chat_v2.py

from typing import TypedDict, Any
from collections.abc import Iterator, AsyncIterator
import os

import gradio as gr

from langgraph.graph.state import CompiledStateGraph
from langgraph.prebuilt import create_react_agent
from langchain_aws import ChatBedrock
import boto3

from ask_candid.tools.org_search import OrganizationIdentifier, find_mentioned_organizations
from ask_candid.tools.search import search_candid_knowledge_base
from ask_candid.tools.general import get_current_day
from ask_candid.utils import html_format_docs_chat
from ask_candid.base.config.constants import START_SYSTEM_PROMPT
from ask_candid.base.config.models import Name2Endpoint
from ask_candid.chat import convert_history_for_graph_agent, format_tool_call, format_tool_response

try:
    from feedback import FeedbackApi
    ROOT = "."
except ImportError:
    from demos.feedback import FeedbackApi
    ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "..")

BOT_LOGO = os.path.join(ROOT, "static", "candid_logo_yellow.png")
if not os.path.isfile(BOT_LOGO):
    BOT_LOGO = os.path.join(ROOT, "..", "..", "static", "candid_logo_yellow.png")


class LoggedComponents(TypedDict):
    context: list[gr.Component]
    found_helpful: gr.Component
    will_recommend: gr.Component
    comments: gr.Component
    email: gr.Component


def build_execution_graph() -> CompiledStateGraph:
    llm = ChatBedrock(
        client=boto3.client("bedrock-runtime", region_name="us-east-1"),
        model=Name2Endpoint["claude-3.5-haiku"]
    )
    org_name_recognition = OrganizationIdentifier(llm=llm) # bind the main chat model to the tool
    return create_react_agent(
        model=llm,
        tools=[
            get_current_day,
            org_name_recognition,
            find_mentioned_organizations,
            search_candid_knowledge_base
        ],
    )


def generate_postscript_messages(history: list[gr.ChatMessage]) -> Iterator[gr.ChatMessage]:
    for record in history:
        title = record.metadata.get("tool_name")
        if title == search_candid_knowledge_base.name:
            yield gr.ChatMessage(
                role="assistant",
                content=html_format_docs_chat(record.metadata.get("documents")),
                metadata={
                    "title": "Source citations",
                }
            )
        elif title == find_mentioned_organizations.name:
            pass


async def execute(
    user_input: dict[str, Any],
    history: list[gr.ChatMessage]
) -> AsyncIterator[tuple[gr.Component, list[gr.ChatMessage]]]:
    if len(history) == 0:
        history.append(gr.ChatMessage(role="system", content=START_SYSTEM_PROMPT))

    history.append(gr.ChatMessage(role="user", content=user_input["text"]))
    for fname in user_input.get("files") or []:
        fname: str
        if fname.endswith('.txt'):
            with open(fname, 'r', encoding='utf8') as f:
                history.append(gr.ChatMessage(role="user", content=f.read()))
    yield gr.MultimodalTextbox(value=None, interactive=True), history

    horizon = len(history)
    inputs = {"messages": convert_history_for_graph_agent(history)}

    graph = build_execution_graph()

    history.append(gr.ChatMessage(role="assistant", content=""))
    async for stream_mode, chunk in graph.astream(inputs, stream_mode=["messages", "tasks"]):
        if stream_mode == "messages" and chunk[0].content:
            for msg in chunk[0].content:
                if 'text' in msg:
                    history[-1].content += msg["text"]
                    yield gr.MultimodalTextbox(value=None, interactive=True), history

        elif stream_mode == "tasks" and chunk.get("name") == "tools" and chunk.get("error") is None:
            if "input" in chunk:
                for msg in format_tool_call(chunk):
                    history.append(msg)
                    yield gr.MultimodalTextbox(value=None, interactive=True), history
            elif "result" in chunk:
                for msg in format_tool_response(chunk):
                    history.append(msg)
                    yield gr.MultimodalTextbox(value=None, interactive=True), history
                history.append(gr.ChatMessage(role="assistant", content=""))

    for post_msg in generate_postscript_messages(history=history[horizon:]):
        history.append(post_msg)
        yield gr.MultimodalTextbox(value=None, interactive=True), history


def send_feedback(
    chat_context,
    found_helpful,
    will_recommend,
    comments,
    email
):
    api = FeedbackApi()
    total_submissions = 0

    try:
        response = api(
            context=chat_context,
            found_helpful=found_helpful,
            will_recommend=will_recommend,
            comments=comments,
            email=email
        )
        total_submissions = response.get("response", 0)
        gr.Info("Thank you for submitting feedback")
    except Exception as ex:
        raise gr.Error(f"Error submitting feedback: {ex}")
    return total_submissions


def build_chat_app():
    with gr.Blocks(theme=gr.themes.Soft(), title="Chat") as demo:

        gr.Markdown(
            """
            <h1>Candid's AI assistant</h1>

            <p>
                Please read the <a
                    href='https://info.candid.org/chatbot-reference-guide'
                    target="_blank"
                    rel="noopener noreferrer"
                >guide</a> to get started.
            </p>
            <hr>
            """
        )

        with gr.Column():
            chatbot = gr.Chatbot(
                label="AskCandid",
                elem_id="chatbot",
                editable="user",
                avatar_images=(
                    None,  # user
                    BOT_LOGO,  # bot
                ),
                height="50vh",
                type="messages",
                show_label=False,
                show_copy_button=True,
                autoscroll=True,
                layout="panel",
            )
            msg = gr.MultimodalTextbox(label="Your message", interactive=True)
            gr.ClearButton(components=[msg, chatbot], size="sm")

            # pylint: disable=no-member
            # chatbot.like(fn=like_callback, inputs=chatbot, outputs=None)
            msg.submit(
                fn=execute,
                inputs=[msg, chatbot],
                outputs=[msg, chatbot],
                show_api=False
            )
            logged = LoggedComponents(context=chatbot)

    return demo, logged


def build_feedback(components: LoggedComponents) -> gr.Blocks:
    with gr.Blocks(theme=gr.themes.Soft(), title="Candid AI demo") as demo:
        gr.Markdown("<h1>Help us improve this tool with your valuable feedback</h1>")

        with gr.Row():
            with gr.Column():
                found_helpful = gr.Radio(
                    [True, False], label="Did you find what you were looking for?"
                )
                will_recommend = gr.Radio(
                    [True, False],
                    label="Will you recommend this Chatbot to others?",
                )
                comment = gr.Textbox(label="Additional comments (optional)", lines=4)
                email = gr.Textbox(label="Your email (optional)", lines=1)
                submit = gr.Button("Submit Feedback")

                components["found_helpful"] = found_helpful
                components["will_recommend"] = will_recommend
                components["comments"] = comment
                components["email"] = email

                # pylint: disable=no-member
                submit.click(
                    fn=send_feedback,
                    inputs=[
                        components["context"],
                        components["found_helpful"],
                        components["will_recommend"],
                        components["comments"],
                        components["email"]
                    ],
                    outputs=None,
                    show_api=False,
                    api_name=False,
                    preprocess=False,
                )

    return demo


def build_app():
    candid_chat, logger = build_chat_app()
    feedback = build_feedback(logger)

    with open(os.path.join(ROOT, "static", "chatStyle.css"), "r", encoding="utf8") as f:
        css_chat = f.read()

    demo = gr.TabbedInterface(
        interface_list=[
            candid_chat,
            feedback
        ],
        tab_names=[
            "Candid's AI assistant",
            "Feedback"
        ],
        title="Candid's AI assistant",
        theme=gr.themes.Soft(),
        css=css_chat,
    )
    return demo


if __name__ == "__main__":
    app = build_app()
    app.queue(max_size=5).launch(
        show_api=False,
        mcp_server=False,
        auth=[
            (os.getenv("APP_USERNAME"), os.getenv("APP_PASSWORD")),
            (os.getenv("APP_PUBLIC_USERNAME"), os.getenv("APP_PUBLIC_PASSWORD")),
        ],
        ssr_mode=False,
        auth_message="Login to Candid's AI assistant",
    )