app.py

import os
import logging
import gradio as gr
from transformers import pipeline

from llama_cpp_agent.providers import LlamaCppPythonProvider
from llama_cpp_agent import LlamaCppAgent, 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 pydantic import BaseModel, Field
from trafilatura import fetch_url, extract
import json
from datetime import datetime, timezone
from typing import List
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFaceHub

llm = None
llm_model = None

huggingface_token = os.environ.get("HUGGINGFACE_TOKEN")

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"]
]

def get_context_by_model(model_name):
    model_context_limits = {
        "Mistral-7B-Instruct-v0.3": 32768,
    }
    return model_context_limits.get(model_name, None)

def get_messages_formatter_type(model_name):
    model_name = model_name.lower()
    if "mistral" in model_name:
        return MessagesFormatterType.MISTRAL
    else:
        return MessagesFormatterType.CHATML

def get_model(temperature, top_p, repetition_penalty):
    return HuggingFaceHub(
        repo_id="mistralai/Mistral-7B-Instruct-v0.3",
        model_kwargs={
            "temperature": temperature,
            "top_p": top_p,
            "repetition_penalty": repetition_penalty,
            "max_length": 1000
        },
        huggingfacehub_api_token=huggingface_token
    )

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."
    )

def write_message_to_user():
    return "Please write the message to the user."

#@spaces.GPU(duration=120)
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 = get_model(temperature, top_p, repeat_penalty)
        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

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Dropdown([
            'Mistral-7B-Instruct-v0.3'
        ],
            value="Mistral-7B-Instruct-v0.3",
            label="Model"
        ),
        gr.Textbox(value=web_search_system_prompt, label="System message"),
        gr.Slider(minimum=1, maximum=4096, value=2048, step=1, label="Max tokens"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.45, 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,
            maximum=100,
            value=40,
            step=1,
            label="Top-k",
        ),
        gr.Slider(
            minimum=0.0,
            maximum=2.0,
            value=1.1,
            step=0.1,
            label="Repetition penalty",
        ),
    ],
    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",
            slider_color="#ff911a",
            button_primary_background_fill="#ff911a",
            button_primary_background_fill_dark="#ff911a",
            button_primary_text_color="#f9f9f9",
            button_primary_text_color_dark="#f9f9f9"
        ),
    examples=examples,
    title="llama.cpp agent",
)

demo.queue().launch()