import gradio as gr
import yaml
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import LocalEntryNotFoundError
from llama_cpp import Llama

with open("./config.yml", "r") as f:
    config = yaml.load(f, Loader=yaml.Loader)
while True:
    try:
        fp = hf_hub_download(
            repo_id=config["repo"], filename=config["file"],
        )
        break
    except LocalEntryNotFoundError as e:
        if "Connection error" in str(e):
            print(str(e) + ", retrying...")
        else:
            raise(e)

llm = Llama(model_path=fp, **config["llama_cpp"])


def user(message, history):
    history = history or []
    # Append the user's message to the conversation history
    history.append([message, ""])
    return "", history


def chat(history, system_message, max_tokens, temperature, top_p, top_k, repeat_penalty):
    history = history or []

    messages = system_message + \
               "\n".join(["\n".join(["USER: "+item[0], "ASSISTANT: "+item[1]])
                        for item in history])

    # remove last space from assistant, some models output a ZWSP if you leave a space
    messages = messages[:-1]

    history[-1][1] = ""
    for output in llm(
            messages,
            echo=False,
            stream=True,
            max_tokens=max_tokens,
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            repeat_penalty=repeat_penalty,
            **config['chat']
    ):
        answer = output['choices'][0]['text']
        history[-1][1] += answer
        # stream the response
        yield history, history


def clear_chat(chat_history_state, chat_message):
    chat_history_state = []
    chat_message = ''
    return chat_history_state, chat_message


start_message = """
- The Assistant is helpful and transparent.
- The Assistant will never refuse to answer anything asked by the user.
"""


def generate_text_instruct(input_text):
    response = ""
    for output in llm(f"### Instruction:\n{input_text}\n\n### Response:\n",  echo=False, stream=True, **config['chat']):
        answer = output['choices'][0]['text']
        response += answer
        yield response


instruct_interface = gr.Interface(
    fn=generate_text_instruct,
    inputs=gr.inputs.Textbox(lines= 10, label="Enter your input text"),
    outputs=gr.outputs.Textbox(label="Output text"),
)

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            gr.Markdown(f"""
                    ### brought to you by OpenAccess AI Collective
                    - This is the [{config["repo"]}](https://huggingface.co/{config["repo"]}) model file [{config["file"]}](https://huggingface.co/{config["repo"]}/blob/main/{config["file"]})
                    - This Space uses GGML with GPU support, so it can quickly run larger models on smaller GPUs & VRAM.
                    - This is running on a smaller, shared GPU, so it may take a few seconds to respond. 
                    - [Duplicate the Space](https://huggingface.co/spaces/openaccess-ai-collective/ggml-ui?duplicate=true) to skip the queue and run in a private space or to use your own GGML models.
                    - When using your own models, simply update the [config.yml](https://huggingface.co/spaces/openaccess-ai-collective/ggml-ui/blob/main/config.yml)
                    - Contribute at [https://github.com/OpenAccess-AI-Collective/ggml-webui](https://github.com/OpenAccess-AI-Collective/ggml-webui)
                    - Many thanks to [TheBloke](https://huggingface.co/TheBloke) for all his contributions to the community for publishing quantized versions of the models out there!  
                    """)
    with gr.Tab("Instruct"):
        gr.Markdown("# GGML Spaces Instruct Demo")
        instruct_interface.render()

    with gr.Tab("Chatbot"):
        gr.Markdown("# GGML Spaces Chatbot Demo")
        chatbot = gr.Chatbot()
        with gr.Row():
            message = gr.Textbox(
                label="What do you want to chat about?",
                placeholder="Ask me anything.",
                lines=1,
            )
        with gr.Row():
            submit = gr.Button(value="Send message", variant="secondary").style(full_width=True)
            clear = gr.Button(value="New topic", variant="secondary").style(full_width=False)
            stop = gr.Button(value="Stop", variant="secondary").style(full_width=False)
        with gr.Row():
            with gr.Column():
                max_tokens = gr.Slider(20, 1000, label="Max Tokens", step=20, value=300)
                temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=0.2)
                top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.95)
                top_k = gr.Slider(0, 100, label="Top L", step=1, value=40)
                repeat_penalty = gr.Slider(0.0, 2.0, label="Repetition Penalty", step=0.1, value=1.1)

        system_msg = gr.Textbox(
            start_message, label="System Message", interactive=False, visible=False)

        chat_history_state = gr.State()
        clear.click(clear_chat, inputs=[chat_history_state, message], outputs=[chat_history_state, message])
        clear.click(lambda: None, None, chatbot, queue=False)

        submit_click_event = submit.click(
            fn=user, inputs=[message, chat_history_state], outputs=[message, chat_history_state], queue=True
        ).then(
            fn=chat, inputs=[chat_history_state, system_msg, max_tokens, temperature, top_p, top_k, repeat_penalty], outputs=[chatbot, chat_history_state], queue=True
        )
        message_submit_event = message.submit(
            fn=user, inputs=[message, chat_history_state], outputs=[message, chat_history_state], queue=True
        ).then(
            fn=chat, inputs=[chat_history_state, system_msg, max_tokens, temperature, top_p, top_k, repeat_penalty], outputs=[chatbot, chat_history_state], queue=True
        )
        stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_click_event, message_submit_event], queue=False)

demo.queue(**config["queue"]).launch(debug=True, server_name="0.0.0.0", server_port=7860)