app.py

import os
import re
import json
import copy
import gradio as gr

from llama2 import GradioLLaMA2ChatPPManager
from llama2 import gen_text

from styles import MODEL_SELECTION_CSS
from js import GET_LOCAL_STORAGE, UPDATE_LEFT_BTNS_STATE, UPDATE_PLACEHOLDERS
from templates import templates
from constants import DEFAULT_GLOBAL_CTX

from pingpong import PingPong
from pingpong.context import CtxLastWindowStrategy

TOKEN = os.getenv('HF_TOKEN')
MODEL_ID = 'meta-llama/Llama-2-70b-chat-hf'

def build_prompts(ppmanager, global_context, win_size=3):
    dummy_ppm = copy.deepcopy(ppmanager)
    dummy_ppm.ctx = global_context
    lws = CtxLastWindowStrategy(win_size)
    return lws(dummy_ppm)

ex_file = open("examples.txt", "r")
examples = ex_file.read().split("\n")
ex_btns = []

chl_file = open("channels.txt", "r")
channels = chl_file.read().split("\n")
channel_btns = []

def get_placeholders(text):
    """Returns all substrings in between <placeholder> and </placeholder>."""
    pattern = r"\[([^\]]*)\]"
    matches = re.findall(pattern, text)
    return matches

def fill_up_placeholders(txt):
    placeholders = get_placeholders(txt)
    highlighted_txt = txt

    return (
        gr.update(
            visible=True,
            value=highlighted_txt
        ),
        gr.update(
            visible=True if len(placeholders) >= 1 else False,
            placeholder=placeholders[0] if len(placeholders) >= 1 else ""
        ),
        gr.update(
            visible=True if len(placeholders) >= 2 else False,
            placeholder=placeholders[1] if len(placeholders) >= 2 else ""
        ),
        gr.update(
            visible=True if len(placeholders) >= 3 else False,
            placeholder=placeholders[2] if len(placeholders) >= 3 else ""
        ),
        "" if len(placeholders) >= 1 else txt
    )

async def rollback_last(
    idx, local_data, chat_state,
    global_context, res_temp, res_topk, res_rpen, res_mnts, res_sample, ctx_num_lconv
):
    res = [
      chat_state["ppmanager_type"].from_json(json.dumps(ppm))
      for ppm in local_data
    ]

    ppm = res[idx]
    last_user_message = res[idx].pingpongs[-1].ping
    res[idx].pingpongs = res[idx].pingpongs[:-1]
    
    ppm.add_pingpong(
        PingPong(last_user_message, "")
    )
    prompt = build_prompts(ppm, global_context, ctx_num_lconv)
    async for result in gen_text(
        prompt, hf_model=MODEL_ID, hf_token=TOKEN,
        parameters={
            'max_new_tokens': res_mnts,
            'do_sample': res_sample,
            'return_full_text': False,
            'temperature': res_temp,
            'top_k': res_topk,
            'repetition_penalty': res_rpen           
        }
    ):
        ppm.append_pong(result)
        yield prompt, ppm.build_uis(), str(res), gr.update(interactive=False)
        
    yield prompt, ppm.build_uis(), str(res), gr.update(interactive=True)

def reset_chat(idx, ld, state):
    res = [state["ppmanager_type"].from_json(json.dumps(ppm_str)) for ppm_str in ld]
    res[idx].pingpongs = []
        
    return (
        "",
        [],
        str(res),
        gr.update(visible=True),
        gr.update(interactive=False),
    )

async def chat_stream(
    idx, local_data, instruction_txtbox, chat_state,
    global_context, res_temp, res_topk, res_rpen, res_mnts, res_sample, ctx_num_lconv
):
    res = [
      chat_state["ppmanager_type"].from_json(json.dumps(ppm))
      for ppm in local_data
    ]

    ppm = res[idx]
    ppm.add_pingpong(
        PingPong(instruction_txtbox, "")
    )
    prompt = build_prompts(ppm, global_context, ctx_num_lconv)
    async for result in gen_text(
        prompt, hf_model=MODEL_ID, hf_token=TOKEN,
        parameters={
            'max_new_tokens': res_mnts,
            'do_sample': res_sample,
            'return_full_text': False,
            'temperature': res_temp,
            'top_k': res_topk,
            'repetition_penalty': res_rpen           
        }
    ):
        ppm.append_pong(result)
        yield "", prompt, ppm.build_uis(), str(res), gr.update(interactive=False)

    yield "", prompt, ppm.build_uis(), str(res), gr.update(interactive=True)

def channel_num(btn_title):
    choice = 0

    for idx, channel in enumerate(channels):
        if channel == btn_title:
            choice = idx

    return choice

def set_chatbot(btn, ld, state):
    choice = channel_num(btn)

    res = [state["ppmanager_type"].from_json(json.dumps(ppm_str)) for ppm_str in ld]
    empty = len(res[choice].pingpongs) == 0
    return (res[choice].build_uis(), choice, gr.update(visible=empty), gr.update(interactive=not empty))

def set_example(btn):
    return btn, gr.update(visible=False)

def get_final_template(
    txt, placeholder_txt1, placeholder_txt2, placeholder_txt3
):
    placeholders = get_placeholders(txt)
    example_prompt = txt    

    if len(placeholders) >= 1:
        if placeholder_txt1 != "":
            example_prompt = example_prompt.replace(f"[{placeholders[0]}]", placeholder_txt1)
    if len(placeholders) >= 2:
        if placeholder_txt2 != "":
            example_prompt = example_prompt.replace(f"[{placeholders[1]}]", placeholder_txt2)
    if len(placeholders) >= 3:
        if placeholder_txt3 != "":
            example_prompt = example_prompt.replace(f"[{placeholders[2]}]", placeholder_txt3)

    return (
        example_prompt,
        "",
        "",
        ""
    )

with gr.Blocks(css=MODEL_SELECTION_CSS, theme='gradio/soft') as demo:
    with gr.Column() as chat_view:
        idx = gr.State(0)
        chat_state = gr.State({
            "ppmanager_type": GradioLLaMA2ChatPPManager
        })
        local_data = gr.JSON({}, visible=False)

        gr.Markdown("## LLaMA2 70B with Gradio Chat and Hugging Face Inference API", elem_classes=["center"])
        gr.Markdown(
            "This space demonstrates how to build feature rich chatbot UI in [Gradio](https://www.gradio.app/). Supported features "
            "include • multiple chatting channels, • chat history save/restoration, • stop generating text response, • regenerate the "
            "last conversation, • clean the chat history, • dynamic kick-starting prompt templates, • adjusting text generation parameters, "
            "• inspecting the actual prompt that the model sees. The underlying Large Language Model is the [Meta AI](https://ai.meta.com/)'s "
            "[LLaMA2-70B](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) which is hosted as [Hugging Face Inference API](https://huggingface.co/inference-api), "
            "and [Text Generation Inference](https://github.com/huggingface/text-generation-inference) is the underlying serving framework. ",
            elem_classes=["center"]
        )
        gr.Markdown(
            "***NOTE:*** If you are subscribing [PRO](https://huggingface.co/pricing#pro), you can simply duplicate this space and use your "
            "Hugging Face Access Token to run the same application. Just add `HF_TOKEN` secret with the Token value accorindg to [this guide](https://huggingface.co/docs/hub/spaces-overview#managing-secrets-and-environment-variables)",
            elem_classes=["center"]
        )
            
        
        with gr.Row():
            with gr.Column(scale=1, min_width=180):
                gr.Markdown("GradioChat", elem_id="left-top")

                with gr.Column(elem_id="left-pane"):
                    chat_back_btn = gr.Button("Back", elem_id="chat-back-btn")
                    
                    with gr.Accordion("Histories", elem_id="chat-history-accordion", open=True):
                        channel_btns.append(gr.Button(channels[0], elem_classes=["custom-btn-highlight"]))

                        for channel in channels[1:]:
                            channel_btns.append(gr.Button(channel, elem_classes=["custom-btn"]))

            with gr.Column(scale=8, elem_id="right-pane"):
                with gr.Column(
                    elem_id="initial-popup", visible=False
                ) as example_block:
                    with gr.Row(scale=1):
                        with gr.Column(elem_id="initial-popup-left-pane"):
                            gr.Markdown("GradioChat", elem_id="initial-popup-title")
                            gr.Markdown("Making the community's best AI chat models available to everyone.")
                        with gr.Column(elem_id="initial-popup-right-pane"):
                            gr.Markdown("Chat UI is now open sourced on Hugging Face Hub")
                            gr.Markdown("check out the [↗ repository](https://huggingface.co/spaces/chansung/test-multi-conv)")

                    with gr.Column(scale=1):
                        gr.Markdown("Examples")
                        with gr.Row():
                            for example in examples:
                                ex_btns.append(gr.Button(example, elem_classes=["example-btn"]))

                with gr.Column(elem_id="aux-btns-popup", visible=True):
                    with gr.Row():
                        # stop = gr.Button("Stop", elem_classes=["aux-btn"])
                        regenerate = gr.Button("Regen", interactive=False, elem_classes=["aux-btn"])
                        clean = gr.Button("Clean", elem_classes=["aux-btn"])

                with gr.Accordion("Context Inspector", elem_id="aux-viewer", open=False):
                    context_inspector = gr.Textbox(
                        "",
                        elem_id="aux-viewer-inspector",
                        label="",
                        lines=30,
                        max_lines=50,
                    )                        
                        
                chatbot = gr.Chatbot(elem_id='chatbot', label="LLaMA2-70B-Chat")
                instruction_txtbox = gr.Textbox(placeholder="Ask anything", label="", elem_id="prompt-txt")

        with gr.Accordion("Example Templates", open=False):
            template_txt = gr.Textbox(visible=False)
            template_md = gr.Markdown(label="Chosen Template", visible=False, elem_classes="template-txt")

            with gr.Row():
                placeholder_txt1 = gr.Textbox(label="placeholder #1", visible=False, interactive=True)
                placeholder_txt2 = gr.Textbox(label="placeholder #2", visible=False, interactive=True)
                placeholder_txt3 = gr.Textbox(label="placeholder #3", visible=False, interactive=True)

            for template in templates:
                with gr.Tab(template['title']):
                    gr.Examples(
                        template['template'],
                        inputs=[template_txt],
                        outputs=[template_md, placeholder_txt1, placeholder_txt2, placeholder_txt3, instruction_txtbox],
                        run_on_click=True,
                        fn=fill_up_placeholders,          
                    )

        with gr.Accordion("Control Panel", open=False) as control_panel:
            with gr.Column():
                with gr.Column():
                    gr.Markdown("#### Global context")
                    with gr.Accordion("global context will persist during conversation, and it is placed at the top of the prompt", open=True):
                        global_context = gr.Textbox(
                            DEFAULT_GLOBAL_CTX,
                            lines=5,
                            max_lines=10,
                            interactive=True,
                            elem_id="global-context"
                        )
                    
                    # gr.Markdown("#### Internet search")
                    # with gr.Row():
                    #     internet_option = gr.Radio(choices=["on", "off"], value="off", label="mode")
                    #     serper_api_key = gr.Textbox(
                    #         value= "" if args.serper_api_key is None else args.serper_api_key,
                    #         placeholder="Get one by visiting serper.dev", 
                    #         label="Serper api key"
                    #     )
                    
                    gr.Markdown("#### GenConfig for **response** text generation")
                    with gr.Row():
                        res_temp = gr.Slider(0.0, 2.0, 1.0, step=0.1, label="temp", interactive=True)
                        res_topk = gr.Slider(20, 1000, 50, step=1, label="top_k", interactive=True)
                        res_rpen = gr.Slider(0.0, 2.0, 1.2, step=0.1, label="rep_penalty", interactive=True)
                        res_mnts = gr.Slider(64, 8192, 512, step=1, label="new_tokens", interactive=True)
                        res_sample = gr.Radio([True, False], value=True, label="sample", interactive=True)
                
                with gr.Column():
                    gr.Markdown("#### Context managements")
                    with gr.Row():
                        ctx_num_lconv = gr.Slider(2, 10, 3, step=1, label="number of recent talks to keep", interactive=True)

    send_event = instruction_txtbox.submit(
        lambda: [
            gr.update(visible=False),
            gr.update(interactive=True)
        ],
        None,
        [example_block, regenerate]
    ).then(
        chat_stream,
        [idx, local_data, instruction_txtbox, chat_state,
         global_context, res_temp, res_topk, res_rpen, res_mnts, res_sample, ctx_num_lconv],
        [instruction_txtbox, context_inspector, chatbot, local_data, regenerate]
    ).then(
        None, local_data, None, 
        _js="(v)=>{ setStorage('local_data',v) }"
    )

    # regen_event1 = regenerate.click(
    #     rollback_last,
    #     [idx, local_data, chat_state],
    #     [instruction_txtbox, chatbot, local_data, regenerate]
    # )
    # regen_event2 = regen_event1.then(
    #     chat_stream,
    #     [idx, local_data, instruction_txtbox, chat_state,
    #      global_context, res_temp, res_topk, res_rpen, res_mnts, res_sample, ctx_num_lconv],
    #     [context_inspector, chatbot, local_data]
    # )
    # regen_event3 = regen_event2.then(
    #     lambda: gr.update(interactive=True),
    #     None,
    #     regenerate
    # )
    # regen_event4 = regen_event3.then(
    #     None, local_data, None, 
    #     _js="(v)=>{ setStorage('local_data',v) }"  
    # )

    regen_event = regenerate.click(
        rollback_last,
        [idx, local_data, chat_state,
         global_context, res_temp, res_topk, res_rpen, res_mnts, res_sample, ctx_num_lconv],
        [context_inspector, chatbot, local_data, regenerate]
    ).then(
        None, local_data, None, 
        _js="(v)=>{ setStorage('local_data',v) }"  
    )
    
    # stop.click(
    #     lambda: gr.update(interactive=True), None, regenerate,
    #     cancels=[send_event, regen_event]
    # )

    for btn in channel_btns:
        btn.click(
            set_chatbot,
            [btn, local_data, chat_state],
            [chatbot, idx, example_block, regenerate]
        ).then(
            None, btn, None, 
            _js=UPDATE_LEFT_BTNS_STATE        
        )

    for btn in ex_btns:
        btn.click(
            set_example,
            [btn],
            [instruction_txtbox, example_block]  
        )

    clean.click(
        reset_chat,
        [idx, local_data, chat_state],
        [instruction_txtbox, chatbot, local_data, example_block, regenerate]
    ).then(
        None, local_data, None, 
        _js="(v)=>{ setStorage('local_data',v) }"
    )

    
    placeholder_txt1.change(
        inputs=[template_txt, placeholder_txt1, placeholder_txt2, placeholder_txt3],
        outputs=[template_md],
        show_progress=False,
        _js=UPDATE_PLACEHOLDERS,
        fn=None
    )

    placeholder_txt2.change(
        inputs=[template_txt, placeholder_txt1, placeholder_txt2, placeholder_txt3],
        outputs=[template_md],
        show_progress=False,
        _js=UPDATE_PLACEHOLDERS,
        fn=None
    )

    placeholder_txt3.change(
        inputs=[template_txt, placeholder_txt1, placeholder_txt2, placeholder_txt3],
        outputs=[template_md],
        show_progress=False,
        _js=UPDATE_PLACEHOLDERS,
        fn=None
    )

    placeholder_txt1.submit(
        inputs=[template_txt, placeholder_txt1, placeholder_txt2, placeholder_txt3],
        outputs=[instruction_txtbox, placeholder_txt1, placeholder_txt2, placeholder_txt3],
        fn=get_final_template
    )

    placeholder_txt2.submit(
        inputs=[template_txt, placeholder_txt1, placeholder_txt2, placeholder_txt3],
        outputs=[instruction_txtbox, placeholder_txt1, placeholder_txt2, placeholder_txt3],
        fn=get_final_template
    )

    placeholder_txt3.submit(
        inputs=[template_txt, placeholder_txt1, placeholder_txt2, placeholder_txt3],
        outputs=[instruction_txtbox, placeholder_txt1, placeholder_txt2, placeholder_txt3],
        fn=get_final_template
    )    

    demo.load(
        None,
        inputs=None,
        outputs=[chatbot, local_data],
        _js=GET_LOCAL_STORAGE,
    )     

demo.queue(concurrency_count=5, max_size=256).launch()