app.py

import os
import logging
import gradio as gr
import json
from typing import List
from datetime import datetime, timezone
from pydantic import BaseModel, Field
from trafilatura import fetch_url, extract
from langchain_community.llms import HuggingFaceHub

from llama_cpp_agent import MessagesFormatterType
from llama_cpp_agent.chat_history import BasicChatHistory
from llama_cpp_agent.chat_history.messages import Roles
from llama_cpp_agent.llm_output_settings import (
    LlmStructuredOutputSettings,
    LlmStructuredOutputType,
)
from llama_cpp_agent.tools import WebSearchTool
from llama_cpp_agent.prompt_templates import web_search_system_prompt, research_system_prompt
from langchain_community.llms import HuggingFaceHub
from llama_cpp_agent.llm_output_settings import LlmStructuredOutputSettings, LlmStructuredOutputType
from pydantic import BaseModel, Field
from llama_cpp_agent.llm_output_settings import LlmStructuredOutputType
from llama_cpp import Llama
from llama_cpp_agent import LlamaCppPythonProvider, LlamaCppAgent
from llama_cpp_agent.chat_history import BasicChatHistory
from llama_cpp_agent.chat_history.messages import Roles
from llama_cpp_agent.llm_output_settings import LlmStructuredOutputSettings, LlmStructuredOutputType
from llama_cpp_agent.tools import WebSearchTool
from llama_cpp_agent.prompt_templates import web_search_system_prompt, research_system_prompt
from pydantic import BaseModel, Field
from typing import List

print("Available LlmStructuredOutputType options:")
for option in LlmStructuredOutputType:
    print(option)

# UI related imports and definitions
css = """
.message-row {
    justify-content: space-evenly !important;
}
.message-bubble-border {
    border-radius: 6px !important;
}
.message-buttons-bot, .message-buttons-user {
    right: 10px !important;
    left: auto !important;
    bottom: 2px !important;
}
.dark.message-bubble-border {
    border-color: #1b0f0f !important;
}
.dark.user {
    background: #140b0b !important;
}
.dark.assistant.dark, .dark.pending.dark {
    background: #0c0505 !important;
}
"""

PLACEHOLDER = """
<div class="message-bubble-border" style="display:flex; max-width: 600px; border-width: 1px; border-color: #e5e7eb; border-radius: 8px; box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); backdrop-filter: blur(10px);">
    <figure style="margin: 0;">
        <img src="https://huggingface.co/spaces/poscye/ddg-web-search-chat/resolve/main/logo.jpg" alt="Logo" style="width: 100%; height: 100%; border-radius: 8px;">
    </figure>
    <div style="padding: .5rem 1.5rem;">
        <h2 style="text-align: left; font-size: 1.5rem; font-weight: 700; margin-bottom: 0.5rem;">llama-cpp-agent</h2>
        <p style="text-align: left; font-size: 16px; line-height: 1.5; margin-bottom: 15px;">DDG Agent allows users to interact with it using natural language, making it easier for them to find the information they need. Offers a convenient and secure way for users to access web-based information.</p>
        <div style="display: flex; justify-content: space-between; align-items: center;">
            <div style="display: flex; flex-flow: column; justify-content: space-between;">
                <span style="display: inline-flex; align-items: center; border-radius: 0.375rem; background-color: rgba(229, 70, 77, 0.1); padding: 0.1rem 0.75rem; font-size: 0.75rem; font-weight: 500; color: #f88181; margin-bottom: 2.5px;">
                    Mistral 7B Instruct v0.3
                </span>
                <span style="display: inline-flex; align-items: center; border-radius: 0.375rem; background-color: rgba(229, 70, 77, 0.1); padding: 0.1rem 0.75rem; font-size: 0.75rem; font-weight: 500; color: #f88181; margin-bottom: 2.5px;">
                    Mixtral 8x7B Instruct v0.1
                </span>
                <span style="display: inline-flex; align-items: center; border-radius: 0.375rem; background-color: rgba(79, 70, 229, 0.1); padding: 0.1rem 0.75rem; font-size: 0.75rem; font-weight: 500; color: #60a5fa; margin-top: 2.5px;">
                    Meta Llama 3 8B Instruct
                </span>
            </div>
            <div style="display: flex; justify-content: flex-end; align-items: center;">
                <a href="https://discord.gg/fgr5RycPFP" target="_blank" rel="noreferrer" style="padding: .5rem;">
                    <svg width="24" height="24" fill="currentColor" xmlns="http://www.w3.org/2000/svg" viewBox="0 5 30.67 23.25">
                        <title>Discord</title>
                        <path d="M26.0015 6.9529C24.0021 6.03845 21.8787 5.37198 19.6623 5C19.3833 5.48048 19.0733 6.13144 18.8563 6.64292C16.4989 6.30193 14.1585 6.30193 11.8336 6.64292C11.6166 6.13144 11.2911 5.48048 11.0276 5C8.79575 5.37198 6.67235 6.03845 4.6869 6.9529C0.672601 12.8736 -0.41235 18.6548 0.130124 24.3585C2.79599 26.2959 5.36889 27.4739 7.89682 28.2489C8.51679 27.4119 9.07477 26.5129 9.55525 25.5675C8.64079 25.2265 7.77283 24.808 6.93587 24.312C7.15286 24.1571 7.36986 23.9866 7.57135 23.8161C12.6241 26.1255 18.0969 26.1255 23.0876 23.8161C23.3046 23.9866 23.5061 24.1571 23.7231 24.312C22.8861 24.808 22.0182 25.2265 21.1037 25.5675C21.5842 26.5129 22.1422 27.4119 22.7621 28.2489C25.2885 27.4739 27.8769 26.2959 30.5288 24.3585C31.1952 17.7559 29.4733 12.0212 26.0015 6.9529ZM10.2527 20.8402C8.73376 20.8402 7.49382 19.4608 7.49382 17.7714C7.49382 16.082 8.70276 14.7025 10.2527 14.7025C11.7871 14.7025 13.0425 16.082 13.0115 17.7714C13.0115 19.4608 11.7871 20.8402 10.2527 20.8402ZM20.4373 20.8402C18.9183 20.8402 17.6768 19.4608 17.6768 17.7714C17.6768 16.082 18.8873 14.7025 20.4373 14.7025C21.9717 14.7025 23.2271 16.082 23.1961 17.7714C23.1961 19.4608 21.9872 20.8402 20.4373 20.8402Z"></path>
                    </svg>
                </a>
                <a href="https://github.com/Maximilian-Winter/llama-cpp-agent" target="_blank" rel="noreferrer" style="padding: .5rem;">
                    <svg width="24" height="24" fill="currentColor" viewBox="3 3 18 18">
                        <title>GitHub</title>
                        <path d="M12 3C7.0275 3 3 7.12937 3 12.2276C3 16.3109 5.57625 19.7597 9.15374 20.9824C9.60374 21.0631 9.77249 20.7863 9.77249 20.5441C9.77249 20.3249 9.76125 19.5982 9.76125 18.8254C7.5 19.2522 6.915 18.2602 6.735 17.7412C6.63375 17.4759 6.19499 16.6569 5.8125 16.4378C5.4975 16.2647 5.0475 15.838 5.80124 15.8264C6.51 15.8149 7.01625 16.4954 7.18499 16.7723C7.99499 18.1679 9.28875 17.7758 9.80625 17.5335C9.885 16.9337 10.1212 16.53 10.38 16.2993C8.3775 16.0687 6.285 15.2728 6.285 11.7432C6.285 10.7397 6.63375 9.9092 7.20749 9.26326C7.1175 9.03257 6.8025 8.08674 7.2975 6.81794C7.2975 6.81794 8.05125 6.57571 9.77249 7.76377C10.4925 7.55615 11.2575 7.45234 12.0225 7.45234C12.7875 7.45234 13.5525 7.55615 14.2725 7.76377C15.9937 6.56418 16.7475 6.81794 16.7475 6.81794C17.2424 8.08674 16.9275 9.03257 16.8375 9.26326C17.4113 9.9092 17.76 10.7281 17.76 11.7432C17.76 15.2843 15.6563 16.0687 13.6537 16.2993C13.98 16.5877 14.2613 17.1414 14.2613 18.0065C14.2613 19.2407 14.25 20.2326 14.25 20.5441C14.25 20.7863 14.4188 21.0746 14.8688 20.9824C16.6554 20.364 18.2079 19.1866 19.3078 17.6162C20.4077 16.0457 20.9995 14.1611 21 12.2276C21 7.12937 16.9725 3 12 3Z"></path>
                    </svg>
                </a>
            </div>
        </div>
    </div>
</div>
"""

# Global variables
huggingface_token = os.environ.get("HUGGINGFACE_TOKEN")

# Example queries
examples = [
    ["latest news about Yann LeCun"],
    ["Latest news site:github.blog"],
    ["Where I can find best hotel in Galapagos, Ecuador intitle:hotel"],
    ["filetype:pdf intitle:python"]
]

class CustomLLMSettings(BaseModel):
    structured_output: LlmStructuredOutputSettings
    temperature: float = Field(default=0.7)
    top_p: float = Field(default=0.95)
    repetition_penalty: float = Field(default=1.1)
    top_k: int = Field(default=50)
    max_tokens: int = Field(default=1000)
    stop: list[str] = Field(default_factory=list)
    echo: bool = Field(default=False)
    stream: bool = Field(default=False)
    logprobs: int = Field(default=None)
    presence_penalty: float = Field(default=0.0)
    frequency_penalty: float = Field(default=0.0)
    best_of: int = Field(default=1)
    logit_bias: dict = Field(default_factory=dict)
    max_tokens_per_summary: int = Field(default=2048)

class HuggingFaceHubWrapper:
    def __init__(self, repo_id, model_kwargs, huggingfacehub_api_token):
        self.model = HuggingFaceHub(
            repo_id=repo_id,
            model_kwargs=model_kwargs,
            huggingfacehub_api_token=huggingfacehub_api_token
        )
        self.temperature = model_kwargs.get('temperature', 0.7)
        self.top_p = model_kwargs.get('top_p', 0.95)
        self.repetition_penalty = model_kwargs.get('repetition_penalty', 1.1)
        self.top_k = model_kwargs.get('top_k', 50)
        self.max_tokens = model_kwargs.get('max_length', 1000)
        self.max_tokens_per_summary = model_kwargs.get('max_tokens_per_summary', 2048)

    def get_provider_default_settings(self):
        return CustomLLMSettings(
            structured_output=LlmStructuredOutputSettings(
                output_type=LlmStructuredOutputType.no_structured_output,
                include_system_prompt=False,
                include_user_prompt=False,
                include_assistant_prompt=False,
            ),
            temperature=self.temperature,
            top_p=self.top_p,
            repetition_penalty=self.repetition_penalty,
            top_k=self.top_k,
            max_tokens=self.max_tokens,
            max_tokens_per_summary=self.max_tokens_per_summary
        )

    def get_provider_identifier(self):
        return "HuggingFaceHub"

    def __call__(self, *args, **kwargs):
        return self.model(*args, **kwargs)

    def get_num_tokens(self, text):
        # This is a placeholder. You might need to implement a proper token counting method
        return len(text.split())

    def get_max_tokens(self):
        # This is a placeholder. Return the actual max tokens for your model
        return 2048

# Utility functions
def get_server_time():
    utc_time = datetime.now(timezone.utc)
    return utc_time.strftime("%Y-%m-%d %H:%M:%S")

def get_website_content_from_url(url: str) -> str:
    try:
        downloaded = fetch_url(url)
        result = extract(downloaded, include_formatting=True, include_links=True, output_format='json', url=url)
        if result:
            result = json.loads(result)
            return f'=========== Website Title: {result["title"]} ===========\n\n=========== Website URL: {url} ===========\n\n=========== Website Content ===========\n\n{result["raw_text"]}\n\n=========== Website Content End ===========\n\n'
        else:
            return ""
    except Exception as e:
        return f"An error occurred: {str(e)}"

class CitingSources(BaseModel):
    sources: List[str] = Field(
        ...,
        description="List of sources to cite. Should be an URL of the source. E.g. GitHub URL, Blogpost URL or Newsletter URL."
    )

# Model function
def get_model(temperature, top_p, repetition_penalty, top_k=50, max_tokens=1000, max_tokens_per_summary=2048):
    return HuggingFaceHubWrapper(
        repo_id="mistralai/Mistral-7B-Instruct-v0.3",
        model_kwargs={
            "temperature": temperature,
            "top_p": top_p,
            "repetition_penalty": repetition_penalty,
            "top_k": top_k,
            "max_length": max_tokens,
            "max_tokens_per_summary": max_tokens_per_summary
        },
        huggingfacehub_api_token=huggingface_token
    )

def get_messages_formatter_type(model_name):
    model_name = model_name.lower()
    if any(keyword in model_name for keyword in ["meta", "aya"]):
        return MessagesFormatterType.LLAMA_3
    elif any(keyword in model_name for keyword in ["mistral", "mixtral"]):
        return MessagesFormatterType.MISTRAL
    elif any(keyword in model_name for keyword in ["einstein", "dolphin"]):
        return MessagesFormatterType.CHATML
    elif "phi" in model_name:
        return MessagesFormatterType.PHI_3
    else:
        return MessagesFormatterType.CHATML

# Main response function
def respond(
    message,
    history: list[tuple[str, str]],
    model,
    system_message,
    max_tokens,
    temperature,
    top_p,
    top_k,
    repeat_penalty,
):
    global llm
    global llm_model

    chat_template = get_messages_formatter_type(model)

    if llm is None or llm_model != model:
        llm = Llama(
            model_path=f"models/{model}",
            flash_attn=True,
            n_gpu_layers=81,
            n_batch=1024,
            n_ctx=get_context_by_model(model),
        )
        llm_model = model

    provider = LlamaCppPythonProvider(llm)
    logging.info(f"Loaded chat examples: {chat_template}")

    search_tool = WebSearchTool(
        llm_provider=provider,
        message_formatter_type=chat_template,
        max_tokens_search_results=12000,
        max_tokens_per_summary=2048,
    )

    web_search_agent = LlamaCppAgent(
        provider,
        system_prompt=web_search_system_prompt,
        predefined_messages_formatter_type=chat_template,
        debug_output=True,
    )

    answer_agent = LlamaCppAgent(
        provider,
        system_prompt=research_system_prompt,
        predefined_messages_formatter_type=chat_template,
        debug_output=True,
    )

    settings = provider.get_provider_default_settings()
    settings.stream = False
    settings.temperature = temperature
    settings.top_k = top_k
    settings.top_p = top_p
    settings.max_tokens = max_tokens
    settings.repeat_penalty = repeat_penalty

    output_settings = LlmStructuredOutputSettings.from_functions(
        [search_tool.get_tool()]
    )

    messages = BasicChatHistory()
    for msn in history:
        user = {"role": Roles.user, "content": msn[0]}
        assistant = {"role": Roles.assistant, "content": msn[1]}
        messages.add_message(user)
        messages.add_message(assistant)

    result = web_search_agent.get_chat_response(
        message,
        llm_sampling_settings=settings,
        structured_output_settings=output_settings,
        add_message_to_chat_history=False,
        add_response_to_chat_history=False,
        print_output=False,
    )

    outputs = ""
    settings.stream = True

    response_text = answer_agent.get_chat_response(
        f"Write a detailed and complete research document that fulfills the following user request: '{message}', based on the information from the web below.\n\n" + result[0]["return_value"],
        role=Roles.tool,
        llm_sampling_settings=settings,
        chat_history=messages,
        returns_streaming_generator=True,
        print_output=False,
    )

    for text in response_text:
        outputs += text
        yield outputs

    output_settings = LlmStructuredOutputSettings.from_pydantic_models(
        [CitingSources], LlmStructuredOutputType.object_instance
    )

    citing_sources = answer_agent.get_chat_response(
        "Cite the sources you used in your response.",
        role=Roles.tool,
        llm_sampling_settings=settings,
        chat_history=messages,
        returns_streaming_generator=False,
        structured_output_settings=output_settings,
        print_output=False,
    )

    outputs += "\n\nSources:\n"
    outputs += "\n".join(citing_sources.sources)
    yield outputs


# Gradio interface
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value=web_search_system_prompt, label="System message"),
        gr.Slider(minimum=1, maximum=1000, value=1000, step=1, label="Max tokens"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
        gr.Slider(minimum=0.0, maximum=2.0, value=1.1, step=0.1, label="Repetition penalty"),
        gr.Slider(minimum=1, maximum=100, value=50, step=1, label="Top-k"),
        gr.Slider(minimum=1, maximum=4096, value=2048, step=1, label="Max tokens per summary"),
    ],
    theme=gr.themes.Soft(
        primary_hue="orange",
        secondary_hue="amber",
        neutral_hue="gray",
        font=[gr.themes.GoogleFont("Exo"), "ui-sans-serif", "system-ui", "sans-serif"]).set(
            body_background_fill_dark="#0c0505",
            block_background_fill_dark="#0c0505",
            block_border_width="1px",
            block_title_background_fill_dark="#1b0f0f",
            input_background_fill_dark="#140b0b",
            button_secondary_background_fill_dark="#140b0b",
            border_color_accent_dark="#1b0f0f",
            border_color_primary_dark="#1b0f0f",
            background_fill_secondary_dark="#0c0505",
            color_accent_soft_dark="transparent",
            code_background_fill_dark="#140b0b"
        ),
    css=css,
    retry_btn="Retry",
    undo_btn="Undo",
    clear_btn="Clear",
    submit_btn="Send",
    cache_examples=False,
    examples=examples,
    description="Mistral-7B: Chat with DuckDuckGo Agent",
    analytics_enabled=False,
    chatbot=gr.Chatbot(
        scale=1,
        placeholder=PLACEHOLDER,
        show_copy_button=True
    )
)

if __name__ == "__main__":
    demo.launch()