import torch
import copy
import gradio as gr
import spaces
from llama_cpp import Llama
import llama_cpp.llama_tokenizer
import os
from huggingface_hub import hf_hub_download


HF_TOKEN = os.environ.get("HF_TOKEN", None)
MODEL_ID = "google/gemma-2-27b-it"
REPO_ID = "bartowski/gemma-2-27b-it-GGUF"
MODEL_NAME = MODEL_ID.split("/")[-1]
MODEL_FILE = "gemma-2-27b-it-Q4_K_M.gguf"

os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"

llm = llama_cpp.Llama.from_pretrained(
    repo_id=REPO_ID,
    filename=MODEL_FILE,
    tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained(MODEL_ID),
    verbose=False,
) 

TITLE = "<h1><center>Chatbox</center></h1>"

DESCRIPTION = f"""
<h3>MODEL: <a href="https://hf.co/{MODELS}">{MODEL_NAME}</a></h3>
<center>
<p>Gemma is the large language model built by Google.
<br>
Feel free to test without log.
</p>
</center>
"""

CSS = """
.duplicate-button {
    margin: auto !important;
    color: white !important;
    background: black !important;
    border-radius: 100vh !important;
}
h3 {
    text-align: center;
}
"""


@spaces.GPU(duration=90)
def stream_chat(message: str, history: list, temperature: float, max_new_tokens: int, top_p: float, top_k: int, penalty: float):
    print(f'message is - {message}')
    print(f'history is - {history}')
    conversation = []
    for prompt, answer in history:
        conversation.extend([{"role": "user", "content": prompt}, {"role": "assistant", "content": answer}])
    conversation.append({"role": "user", "content": message})

    print(f"Conversation is -\n{conversation}")
    
    output = llm(
        messages=conversation,
        top_k=top_k,
        top_p=top_p,
        repeat_penalty=penalty,
        max_tokens=max_new_tokens, 
        stream =True, 
        temperature=temperature,
    )
    
    for out in output:
        stream = copy.deepcopy(out)
        temp += stream["choices"][0]["text"]
        yield temp



chatbot = gr.Chatbot(height=600)

with gr.Blocks(css=CSS, theme="soft") as demo:
    gr.HTML(TITLE)
    gr.HTML(DESCRIPTION)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
    gr.ChatInterface(
        fn=stream_chat,
        chatbot=chatbot,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
        additional_inputs=[
            gr.Slider(
                minimum=0,
                maximum=1,
                step=0.1,
                value=0.8,
                label="Temperature",
                render=False,
            ),
            gr.Slider(
                minimum=128,
                maximum=2048,
                step=1,
                value=1024,
                label="Max Tokens",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=1.0,
                step=0.1,
                value=0.8,
                label="top_p",
                render=False,
            ),
            gr.Slider(
                minimum=1,
                maximum=20,
                step=1,
                value=20,
                label="top_k",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=2.0,
                step=0.1,
                value=1.0,
                label="Repetition penalty",
                render=False,
            ),
        ],
        examples=[
            ["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."],
            ["What are 5 creative things I could do with my kids' art? I don't want to throw them away, but it's also so much clutter."],
            ["Tell me a random fun fact about the Roman Empire."],
            ["Show me a code snippet of a website's sticky header in CSS and JavaScript."],
        ],
        cache_examples=False,
    )


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