from llama_cpp import Llama
from huggingface_hub import hf_hub_download

# Download the single GGUF shard by its repo path:
model_path = hf_hub_download(
    repo_id="Inventors-Hub/SwarmChat-models",
    repo_type="model",
    filename="EuroLLM-9B-Instruct-Q4_K_M.gguf",
)

# llm = Llama(model_path=model_path, n_ctx=1024)#, verbose=True)
llm = Llama(
    model_path=model_path,
    n_ctx=512,            # down from 4096
    low_vram=True,         # llama.cpp low-vram mode
    f16_kv=True,           # half-precision kv cache
    use_mmap=True,         # mmap file
    use_mlock=False,
)
# print("Llama backend initialized successfully!")



# Function to process text using EuroLLM
def translate_text(text):
    input_prompt = f"""
    <|im_start|>system
    <|im_end|>
    <|im_start|>user
    Translate the following text to English:
    Text: {text}
    English: 
    <|im_end|>
    <|im_start|>assistant
    """
    output = llm(input_prompt, max_tokens=1024, temperature=0)

    translated_text = output.get("choices", [{}])[0].get("text", "").strip()

    return translated_text