app.py

import json
import os
import shutil
import requests
import warnings

import gradio as gr
from huggingface_hub import Repository
from text_generation import Client

from share_btn import community_icon_html, loading_icon_html, share_js, share_btn_css

HF_TOKEN = os.environ.get("HF_TOKEN", None)

API_URL_G = "https://api-inference.huggingface.co/models/ArmelR/starcoder-gradio-v0"
API_URL_S = "https://api-inference.huggingface.co/models/HuggingFaceH4/starcoderbase-finetuned-oasst1"

with open("./HHH_prompt_short.txt", "r") as f:
    HHH_PROMPT = f.read() + "\n\n"

with open("./TA_prompt_v0.txt", "r") as f:
    TA_PROMPT = f.read()

NO_PROMPT = ""

FIM_PREFIX = "<fim_prefix>"
FIM_MIDDLE = "<fim_middle>"
FIM_SUFFIX = "<fim_suffix>"

FIM_INDICATOR = "<FILL_HERE>"

FORMATS = """
# Chat mode
Chat mode prepends the custom [TA prompt](https://huggingface.co/spaces/bigcode/chat-playground/blob/main/TA_prompt_v0.txt) or the [HHH prompt](https://gist.github.com/jareddk/2509330f8ef3d787fc5aaac67aab5f11#file-hhh_prompt-txt) from Anthropic to the request which conditions the model to serve as an assistant.

⚠️ **Intended Use**: this app and its [supporting model](https://huggingface.co/bigcode) are provided for demonstration purposes; not to serve as replacement for human expertise. For more details on the model's limitations in terms of factuality and biases, see the [model card.](hf.co/bigcode)

"""

theme = gr.themes.Monochrome(
    primary_hue="indigo",
    secondary_hue="blue",
    neutral_hue="slate",
    radius_size=gr.themes.sizes.radius_sm,
    font=[
        gr.themes.GoogleFont("Open Sans"),
        "ui-sans-serif",
        "system-ui",
        "sans-serif",
    ],
)

client_g = Client(
    API_URL_G, headers={"Authorization": f"Bearer {HF_TOKEN}"},
)

client_s = Client(
    API_URL_S, headers={"Authorization": f"Bearer {HF_TOKEN}"},
)

def generate(
    prompt,
    temperature=0.9,
    max_new_tokens=256,
    top_p=0.95,
    repetition_penalty=1.0,
    chat_mode="TA prompt",
    version="StarCoder-gradio",
):

    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)
    fim_mode = False

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        truncate=7500,
        do_sample=True,
        seed=42,
        stop_sequences=["\nHuman", "\n-----", "Question:", "Answer:"],
    )
    
    if chat_mode == "HHH prompt":
        base_prompt = HHH_PROMPT
    elif chat_mode == "TA prompt":
        base_prompt = TA_PROMPT
    else :
        base_prompt = NO_PROMPT


    if version == "StarCoder-gradio" :
        chat_prompt = prompt + "\n\nAnswer:"
        prompt = base_prompt + chat_prompt
        stream = client_g.generate_stream(prompt, **generate_kwargs)
    elif version == "StarChat-alpha" :
        chat_prompt = prompt + "\n\nAssistant:"
        prompt = base_prompt + chat_prompt
        stream = client_s.generate_stream(prompt, **generate_kwargs)
    else :
        ValueError("Unsupported version of the Coding assistant")
        
    output = ""
    previous_token = ""
    
    for response in stream:
        if (
            (response.token.text in ["Human", "-----", "Question:"]
            and previous_token in ["\n", "-----"])
            or response.token.text in ["<|endoftext|>", "<|end|>", "Answer:"]
        ):
            return output.strip()
        else:
            output += response.token.text
        previous_token = response.token.text

    return output.strip()


# chatbot mode
def user(user_message, history):
    return "", history + [[user_message, None]]


def bot(
    history,
    temperature=0.9,
    max_new_tokens=256,
    top_p=0.95,
    repetition_penalty=1.0,
    chat_mode=None,
    version="StarChat", 
):
    # concat history of prompts with answers expect for last empty answer only add prompt
    if version == "StarCoder-gradio" :
        prompt = "\n".join(
            [f"Question: {prompt}\n\nAnswer: {answer}" for prompt, answer in history[:-1]] + [f"\nQuestion: {history[-1][0]}"]
        )
    else :
        prompt = "\n".join(
            [f"Human: {prompt}\n\nAssistant: {answer}" for prompt, answer in history[:-1]] + [f"\nHuman: {history[-1][0]}"]
        )
    
    bot_message = generate(
        prompt,
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        chat_mode=chat_mode,
        version=version
        
        
    )
    history[-1][1] = bot_message
    return history


examples = [
    "def print_hello_world():",
    "def fibonacci(n):",
    "class TransformerDecoder(nn.Module):",
    "class ComplexNumbers:",
    "How to install gradio"
]


def process_example(args):
    for x in generate(args):
        pass
    return x


css = ".generating {visibility: hidden}" + share_btn_css

with gr.Blocks(theme=theme, analytics_enabled=False, css=css) as demo:
    with gr.Column():
        gr.Markdown(
            """\
#Gradio Assistant powered by ‍💫 StarCoder
_Note:_ this is an internal chat playground - **please do not share**. The deployment can also change and thus the space not work as we continue development.\
"""
        )
        with gr.Row():
            column_1, column_2 = gr.Column(scale=3), gr.Column(scale=1)
            with column_2:
                chat_mode = gr.Dropdown(
                    ["NO prompt","TA prompt", "HHH prompt"],
                    value="NO prompt",
                    label="Chat mode",
                    info="Use Anthropic's HHH prompt or our custom tech prompt to turn the model into an assistant.",
                )
                temperature = gr.Slider(
                    label="Temperature",
                    value=0.2,
                    minimum=0.0,
                    maximum=2.0,
                    step=0.1,
                    interactive=True,
                    info="Higher values produce more diverse outputs",
                )
                max_new_tokens = gr.Slider(
                    label="Max new tokens",
                    value=512,
                    minimum=0,
                    maximum=8192,
                    step=64,
                    interactive=True,
                    info="The maximum numbers of new tokens",
                )
                top_p = gr.Slider(
                    label="Top-p (nucleus sampling)",
                    value=0.90,
                    minimum=0.0,
                    maximum=1,
                    step=0.05,
                    interactive=True,
                    info="Higher values sample more low-probability tokens",
                )
                repetition_penalty = gr.Slider(
                    label="Repetition penalty",
                    value=1.2,
                    minimum=1.0,
                    maximum=2.0,
                    step=0.05,
                    interactive=True,
                    info="Penalize repeated tokens",
                )
                version = gr.Dropdown(
                    ["StarCoder-gradio", "StarChat-alpha"],
                    value="StarCoder-gradio",
                    label="Version",
                    info="",
                )
            with column_1:
                # output = gr.Code(elem_id="q-output")
                # add visibl=False and update if chat_mode True
                chatbot = gr.Chatbot()
                instruction = gr.Textbox(
                    placeholder="Enter your prompt here",
                    label="Prompt",
                    elem_id="q-input",
                )
                with gr.Row():
                    with gr.Column():
                        clear = gr.Button("Clear Chat")
                    with gr.Column():
                        submit = gr.Button("Generate", variant="primary")
                with gr.Group(elem_id="share-btn-container"):
                    community_icon = gr.HTML(community_icon_html, visible=True)
                    loading_icon = gr.HTML(loading_icon_html, visible=True)
                    share_button = gr.Button(
                        "Share to community", elem_id="share-btn", visible=True
                    )
                # examples of non-chat mode
                #gr.Examples(
                #    examples=examples,
                #    inputs=[instruction],
                #    cache_examples=False,
                #    fn=process_example,
                #    outputs=[output],
                # )
                gr.Markdown(FORMATS)


        instruction.submit(
            user, [instruction, chatbot], [instruction, chatbot], queue=False
        ).then(
            bot,
            [chatbot, temperature, max_new_tokens, top_p, repetition_penalty, chat_mode, version],
            chatbot,
        )

        submit.click(
            user, [instruction, chatbot], [instruction, chatbot], queue=False
        ).then(
            bot,
            [chatbot, temperature, max_new_tokens, top_p, repetition_penalty, chat_mode, version],
            chatbot,
        )
        clear.click(lambda: None, None, chatbot, queue=False)
    
    share_button.click(None, [], [], _js=share_js)
demo.queue(concurrency_count=16).launch(debug=True)