patch_models.py

"""Patch backend: OpenRouter routing + DeepSeek V4 Flash 0731."""

import ast
import os
import re

AGENT_FILE = "/app/backend/routes/agent.py"
LLM_PARAMS_FILE = "/app/agent/core/llm_params.py"
IDS_FILE = "/app/agent/core/model_ids.py"

with open(IDS_FILE) as f:
    content = f.read()

# Remove NO_TOOLS_MODELS if present from previous patches
if "NO_TOOLS_MODELS" in content:
    content = re.sub(r'NO_TOOLS_MODELS\s*=\s*\{[^}]*\}\n?', '', content)
    print("OK: Removed old NO_TOOLS_MODELS")

# Fix model IDs for OpenRouter
content = content.replace(
    'KIMI_K27_CODE_MODEL_ID = "moonshotai/Kimi-K2.7-Code:novita"',
    'KIMI_K27_CODE_MODEL_ID = "deepseek-ai/DeepSeek-V4-Pro"'
)
content = content.replace(
    'MINIMAX_M3_MODEL_ID = "MiniMaxAI/MiniMax-M3:novita"',
    'MINIMAX_M3_MODEL_ID = "deepseek-ai/DeepSeek-V4-Flash"'
)
content = content.replace(
    'GLM_52_MODEL_ID = "zai-org/GLM-5.2:novita"',
    'GLM_52_MODEL_ID = "openai/deepseek/deepseek-v4-flash"'
)
content = content.replace(
    'DEEPSEEK_V4_PRO_MODEL_ID = "deepseek-ai/DeepSeek-V4-Pro:novita"',
    'DEEPSEEK_V4_PRO_MODEL_ID = "nvidia/nemotron-3-super-120b-a12b:free"'
)

# Add missing constants before DEEPSEEK_V4_PRO_MODEL_ID line
insert_before = '''TENCENT_HY3_FREE_MODEL_ID = "openai/tencent/hy3:free"
GEMMA_4_31B_FREE_MODEL_ID = "openai/google/gemma-4-31b-it:free"
LLAMA_3_3_70B_FREE_MODEL_ID = "openai/meta-llama/llama-3.3-70b-instruct:free"
LLAMA_3_1_8B_MODEL_ID = "openai/meta-llama/llama-3.1-8b-instruct"
LAGUNA_M1_FREE_MODEL_ID = "openai/poolside/laguna-m.1:free"
LAGUNA_S21_FREE_MODEL_ID = "openai/poolside/laguna-s-2.1:free"
NEX_N2_MINI_MODEL_ID = "openai/nex-agi/nex-n2-mini"
LING_3_0_FLASH_FREE_MODEL_ID = "openai/inclusionai/ling-3.0-flash:free"
'''
if "TENCENT_HY3_FREE_MODEL_ID" not in content:
    content = content.replace('DEEPSEEK_V4_PRO_MODEL_ID', insert_before + 'DEEPSEEK_V4_PRO_MODEL_ID')

# Replace all references to latest deepseek models with 0731 version
content = content.replace('"~deepseek/deepseek-v4-flash-latest"', '"deepseek/deepseek-v4-flash-0731"')
content = content.replace('"~deepseek/deepseek-v4-flash-0731"', '"deepseek/deepseek-v4-flash-0731"')

with open(IDS_FILE, "w") as f:
    f.write(content)
print("OK: model_ids.py patched")

ast.parse(content)  # validate syntax
print("OK: model_ids.py syntax OK")

with open(AGENT_FILE) as f:
    content = f.read()

content = content.replace(
    "DEFAULT_MODEL_ID = GLM_52_MODEL_ID",
    'DEFAULT_MODEL_ID = "openai/tencent/hy3:free"'
)
content = content.replace(
    "DEFAULT_GPT_MODEL_ID = GPT_55_MODEL_ID",
    "DEFAULT_GPT_MODEL_ID = LAGUNA_S21_FREE_MODEL_ID"
)

# Update imports
old_import = (
    "from agent.core.model_ids import (\n"
    "    CLAUDE_OPUS_48_MODEL_ID,\n"
    "    DEEPSEEK_V4_PRO_MODEL_ID,\n"
    "    GLM_52_MODEL_ID,\n"
    "    GPT_55_MODEL_ID,\n"
    "    KIMI_K27_CODE_MODEL_ID,\n"
    "    MINIMAX_M3_MODEL_ID,\n"
    "    strip_huggingface_model_prefix,\n"
    ")"
)
new_import = (
    "from agent.core.model_ids import (\n"
    "    DEEPSEEK_V4_PRO_MODEL_ID,\n"
    "    GLM_52_MODEL_ID,\n"
    "    GPT_55_MODEL_ID,\n"
    "    KIMI_K27_CODE_MODEL_ID,\n"
    "    MINIMAX_M3_MODEL_ID,\n"
    "    TENCENT_HY3_FREE_MODEL_ID,\n"
    "    GEMMA_4_31B_FREE_MODEL_ID,\n"
    "    LLAMA_3_3_70B_FREE_MODEL_ID,\n"
    "    LLAMA_3_1_8B_MODEL_ID,\n"
    "    LAGUNA_M1_FREE_MODEL_ID,\n"
    "    LAGUNA_S21_FREE_MODEL_ID,\n"
    "    NEX_N2_MINI_MODEL_ID,\n"
    "    LING_3_0_FLASH_FREE_MODEL_ID,\n"
    "    strip_huggingface_model_prefix,\n"
    ")"
)
content = content.replace(old_import, new_import)

# Update _available_models to include DeepSeek V4 Flash 0731
new_func = '''def _available_models() -> list[dict[str, Any]]:
    models = [
        {"id": TENCENT_HY3_FREE_MODEL_ID, "label": "Tencent HY3:free", "recommended": True},
        {"id": GEMMA_4_31B_FREE_MODEL_ID, "label": "Gemma 4 31B:free", "recommended": True},
        {"id": LLAMA_3_3_70B_FREE_MODEL_ID, "label": "Llama 3.3 70B:free", "recommended": True},
        {"id": LAGUNA_M1_FREE_MODEL_ID, "label": "Laguna M.1:free", "recommended": True},
        {"id": DEFAULT_GPT_MODEL_ID, "label": "Laguna S 2.1:free", "recommended": True},
        {"id": NEX_N2_MINI_MODEL_ID, "label": "nex-agi/nex-n2-mini", "recommended": True},
        {"id": LING_3_0_FLASH_FREE_MODEL_ID, "label": "inclusionai/ling-3.0-flash:free", "recommended": True},
        {"id": LLAMA_3_1_8B_MODEL_ID, "label": "Llama 3.1 8B"},
        {"id": KIMI_K27_CODE_MODEL_ID, "label": "DeepSeek V4 Pro"},
        {"id": MINIMAX_M3_MODEL_ID, "label": "DeepSeek V4 Flash"},
        {"id": DEFAULT_MODEL_ID, "label": "DeepSeek V4 Flash"},
        {"id": DEEPSEEK_V4_PRO_MODEL_ID, "label": "Nemotron 3 Super 120B"},
        {"id": "deepseek/deepseek-v4-flash-0731", "label": "DeepSeek V4 Flash 0731", "recommended": True},
    ]
    return models'''

func_match = re.search(
    r"def _available_models\(\)\s*->\s*list\[dict\[str,\s*Any\]\]:.*?return models",
    content, re.DOTALL
)
if func_match:
    content = content[:func_match.start()] + new_func + content[func_match.end():]
    print("OK: Replaced _available_models() with DeepSeek V4 Flash 0731")
else:
    old_func = (
        "def _available_models() -> list[dict[str, Any]]:\n"
        "    models = [\n"
        '        {"id": CLAUDE_OPUS_48_MODEL_ID, "label": "Claude Opus 4.8"},\n'
        '        {"id": DEFAULT_GPT_MODEL_ID, "label": "GPT-5.5"},\n'
        '        {"id": KIMI_K27_CODE_MODEL_ID, "label": "Kimi K2.7 Code"},\n'
        '        {"id": MINIMAX_M3_MODEL_ID, "label": "MiniMax M3"},\n'
        '        {"id": DEFAULT_MODEL_ID, "label": "GLM 5.2", "recommended": True},\n'
        '        {"id": DEEPSEEK_V4_PRO_MODEL_ID, "label": "DeepSeek V4 Pro"},\n'
        "    ]\n"
        "    return models"
    )
    if old_func in content:
        content = content.replace(old_func, new_func)
        print("OK: String replace of _available_models()")
    else:
        print("WARN: Could not find _available_models() pattern, searching...")
        idx = content.find("def _available_models")
        if idx >= 0:
            print(f"Found at index {idx}, context: {content[idx:idx+400]}")

content = content.replace(
    '"openai/gpt-oss-120b:cerebras",',
    '"huggingface/deepseek-ai/DeepSeek-V4-Pro",'
)

try:
    ast.parse(content)
    print("OK: agent.py syntax OK")
except SyntaxError as e:
    print(f"FAIL: agent.py syntax error: {e}")
    raise

with open(AGENT_FILE, "w") as f:
    f.write(content)

# Patch llm_params for OpenRouter routing
with open(LLM_PARAMS_FILE) as f:
    llm_content = f.read()

if "normalized_model.startswith" not in llm_content:
    api_key_find = "api_key = _resolve_hf_router_token(session_hf_token)"
    if api_key_find in llm_content:
        line_end = llm_content.find("\n", llm_content.find(api_key_find) + len(api_key_find)) + 1
        routing_insert = (
            '    # Route deepseek/ models to OpenRouter\n'
            '    if normalized_model.startswith("deepseek/"):\n'
            '        return {\n'
            '            "model": normalized_model,\n'
            '            "api_base": "https://openrouter.ai/api/v1",\n'
            '            "api_key": os.environ.get("OPENROUTER_API_KEY") or api_key or "",\n'
            '        }\n\n'
            '    # Route openai/-prefixed models to OpenRouter\n'
            '    if normalized_model.startswith("openai/"):\n'
            '        return {\n'
            '            "model": normalized_model,\n'
            '            "api_base": "https://openrouter.ai/api/v1",\n'
            '            "api_key": os.environ.get("OPENROUTER_API_KEY") or api_key or "",\n'
            '        }\n\n'
        )
        llm_content = llm_content[:line_end] + routing_insert + llm_content[line_end:]
        print("OK: Patched _resolve_llm_params with deepseek/ routing")
    else:
        print("WARN: Could not find api_key line")
else:
    print("OK: llm_params.py already patched")

try:
    ast.parse(llm_content)
    print("OK: llm_params.py syntax OK")
except SyntaxError as e:
    print(f"FAIL: llm_params.py syntax error: {e}")
    raise

with open(LLM_PARAMS_FILE, "w") as f:
    f.write(llm_content)

print("OK: ALL BACKEND PATCHES APPLIED")