import gradio as gr
from llama_cpp import Llama

# 1. Path to your GGUF file inside the Space repository
#MODEL_PATH = "simonper/fine-tuned-gguf-modal1/Llama-3.2-1B.Q8_0.gguf"   # <- change if your file is named differently

llm = Llama.from_pretrained(
	repo_id="simonper/fine-tuned-gguf-modal1",
	filename="Llama-3.2-1B.Q8_0.gguf",
)

"""
# 2. Load the GGUF model once at startup
llm = Llama(
    model_path=MODEL_PATH,
    n_ctx=4096,        # context length, adjust if needed
    n_threads=8,       # tweak based on CPU in the Space
    n_gpu_layers=0,    # 0 = pure CPU, >0 if GPU layers are available
)
"""

def build_prompt(system_message: str, history: list[dict], user_message: str) -> str:
    """
    Simple instruction-style prompt builder for GGUF/llama.cpp.
    You can make this fancier or closer to Llama 3's official format if you want.
    """
    lines = []

    if system_message:
        lines.append(f"System: {system_message}\n")

    for turn in history:
        role = turn["role"]
        content = turn["content"]
        if role == "user":
            lines.append(f"User: {content}")
        elif role == "assistant":
            lines.append(f"Assistant: {content}")

    lines.append(f"User: {user_message}")
    lines.append("Assistant:")

    return "\n".join(lines)


def respond(
    message,
    history: list[dict[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    # 3. Build a text prompt from system + history + new message
    prompt = build_prompt(system_message, history, message)

    # 4. Call llama.cpp model
    output = llm(
        prompt,
        max_tokens=int(max_tokens),
        temperature=float(temperature),
        top_p=float(top_p),
        stop=["User:", "System:"],  # stop when next user/system turn would start
    )

    reply = output["choices"][0]["text"].strip()
    return reply


# 5. Gradio UI
chatbot = gr.ChatInterface(
    respond,
    type="messages",   # history comes in as [{"role": "...", "content": "..."}]
    additional_inputs=[
        gr.Textbox(
            value="You are a friendly chatbot.",
            label="System message",
        ),
        gr.Slider(
            minimum=1,
            maximum=2048,
            value=512,
            step=1,
            label="Max new tokens",
        ),
        gr.Slider(
            minimum=0.1,
            maximum=4.0,
            value=0.7,
            step=0.1,
            label="Temperature",
        ),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)

demo = chatbot

if __name__ == "__main__":
    demo.launch()




# Old UI implementation
'''
import gradio as gr
from huggingface_hub import InferenceClient


def respond(
    message,
    history: list[dict[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    hf_token: gr.OAuthToken,
):
    """
    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
    """
    client = InferenceClient(token=hf_token.token, model="meta-llama/Meta-Llama-3-8B")

    messages = [{"role": "system", "content": system_message}]

    messages.extend(history)

    messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        choices = message.choices
        token = ""
        if len(choices) and choices[0].delta.content:
            token = choices[0].delta.content

        response += token
        yield response


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
chatbot = gr.ChatInterface(
    respond,
    type="messages",
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)

with gr.Blocks() as demo:
    with gr.Sidebar():
        gr.LoginButton()
    chatbot.render()


if __name__ == "__main__":
    demo.launch()
'''