import gradio as gr
from gpt4all import GPT4All
from huggingface_hub import hf_hub_download
from diarizationlm import utils

title = "DiarizationLM GGUF inference on CPU"

description = """
A demo of the DiarizationLM model finetuned from Llama 2. In this demo, we run a 4-bit quantized GGUF model on CPU. 

To learn more about DiarizationLM, check our paper: https://arxiv.org/abs/2401.03506
"""

model_path = "models"
model_name = "q4_k_m.gguf"
prompt_suffix = " --> "
completion_suffix = " [eod]"

hf_hub_download(repo_id="google/DiarizationLM-13b-Fisher-v1", filename=model_name, local_dir=model_path, local_dir_use_symlinks=False)

print("Start the model init process")
model = GPT4All(model_name=model_name,
                model_path=model_path,
                allow_download = False, 
                evice="cpu")
print("Finish the model init process")

def generater(message, history):
    prompt = message + prompt_suffix
    max_new_tokens = round(len(prompt) / 3.0 * 1.2)
    outputs = []    
    for token in model.generate(prompt=prompt,
                                temp=0.0,
                                top_k=50,
                                top_p=0.9,
                                max_tokens=max_new_tokens,
                                streaming=True):
        outputs.append(token)
        completion = "".join(outputs)
        if completion.endswith(" [eod]"):
            transferred_completion = utils.transfer_llm_completion(completion, message)
            yield transferred_completion
            return
        else:
            yield completion
        
def vote(data: gr.LikeData):
    if data.liked:
        return
    else:
        return

print("Create chatbot")
chatbot = gr.Chatbot()
print("Created chatbot")

iface = gr.ChatInterface(
    fn = generater,
    title=title,
    description = description,
    chatbot=chatbot,
    additional_inputs=[],
    examples=[
        ["<speaker:1> Hello, how are you doing <speaker:2> today? I am doing well. What about <speaker:1> you? I'm doing well, too. Thank you."],
   ]
)

with gr.Blocks() as demo:
    chatbot.like(vote, None, None)
    iface.render()


if __name__ == "__main__":
    demo.queue(max_size=3).launch()