import os
# ! UI Markdown information
MODEL_TITLE = """
SeaLLMs - Large Language Models for Southeast Asia
"""
# 
# 
#
MODEL_DESC = f"""
SeaLMMM-7B-early - multilingual multimodal assistant for Southeast Asia. It handles both text-only (LLMs and vision instructions (LVMs). SeaLMMM-7B has not finished training.
The chatbot may produce false and harmful content!
By using our service, you are required to agree to our Terms Of Use
""".strip()
MODEL_DESC = f"""
{MODEL_NAME}-v2 - a helpful assistant for Southeast Asian Languages 🇬🇧 🇻🇳 🇮🇩 🇹🇠🇲🇾 🇰🇠🇱🇦 🇵🇠🇲🇲.
Explore our article for more.
NOTE: The chatbot may produce false and harmful content and does not have up-to-date knowledge.
By using our service, you are required to agree to our Terms Of Use, which includes
not to use our service to generate any harmful, inappropriate or illegal content.
The service collects user dialogue data for testing and improvement under
(CC-BY) or similar license. So do not enter any personal information!
""".strip()
"""
By using our service, you are required to agree to our Terms Of Use, which includes
not to use our service to generate any harmful, inappropriate or illegal content.
The service collects user dialogue data for testing and improvement under
(CC-BY) or similar license. So do not enter any personal information!
"""
# MODEL_INFO = """
# Model Name: {model_path}
# """
MODEL_INFO = ""
CITE_MARKDOWN = """
## Citation
If you find our project useful, hope you can star our repo and cite our paper as follows:
```
@article{damonlpsg2023seallm,
author = {Xuan-Phi Nguyen*, Wenxuan Zhang*, Xin Li*, Mahani Aljunied*, Zhiqiang Hu, Chenhui Shen^, Yew Ken Chia^, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing},
title = {SeaLLMs - Large Language Models for Southeast Asia},
year = 2023,
}
```
"""
USE_PANEL = bool(int(os.environ.get("USE_PANEL", "1")))
CHATBOT_HEIGHT = int(os.environ.get("CHATBOT_HEIGHT", "500"))
ALLOWED_PATHS = ["seammm_2.png"]
DEMOS = os.environ.get("DEMOS", "")
DEMOS = DEMOS.split(",") if DEMOS.strip() != "" else [
"DocChatInterfaceDemo",
"ChatInterfaceDemo",
"TextCompletionDemo",
# "RagChatInterfaceDemo",
# "VisionChatInterfaceDemo",
# "VisionDocChatInterfaceDemo",
]
# DEMOS=DocChatInterfaceDemo,ChatInterfaceDemo,RagChatInterfaceDemo,TextCompletionDemo
# ! server info
DELETE_FOLDER = os.environ.get("DELETE_FOLDER", "")
PORT = int(os.environ.get("PORT", "7860"))
PROXY = os.environ.get("PROXY", "").strip()
# ! backend info
BACKEND = os.environ.get("BACKEND", "debug")
# ! model information
# for RAG
RAG_EMBED_MODEL_NAME = os.environ.get("RAG_EMBED_MODEL_NAME", "sentence-transformers/all-MiniLM-L6-v2")
CHUNK_SIZE = int(os.environ.get("CHUNK_SIZE", "1024"))
CHUNK_OVERLAP = int(os.environ.get("CHUNK_SIZE", "50"))
SYSTEM_PROMPT = os.environ.get("SYSTEM_PROMPT", """You are a helpful, respectful, honest and safe AI assistant.""")
MAX_TOKENS = int(os.environ.get("MAX_TOKENS", "2048"))
TEMPERATURE = float(os.environ.get("TEMPERATURE", "0.1"))
# ! these values currently not used
FREQUENCE_PENALTY = float(os.environ.get("FREQUENCE_PENALTY", "0.0"))
PRESENCE_PENALTY = float(os.environ.get("PRESENCE_PENALTY", "0.0"))
# Transformers or vllm
MODEL_PATH = os.environ.get("MODEL_PATH", "mistralai/Mistral-7B-Instruct-v0.2")
MODEL_NAME = os.environ.get("MODEL_NAME", "Cool-Chatbot")
DTYPE = os.environ.get("DTYPE", "bfloat16")
DEVICE = os.environ.get("DEVICE", "cuda")
# VLLM
GPU_MEMORY_UTILIZATION = float(os.environ.get("GPU_MEMORY_UTILIZATION", "0.9"))
TENSOR_PARALLEL = int(os.environ.get("TENSOR_PARALLEL", "1"))
QUANTIZATION = str(os.environ.get("QUANTIZATION", ""))
STREAM_YIELD_MULTIPLE = int(os.environ.get("STREAM_YIELD_MULTIPLE", "1"))
# how many iterations to perform safety check on response
STREAM_CHECK_MULTIPLE = int(os.environ.get("STREAM_CHECK_MULTIPLE", "0"))
# llama.cpp
DEFAULT_CHAT_TEMPLATE = os.environ.get("DEFAULT_CHAT_TEMPLATE", "chatml")
N_CTX = int(os.environ.get("N_CTX", "4096"))
N_GPU_LAYERS = int(os.environ.get("N_GPU_LAYERS", "-1"))
# llava.llama.cpp
# Multimodal
IMAGE_TOKEN = os.environ.get("IMAGE_TOKEN", "[IMAGE]<|image|>[/IMAGE]")
IMAGE_TOKEN_INTERACTIVE = bool(int(os.environ.get("IMAGE_TOKEN_INTERACTIVE", "0")))
IMAGE_TOKEN_LENGTH = int(os.environ.get("IMAGE_TOKEN_LENGTH", "576"))
MAX_PACHES = int(os.environ.get("MAX_PACHES", "1"))
