.insert(0, os.path.join(LTX_REPO_DIR, "packages", "ltx-pipelines", "src"))
 sys.path.insert(0, os.path.join(LTX_REPO_DIR, "packages", "ltx-core", "src"))
 
-# Patch LTX tokenizer max_length before imports load it
-import pathlib
-_tokenizer_file = pathlib.Path(LTX_REPO_DIR) / "packages/ltx-core/src/ltx_core/text_encoders/gemma/tokenizer.py"
-if _tokenizer_file.exists():
-    _src = _tokenizer_file.read_text()
-    _patched = _src.replace(
-        "def __init__(self, tokenizer_path: str, max_length: int = 1024):",
-        "def __init__(self, tokenizer_path: str, max_length: int = 4096):",
-    )
-    if _patched != _src:
-        _tokenizer_file.write_text(_patched)
-        print("[Patch] Tokenizer max_length patched: 1024 → 4096")
-    else:
-        print("[Patch] Tokenizer max_length already patched or pattern not found")
-
 _I2V_SYSTEM_PROMPT = (
     "You are a Creative Assistant writing concise, action-focused image-to-video prompts."
     " Given an image (first frame) and user Raw Input Prompt, generate a prompt to guide"
@@ -462,6 +447,24 @@ if os.path.exists(weight_link):
 os.symlink(MERGED_WEIGHTS, weight_link)
 print(f"[Gemma] Root ready: {gemma_root}")
 
+# ↓ INSERT BLOCK 2 HERE ↓
+import json as _json
+
+_tok_config_path = os.path.join(gemma_root, "tokenizer_config.json")
+if os.path.islink(_tok_config_path) or os.path.exists(_tok_config_path):
+    with open(_tok_config_path, "r") as _f:
+        _tok_config = _json.load(_f)
+    _old = _tok_config.get("model_max_length", "unset")
+    _tok_config["model_max_length"] = 4096
+    if os.path.islink(_tok_config_path):
+        os.remove(_tok_config_path)
+    with open(_tok_config_path, "w") as _f:
+        _json.dump(_tok_config, _f, indent=2)
+    print(f"[Patch] tokenizer_config.json model_max_length: {_old} → 4096")
+else:
+    print("[Patch] tokenizer_config.json not found in gemma_root")
+# ↑ END BLOCK 2 ↑
+
 # ---- Insert block (LoRA downloads) between lines 268 and 269 ----
 # LoRA repo + download the requested LoRA adapters
 LORA_REPO = "dagloop5/LoRA"