import os
import gradio as gr
from llama_cpp import Llama
from huggingface_hub import hf_hub_download

model = Llama(
    model_path=hf_hub_download(
        repo_id=os.environ.get("REPO_ID", "Lyte/LLaMA-O1-Supervised-1129-Q4_K_M-GGUF"),
        filename=os.environ.get("MODEL_FILE", "llama-o1-supervised-1129-q4_k_m.gguf"),
    )
)

DESCRIPTION = '''
# SimpleBerry/LLaMA-O1-Supervised-1129 | Duplicate the space and set it to private for faster & personal inference for free.
SimpleBerry/LLaMA-O1-Supervised-1129: an experimental research model developed by the SimpleBerry.  
Focused on advancing AI reasoning capabilities.  

**To start a new chat**, click "clear" and start a new dialog.
'''

LICENSE = """
--- MIT License ---
"""

template = "<start_of_father_id>-1<end_of_father_id><start_of_local_id>0<end_of_local_id><start_of_thought><problem>{content}<end_of_thought><start_of_rating><positive_rating><end_of_rating>\n<start_of_father_id>0<end_of_father_id><start_of_local_id>1<end_of_local_id><start_of_thought><expansion>"

def llama_o1_template(data):
    #query = data['query']
    text = template.format(content=data)
    return text

def generate_text(message, history, max_tokens=512, temperature=0.9, top_p=0.95):
    temp = ""
    input_texts = [llama_o1_template(message)]
    input_texts = [input_text.replace('<|end_of_text|>','') for input_text in input_texts]
    #print(f"input_texts[0]: {input_texts[0]}")
    inputs = model.tokenize(input_texts[0].encode('utf-8'))
    for token in model.generate(inputs, top_p=top_p, temp=temperature):
        #print(f"token: {token}")
        text = model.detokenize([token])
        #print(f"text detok: {text}")
        temp += text.decode('utf-8')
        yield temp


with gr.Blocks() as demo:
    gr.Markdown(DESCRIPTION)

    chatbot = gr.ChatInterface(
        generate_text,
        title="SimpleBerry/LLaMA-O1-Supervised-1129 | GGUF Demo",
        description="Edit Settings below if needed.",
        examples=[
            ["How many r's are in the word strawberry?"],
            ['What is an LLM? and how can we make it reach AGI?'],
            ['Explain to me how gravity works like I am 5!'],
        ],
        cache_examples=False,
        fill_height=True
    )

    with gr.Accordion("Adjust Parameters", open=False):
        gr.Slider(minimum=1024, maximum=8192, value=2048, step=1, label="Max Tokens")
        gr.Slider(minimum=0.1, maximum=1.5, value=0.7, step=0.1, label="Temperature")
        gr.Slider(minimum=0.05, maximum=1.0, value=0.95, step=0.01, label="Top-p (nucleus sampling)")

    gr.Markdown(LICENSE)

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