import gradio as gr
import os 
from pathlib import Path
import argparse
model_file = "Yi-6B.q4_k_m.gguf"
if not os.path.isfile("Yi-6B.q4_k_m.gguf"):
    os.system("wget -c https://huggingface.co/SamPurkis/Yi-6B-GGUF/resolve/main/Yi-6B.q4_k_m.gguf")

DEFAULT_MODEL_PATH = model_file

from llama_cpp import Llama
llm = Llama(model_path=model_file)
old_tokenize = llm._model.tokenize
llm._model.tokenize = lambda text, add_bos, spec: old_tokenize(text, False, spec)


def predict(input, chatbot, max_length, top_p, temperature, history):
    chatbot.append((input, ""))
    response = ""
    history.append(input)

    for output in llm(input, stream=True, temperature=temperature, top_p=top_p, max_tokens=max_length, ):
        piece = output['choices'][0]['text']
        response += piece
        chatbot[-1] = (chatbot[-1][0], response)

        yield chatbot, history

    history.append(response)
    yield chatbot, history


def reset_user_input():
    return gr.update(value="")


def reset_state():
    return [], []


with gr.Blocks() as demo:
    gr.HTML("""<h1 align="center">Yi-6B-GGUF by llama-cpp-python</h1>""")

    chatbot = gr.Chatbot()
    with gr.Row():
        with gr.Column(scale=4):
            user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=8)
            submitBtn = gr.Button("Submit", variant="primary")
        with gr.Column(scale=1):
            max_length = gr.Slider(0, 32048, value=2048, step=1.0, label="Maximum Length", interactive=True)
            top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True)
            temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="Temperature", interactive=True)
            emptyBtn = gr.Button("Clear History")

    history = gr.State([])

    submitBtn.click(
        predict, [user_input, chatbot, max_length, top_p, temperature, history], [chatbot, history], show_progress=True
    )
    submitBtn.click(reset_user_input, [], [user_input])

    emptyBtn.click(reset_state, outputs=[chatbot, history], show_progress=True)

demo.queue().launch(share=False, inbrowser=True)