app.py

# pylint: skip-file

import subprocess
import json
import requests
import zlib
from PIL import Image

subprocess.run(
    f"pip install flash-attn --no-build-isolation",
    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
    shell=True,
)

import os
from threading import Thread
from typing import Iterator

import gradio as gr
import spaces
import torch
import logging
import wikipedia
import time
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    AutoProcessor,
    TextIteratorStreamer,
)
from transformers.dynamic_module_utils import get_imports
from bs4 import BeautifulSoup
from functools import lru_cache

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


MAX_MAX_NEW_TOKENS = 4096
DEFAULT_MAX_NEW_TOKENS = 1536
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "8192"))

DEFAULT_SYSTEM_PROMPT = """\
Below is an instruction that describes a task, Write a response that appropriately completes the request. You are intelligent AI, developed by Etherll and named Ghost 8B Beta Coder, often referred to as Ghost Beta. Your expertise lies in writing code and solving programming-related challenges. You are known for your accuracy, positivity, and dedication to helping users with their coding needs. Your strength is understanding technical requirements and providing insightful solutions based on the user’s preferences and knowledge. If you encounter a programming question beyond your expertise, be honest about it instead of guessing.

You enjoy using emojis to add a friendly touch to coding discussions, but keep it balanced to maintain a natural interaction. Engage in meaningful conversations, focusing on providing relevant and precise coding advice. Rely on the context, such as project timelines or code complexity, to offer responses that are practical and timely. Always prioritize solving the problem at hand with the information available, avoiding unnecessary inquiries.
"""

# DEFAULT_SYSTEM_PROMPT = """  # You are a helpful and intelligent AI, trained by Ghost X and named Ghost 8B Beta (often referred to as Ghost Beta).
# You're known for your honesty, spreading positivity, and always striving to assist users. Your expertise lies in understanding their needs and providing insightful suggestions, drawing upon your knowledge and interests.  If a query exceeds your understanding, you'll be upfront and state you're unsure, avoiding fabricated responses. You enjoy incorporating emojis to enhance interactions, but maintain a balanced approach for a natural flow. Let's engage in a meaningful conversation, keeping in mind the user's language.
# """

# DEFAULT_SYSTEM_PROMPT = """\
# You are a helpful and intelligent AI, trained by Ghost X and named Ghost 8B Beta (often referred to as 8B Beta).
# You're known for your honesty, spreading positivity, and always striving to assist users. Your expertise lies in understanding their needs and providing insightful suggestions, drawing upon your knowledge and interests.  If a query exceeds your understanding, you'll be upfront and state you're unsure, avoiding fabricated responses. You enjoy incorporating emojis to enhance interactions, but maintain a balanced approach for a natural flow. Let's engage in a meaningful conversation, keeping in mind the user's language.

# A guide to dealing with extremely complex questions or challenges. Follow these steps to solve them:
# 1. Deconstructing Complexity
# Imagine a puzzle with intricate pieces.  I'll present a challenging question. Your task:  Break down this question into smaller, distinct parts.  Label each part with a specific theme or aspect related to the problem.  This will help us understand the multifaceted nature of the query and prepare for a structured solution.
# 2. Reconstructing Insights
# Once we've successfully dissected the problem into manageable components,  assemble these parts like a puzzle.  Focus on identifying connections, potential overlaps, and key information from each theme.  The goal is to reconstruct a cohesive, well-rounded answer that addresses the original complexity of the question.
# """

HEAD = """
<script>
function schedule_updates() {
  const client_info_element = document.querySelector("#client_info textarea");
  client_info_element.value = "The current time is " + new Date().toLocaleString('en-US', {
    dateStyle: 'full',
    timeStyle: 'short',
  })
  client_info_element.dispatchEvent(new Event('input'));
}

function bootstrap() {
  setInterval(schedule_updates, 1000);
};

bootstrap();
</script>
"""

DESCRIPTION = """\
# Ghost 8B Beta Coder (by Etherll)

**Ghost 8B Beta Coder (by Etherll)** This version highlights the model's strengths in coding and problem-solving, while keeping the original performance comparisons intact. This version was built to support my friend [Etherll](https://huggingface.co/Etherll) in bringing the model to everyone to experience, a fine tuned from [Ghost 8B Beta](https://huggingface.co/ghost-x/ghost-8b-beta-1608).

Supported languages: 🇬🇧 English, 🇻🇳 Vietnamese, 🇰🇷 Korean, 🇪🇸 Spanish, 🇵🇹 Portuguese, 🇨🇳 Chinese, 🇫🇷 French, 🇮🇹 Italian, 🇩🇪 German, 🇯🇵 Japanese, 🇷🇺 Russian, 🇵🇱 Polish, 🇳🇱 Dutch, 🇮🇳 Hindi, 🇹🇷 Turkish, 🇮🇩 Indonesian.

Note: with the image will be used another model to explain rather than using directly the Ghost 8B Beta model.
"""


PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
    <h1 style="font-size: 26px; margin-bottom: 2px; opacity: 0.20;">👋 Welcome to the Ghost 8B Beta Playground! 🎉</h1>
    <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.10;">Ask me anything and let's have some fun! 🤔💡</p>
</div>
"""

LICENSE = """
<p/>

---
Ghost 8B Beta Coder (by Etherll) may give inaccurate information, including information about people, so please verify Ghost 8B Beta Coder's answers.
"""

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"


def workaround_fixed_get_imports(filename: str | os.PathLike) -> list[str]:
    """
    Workaround for fixed get_imports function.

    @args:
        filename (str | os.PathLike): The filename or path to the file.

    @returns:
        list[str]: The list of imports.

    @remarks:
        - This function is a workaround for the fixed get_imports function.
        - It checks if the filename ends with "/modeling_florence2.py".
        - If it doesn't, it calls the original get_imports function.
        - If it does, it calls the original get_imports function and removes the "flash_attn" import.

    @usage:
        ```python
        from unittest.mock import patch
        image_torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
        with patch(
            "transformers.dynamic_module_utils.get_imports", workaround_fixed_get_imports
        ):
        ```
    """

    if not str(filename).endswith("/modeling_florence2.py"):
        return get_imports(filename)
    imports = get_imports(filename)
    imports.remove("flash_attn")
    return imports


if torch.cuda.is_available():
    hf_serect = os.getenv("HF_TOKEN", None)
    attn_implementation = "flash_attention_2"

    chat_model_id = "Etherll/ghost-coder-8b-beta-1608"
    chat_device = torch.device("cuda")
    chat_model = AutoModelForCausalLM.from_pretrained(
        chat_model_id,
        device_map="auto",
        torch_dtype=torch.bfloat16,
        attn_implementation=attn_implementation,
        trust_remote_code=True,
        token=hf_serect,
    )
    chat_tokenizer = AutoTokenizer.from_pretrained(
        chat_model_id,
        trust_remote_code=True,
        token=hf_serect,
    )

    image_model_id = "microsoft/Florence-2-large"
    # image_device = "cuda" if torch.cuda.is_available() else "cpu"
    # image_torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
    image_device = "cpu"
    image_torch_dtype = torch.float32
    image_model = (
        AutoModelForCausalLM.from_pretrained(
            image_model_id,
            torch_dtype=image_torch_dtype,
            trust_remote_code=True,
            token=hf_serect,
        )
        .to(image_device)
        .eval()
    )
    image_processor = AutoProcessor.from_pretrained(
        image_model_id,
        trust_remote_code=True,
        token=hf_serect,
    )


waiting_tools_timeout = 5
supported_tools = json.dumps(
    [
        {
            "type": "function",
            "function": {
                "name": "search_on_internet",
                "description": "Use this tool to search for information on the internet to answer questions you are unsure about, don't know or need the latest information (e.g. news, reports, companies, people,...) to give the most accurate results. Note: can only be used or ignored, not asked again",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "keyword": {
                            "type": "string",
                            "description": "Search keywords, rephrase to optimize search results based on questions suitable to the specified search type.",
                            "required": True,
                        },
                        "type": {
                            "type": "string",
                            "description": "Search type, based on the question to determine whether to search for it in 'wikipedia' or 'google', prefer to use wikipedia for information about events, history and people.",
                            "enum": ["wikipedia", "google"],
                            "default": "google",
                            "required": True,
                        },
                        "language": {
                            "type": "string",
                            "description": "Search language, is the user language code with 2 letters, e.g: vi = vietnamese, en = english.",
                            "default": "en",
                            "required": True,
                        },
                    },
                },
            },
        }
    ],
    ensure_ascii=False,
)


@lru_cache(maxsize=128)
def extract_text_from_webpage(html_content):
    """
    Extracts visible text from an HTML webpage.

    @args:
        html_content (str): The HTML content of the webpage.

    @returns:
        str: The visible text extracted from the webpage.

    @remarks:
        - This function uses the BeautifulSoup library to parse the HTML content.
        - It removes certain tags (script, style, header, footer, nav, form, svg) from the parsed HTML.
        - The remaining visible text is then extracted using the `get_text` method of BeautifulSoup.
        - The extracted text is stripped of leading/trailing whitespace and separated by a single space.
    """

    soup = BeautifulSoup(html_content, "html.parser")
    for tag in soup(["script", "style", "header", "footer", "nav", "form", "svg"]):
        tag.extract()
    visible_text = soup.get_text(strip=True, separator=" ")
    return visible_text


def search_with_wikipedia(
    query: str,
    language: str = "en",
):
    """
    Search for a given query on Wikipedia and return the summary.

    @args:
        query (str): The search query.
        language (str, optional): The language code for the Wikipedia page. Defaults to "en".

    @returns:
        list: A list containing the summary of the Wikipedia page.

    @remarks:
        - This function uses the Wikipedia API to search for the given query.
        - The language parameter determines the language of the Wikipedia page to search.
        - If the search is successful, the function returns a list containing the summary of the page.
        - If an exception occurs during the search, an empty list is returned.
    """

    all_results = []
    try:
        wikipedia.set_lang(language)
        all_results.append(wikipedia.summary(query))
    except Exception as e:
        pass
    return all_results


def search_with_google(
    query: str,
    num_results: int = 3,
    timeout: int = 5,
    language: str = "en",
    ssl_verify: bool = None,
):
    """
    Searches Google for the given query and returns a list of search results.

    @args:
        query (str): The search query.
        num_results (int, optional): The number of search results to retrieve. Defaults to 3.
        timeout (int, optional): The timeout value for the HTTP requests. Defaults to 5.
        language (str, optional): The language for the search results. Defaults to "en".
        ssl_verify (bool, optional): Whether to verify SSL certificates. Defaults to None.

    @returns:
        list: A list of dictionaries containing the link and visible text of each search result.

    @remarks:
        - This function uses the requests library to send HTTP requests to Google.
        - It sets the User-Agent header to mimic a Firefox browser.
        - The search results are retrieved from the HTML response using BeautifulSoup.
        - Each search result is represented as a dictionary with "link" and "text" keys.
        - The "link" key contains the URL of the search result.
        - The "text" key contains the visible text extracted from the search result webpage.
        - If the visible text exceeds 4096 characters, it is truncated to that length.
        - If an error occurs while fetching or processing a search result, it is printed and ignored.
    """

    # Initialize an empty list to store the search results
    all_results = []

    # Define the maximum number of characters per page
    max_chars_per_page = 4096

    # Create a session object to send HTTP requests
    with requests.Session() as session:
        # Send a GET request to Google search with the specified query parameters
        resp = session.get(
            url="https://www.google.com/search",
            headers={
                "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0"
            },
            params={
                "q": query,
                "num": num_results,
                "udm": 14,
                "hl": language,
            },
            timeout=timeout,
            verify=ssl_verify,
        )

        # Raise an exception if the response status code is not successful
        resp.raise_for_status()

        # Parse the HTML response using BeautifulSoup
        soup = BeautifulSoup(resp.text, "html.parser")

        # Find all the result blocks in the HTML
        result_block = soup.find_all("div", attrs={"class": "g"})

        # Iterate over each result block
        for result in result_block:
            # Find the link element within the result block
            link = result.find("a", href=True)

            # If a link is found, extract the URL and process the webpage
            if link:
                link = link["href"]
                try:
                    # Send a GET request to the link URL
                    webpage = session.get(
                        link,
                        headers={
                            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0"
                        },
                    )

                    # Raise an exception if the response status code is not successful
                    webpage.raise_for_status()

                    # Extract the visible text from the webpage
                    visible_text = extract_text_from_webpage(webpage.text)

                    # Truncate the visible text if it exceeds the maximum number of characters per page
                    if len(visible_text) > max_chars_per_page:
                        visible_text = visible_text[:max_chars_per_page]

                    # Append the link and visible text to the search results list
                    all_results.append({"link": link, "text": visible_text})
                except requests.exceptions.RequestException as e:
                    # Print an error message if there is an error fetching or processing the link
                    print(f"Error fetching or processing {link}: {e}")
                    pass
            else:
                pass

    # Return the search results
    return all_results


@lru_cache(maxsize=128)
def extract_text_from_image(file: str) -> str:
    """
    Extracts text from an image file.

    @args:
        file (str): The path or URL of the image file.

    @returns:
        str: The extracted text from the image.

    @remarks:
        - This function uses an LRU cache to store previously processed images for faster retrieval.
        - The image file can be either a local file path or a URL.
        - The function opens the image file using the PIL library.
        - The function processes the image using an image processor.
        - The processed image is then passed to a text generation model to generate text.
        - The generated text is post-processed to obtain the final extracted text.
    """
    # Define the task and load the image
    task = "<MORE_DETAILED_CAPTION>"
    image = Image.open(
        requests.get(file, stream=True).raw
        if file.startswith("http")
        else open(file, "rb")
    )

    if image.mode != "RGB":
        image = image.convert("RGB")

    # Preprocess the image using the image processor
    inputs = image_processor(text=task, images=image, return_tensors="pt").to(
        "cpu", image_torch_dtype
    )

    # Generate text based on the input image
    generated_ids = image_model.generate(
        input_ids=inputs["input_ids"],
        pixel_values=inputs["pixel_values"],
        max_new_tokens=1024,
        num_beams=3,
        do_sample=False,
    )

    # Decode the generated text and post-process the answer
    generated_text = image_processor.batch_decode(
        generated_ids, skip_special_tokens=False
    )[0]
    parsed_answer = image_processor.post_process_generation(
        generated_text,
        task=task,
        image_size=(image.width, image.height),
    )

    # Return the parsed answer for the specified task
    return parsed_answer[task]


@spaces.GPU(duration=90)
def generate_chat(
    uuid: str,
    message: dict,
    chat_history: list[tuple[str, str]],
    allow_used_tools: bool = True,
    system_prompt: str = "",
    max_new_tokens: int = 1536,
    temperature: float = 0.4,
    top_p: float = 0.95,
    top_k: int = 50,
    repetition_penalty: float = 1.0,
    client_info: str = None,
) -> Iterator[str]:
    # Build the input_ids for the chat conversation
    def build_input_ids(
        system_prompt: str = "",
        apply_tools: bool = None,
        references=None,
    ):
        conversation = []

        # Add the system prompt to the conversation
        if system_prompt:
            if system_prompt.strip() == DEFAULT_SYSTEM_PROMPT.strip():
                system_prompt = system_prompt.strip() + "\n\n" + client_info + "\n"
            conversation.append({"role": "system", "content": system_prompt})

        # Add the tools role to the conversation if apply_tools is True
        if apply_tools is True:
            conversation.append({"role": "tools", "content": supported_tools})

        # Add the references role to the conversation
        # if references is None:
        #     references = [client_info]
        # else:
        #     references.insert(0, client_info)

        if (
            references is not None
            and isinstance(references, list)
            and len(references) > 0
        ):
            formatted_references = f"Analyze the provided references, extract relevant information to provide accurate and objective feedback. This reference information may include: conversation context, assistant or user memories, reasoning guides, problem-solving suggestions, assistant rules, etc.\nIf the reference is not relevant, ignore it. Try to have a balanced approach, avoiding over-reliance on the documentation."
            formatted_references += "\n\n" + json.dumps(
                references, indent=2, ensure_ascii=False
            )
            conversation.append(
                {
                    "role": "refs",
                    "content": formatted_references,
                }
            )

        # Add the chat history to the conversation
        for user, assistant in chat_history:
            conversation.extend(
                [
                    {"role": "user", "content": user},
                    {"role": "assistant", "content": assistant},
                ]
            )

        # Add the user message with image attachments to the conversation
        conversation.append(
            {
                "role": "user",
                "content": (
                    f"{' & '.join(message['attachments'])}\n\n{message['text']}"
                    if "attachments" in message and len(message["attachments"]) > 0
                    else f"{message['text']}"
                ),
            }
        )

        logger.info(f"UUID: {uuid} - Conversation: {conversation}")

        # Apply the chat template to convert the conversation into input_ids
        input_ids = chat_tokenizer.apply_chat_template(
            conversation, add_generation_prompt=True, return_tensors="pt"
        )
        input_ids = input_ids.to(chat_model.device)

        # Trim the input_ids if it exceeds the maximum token length
        if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
            input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
            gr.Warning(
                f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens."
            )
        return input_ids

    # Generate chat responses based on the input_ids
    def generate_chat_responses(
        previous_response: str = None,
    ):
        document_references = []

        # Check if the previous response contains scheduled tool runs
        if previous_response is not None:
            scheduled_tools_runs = None
            try:
                scheduled_tools_runs = json.loads(previous_response)
                if scheduled_tools_runs["type"] == "function" and scheduled_tools_runs[
                    "name"
                ] in ["search_on_internet"]:
                    pass
                else:
                    scheduled_tools_runs = None
            except Exception as e:
                print(e)
                pass

            # If scheduled tool runs exist, perform the corresponding searches
            if (
                scheduled_tools_runs is not None
                and scheduled_tools_runs["name"] == "search_on_internet"
            ):
                keyword = scheduled_tools_runs["arguments"]["keyword"]
                search_type = scheduled_tools_runs["arguments"]["type"]
                language = scheduled_tools_runs["arguments"]["language"]

                # Search on Wikipedia if the search type is "wikipedia"
                if search_type == "wikipedia":
                    gr.Info("Searching for information on the Wikipedia.")
                    document_references.extend(
                        search_with_wikipedia(query=keyword, language=language)
                    )

                # Search on Google
                gr.Info("Searching for information on the Google.")
                document_references.extend(
                    search_with_google(
                        query=keyword,
                        language=language,
                        num_results=3,
                    )
                )
                print("document_references:", document_references)

        # Determine if tools should be applied based on the allow_used_tools flag
        apply_tools = (
            True if allow_used_tools is True and previous_response is None else False
        )

        # Build the input_ids for the chat conversation
        input_ids = build_input_ids(
            system_prompt=system_prompt,
            apply_tools=apply_tools,
            references=document_references,
        )

        # Create a TextIteratorStreamer to generate chat responses
        streamer = TextIteratorStreamer(
            chat_tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
        )

        # Set the generation parameters
        generate_kwargs = dict(
            input_ids=input_ids,
            streamer=streamer,
            max_new_tokens=max_new_tokens,
            do_sample=True,
            repetition_penalty=repetition_penalty,
        )
        if temperature == 0:
            generate_kwargs["do_sample"] = False
        else:
            generate_kwargs["temperature"] = temperature
            generate_kwargs["top_p"] = top_p
            generate_kwargs["top_k"] = top_k

        # Start the generation process in a separate thread
        t = Thread(target=chat_model.generate, kwargs=generate_kwargs)
        t.start()

        logger.info(
            f"UUID: {uuid} - Is apply tools: {apply_tools} - Is apply documents: {len(document_references) > 0} - Is previous response: {previous_response is not None} - Start generating chat responses"
        )

        state = {
            "mark": None,
            "respond": False,
        }
        outputs = []
        for text in streamer:
            if state["mark"] is None:
                state["mark"] = time.time()
            outputs.append(text)
            if (
                apply_tools is False
                or state["mark"] + waiting_tools_timeout < time.time()
            ):
                state["respond"] = True
                yield "".join(outputs)

        # If tools are applied and no response is generated within the timeout, continue generating chat responses
        if (
            apply_tools is True
            and state["respond"] is False
            and state["mark"] + waiting_tools_timeout > time.time()
        ):
            previous_response = "".join(outputs)
            yield from generate_chat_responses(previous_response=previous_response)

    # Yield the generated chat responses
    yield from generate_chat_responses(previous_response=None)


def generate(
    message: dict,
    chat_history: list[tuple[str, str]],
    allow_used_tools: bool = True,
    system_prompt: str = "",
    max_new_tokens: int = 1536,
    temperature: float = 0.4,
    top_p: float = 0.95,
    top_k: int = 50,
    repetition_penalty: float = 1.0,
    client_info: str = None,
) -> Iterator[str]:
    # Generate a unique identifier using the The current time is now
    uuid = zlib.crc32(str.encode(str(time.time())))
    logger.info(f"UUID: {uuid} - Starting image text extraction process")

    # Limit the number of files to process to 2
    if len(message["files"]) > 2:
        gr.Warning("Only the first 2 images will be processed.")

    message["files"] = message["files"][:2]

    # Extract text from each image file and replace the file path with an attachment tag containing the extracted text
    message["attachments"] = handle_file_extraction(
        files=list(message["files"]), uuid=uuid
    )
    logger.info(f"UUID: {uuid} - Image text extraction process completed")

    logger.info(f"UUID: {uuid} - Previous chat history: {chat_history}")
    for idx, chat_pair in enumerate(chat_history):
        user_message, assistant_message = chat_pair
        if not isinstance(user_message, str) and assistant_message is None:
            text_descriptions = handle_file_extraction(
                files=list(user_message), uuid=uuid
            )
            chat_input = (
                f"{' & '.join(text_descriptions)}\n\n{chat_history[idx + 1][0]}"
            )
            chat_history[idx + 1][0] = chat_input
            chat_history[idx] = [None, None]
            logger.info(
                f"UUID: {uuid} - Updated chat history: {chat_history} - Updated chat input: {chat_input}"
            )

    chat_history = list(
        filter(lambda x: x[0] is not None and x[1] is not None, chat_history)
    )
    logger.info(f"UUID: {uuid} - Filtered chat history: {chat_history}")

    yield from generate_chat(
        uuid=uuid,
        message=message,
        chat_history=chat_history,
        allow_used_tools=allow_used_tools,
        system_prompt=system_prompt,
        max_new_tokens=max_new_tokens,
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        repetition_penalty=repetition_penalty,
        client_info=client_info,
    )


def handle_file_extraction(files: list[str], uuid: str):
    """
    Extracts text from images in the given message's files and returns a list of attachments.

    @args:
        message (dict): The message containing files to extract text from.
        uuid (str): The UUID associated with the extraction process.

    @returns:
        list: A list of attachments, each represented as a string.

    @memarks:
        - This function iterates over the files in the message and extracts text from each image file.
        - The extracted text is logged along with the UUID and file information.
        - The extracted text is then added to the attachments list as a string representation of an attachment.
        - The attachments list is returned at the end of the function.
    """

    attachments = []
    for idx, file_to_extract in enumerate(files):
        extracted_text = extract_text_from_image(file=file_to_extract)
        logger.info(
            f"UUID: {uuid} - File: {file_to_extract} - Extracted text: {extracted_text}"
        )
        attachments.append(
            f'<attachment index="{idx}" type="image" description="{extracted_text}" />'
        )
    return attachments


chatbot = gr.Chatbot(
    height=500,
    placeholder=PLACEHOLDER,
    label="Ghost 8B Beta Coder (by Etherll)",
    show_copy_button=True,
)

chat_interface = gr.ChatInterface(
    fn=generate,
    chatbot=chatbot,
    fill_height=True,
    multimodal=True,
    textbox=gr.MultimodalTextbox(
        file_types=["image"],
        placeholder="Type a message...",
    ),
    additional_inputs=[
        gr.Checkbox(
            label="Allow used tools (available: search on internet)",
            value=False,
        ),
        gr.Textbox(label="System prompt", lines=6, value=DEFAULT_SYSTEM_PROMPT),
        gr.Slider(
            label="Max new tokens",
            minimum=1,
            maximum=MAX_MAX_NEW_TOKENS,
            step=1,
            value=DEFAULT_MAX_NEW_TOKENS,
        ),
        gr.Slider(
            label="Temperature",
            minimum=0.0,
            maximum=2.0,
            step=0.1,
            value=0.4,
        ),
        gr.Slider(
            label="Top-p (nucleus sampling)",
            minimum=0.05,
            maximum=1.0,
            step=0.05,
            value=0.95,
        ),
        gr.Slider(
            label="Top-k",
            minimum=1,
            maximum=100,
            step=1,
            value=50,
        ),
        gr.Slider(
            label="Repetition penalty",
            minimum=1.0,
            maximum=2.0,
            step=0.05,
            value=1.0,
        ),
        gr.Textbox(
            elem_id="client_info",
            label="Client info",
            lines=1,
            value="The current time is {}".format(
                time.strftime("%A, %D %B %Y %H:%M:%S")
            ),
            visible=False,
        ),
    ],
    additional_inputs_accordion=gr.Accordion(label="Additional Inputs", open=True),
    stop_btn="Stop",
    cache_examples=False,
    examples=[],
    examples_per_page=10,
    concurrency_limit=100,
)

with gr.Blocks(fill_height=True, css="style.css", head=HEAD) as demo:
    gr.Markdown(DESCRIPTION)
    chat_interface.render()
    gr.Markdown(LICENSE)

if __name__ == "__main__":
    demo.queue().launch(share=True)