app.py

import os
import logging
import streamlit as st

# Install necessary libraries using os.system
os.system("pip install --upgrade pip")
os.system("pip install streamlit llama-cpp-agent huggingface_hub trafilatura beautifulsoup4 requests duckduckgo-search googlesearch-python")

# Attempt to import all required modules
try:
    from llama_cpp import Llama
    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 utils import CitingSources
    from settings import get_context_by_model, get_messages_formatter_type
except ImportError as e:
    st.error(f"Error importing modules: {e}")
    if 'utils' in str(e):
        st.warning("Mocking utils.CitingSources")
        class CitingSources:
            sources = []

    if 'settings' in str(e):
        st.warning("Mocking settings functions")
        def get_context_by_model(model):
            return 4096

        def get_messages_formatter_type(model):
            return MessagesFormatterType.BASIC

import logging
from huggingface_hub import hf_hub_download

# Download the models
hf_hub_download(
    repo_id="bartowski/Mistral-7B-Instruct-v0.3-GGUF",
    filename="Mistral-7B-Instruct-v0.3-Q6_K.gguf",
    local_dir="./models"
)
hf_hub_download(
    repo_id="bartowski/Meta-Llama-3-8B-Instruct-GGUF",
    filename="Meta-Llama-3-8B-Instruct-Q6_K.gguf",
    local_dir="./models"
)
hf_hub_download(
    repo_id="TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF",
    filename="mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf",
    local_dir="./models"
)

# Function to respond to user messages
def respond(message, history, system_message, temperature, top_p, top_k, repeat_penalty):
    model = "mixtral-8x7b-instruct-v0.1.Q5_K_M.gguf"
    max_tokens = 3000
    chat_template = get_messages_formatter_type(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),
    )
    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

st.title("Novav2 Web Engine")

message = st.text_input("Enter your message:")
history = st.session_state.get("history", [])
system_message = st.text_area("System message", value=web_search_system_prompt)
temperature = st.slider("Temperature", min_value=0.1, max_value=1.0, value=0.45, step=0.1)
top_p = st.slider("Top-p", min_value=0.1, max_value=1.0, value=0.95, step=0.05)
top_k = st.slider("Top-k", min_value=0, max_value=100, value=40, step=1)
repeat_penalty = st.slider("Repetition penalty", min_value=0.0, max_value=2.0, value=1.1, step=0.1)

if st.button("Send"):
    response_generator = respond(message, history, system_message, temperature, top_p, top_k, repeat_penalty)
    for response in response_generator:
        st.write(response)
        history.append((message, response))
        st.session_state["history"] = history