app.py

"""
Madia — Multi-Agent Debate Application.

Gradio interface where 4 AI agents (Chef, Nutrition, Éco, Modérateur)
debate to propose the best meal for the user's request.
"""

import gradio as gr
import spaces
from langchain_core.messages import HumanMessage

from graph.workflow import build_graph
from utils.helpers import extract_final_decision, format_message_for_display

# Agent name-to-display mapping for step-by-step updates
AGENT_NAMES = ["Chef cuisinier", "Nutritionniste", "Écologiste", "Modérateur"]

# Build the graph once at startup
graph = build_graph()


@spaces.GPU(duration=10)
def dummy_gpu_function():
    pass

def run_debate(user_request: str, location: str, history: list, agent_messages: list):
    """
    Generator function that runs the debate and yields updates step-by-step.

    Each time an agent speaks, the chatbot history is updated and yielded
    so the user sees the debate unfold progressively.

    Args:
        user_request: The user's meal request.
        history: Current Gradio chatbot history.
        agent_messages: LangChain messages state.

    Yields:
        Tuples of (chatbot_history, decision_markdown, status_text, agent_messages)
        after each agent speaks.
    """
    if not user_request.strip():
        yield history, "", "Veuillez entrer une demande.", agent_messages
        return

    # Add the user's message to the chatbot display
    history = history + [{"role": "user", "content": user_request}]
    yield history, "", "Lancement du débat...", agent_messages

    # Add the new request to the agent history
    agent_messages = agent_messages + [HumanMessage(content=user_request)]

    # Prepare the initial state for the graph
    initial_state = {
        "messages": agent_messages,
        "user_request": user_request,
        "location": location,
        "current_round": 1,
        "decision_reached": False,
        "final_decision": "",
    }

    # Stream the graph execution step by step
    final_decision_text = ""

    try:
        for event in graph.stream(initial_state, stream_mode="updates"):
            # Each event is a dict: {node_name: state_update}
            for node_name, state_update in event.items():
                if node_name == "__start__":
                    continue

                # Get the new messages from this step
                new_messages = state_update.get("messages", [])
                current_round = state_update.get("current_round", None)

                # Append to our persistent LangChain history
                agent_messages = agent_messages + new_messages

                for msg in new_messages:
                    agent_name = getattr(msg, "name", node_name.capitalize())
                    display_msg = format_message_for_display(
                        agent_name, msg.content
                    )
                    history = history + [display_msg]

                # Map node name to display info
                agent_display = {
                    "chef": "Chef cuisinier",
                    "nutrition": "Nutritionniste",
                    "eco": "Écologiste",
                    "moderator": "Modérateur",
                }
                agent_status = agent_display.get(node_name, "")
                status = f"{agent_status} a parlé"

                # Check for final decision
                if state_update.get("decision_reached", False):
                    final_decision_text = state_update.get(
                        "final_decision", ""
                    )
                    status = "Décision finale atteinte"

                yield history, format_decision_panel(final_decision_text), status, agent_messages

    except Exception as e:
        error_msg = f"Erreur : {str(e)}"
        history = history + [
            {"role": "assistant", "content": error_msg}
        ]
        yield history, "", error_msg, agent_messages
        return

    # If no decision was found in the stream, try to extract from last message
    if not final_decision_text and history:
        last_content = history[-1].get("content", "")
        final_decision_text = extract_final_decision(last_content) or ""

    # Ultimate fallback: use the last agent message as the decision
    if not final_decision_text and history:
        for msg in reversed(history):
            if msg.get("role") == "assistant" and msg.get("content", "").strip():
                final_decision_text = msg["content"]
                break

    final_status = "Débat terminé"

    yield history, format_decision_panel(final_decision_text), final_status, agent_messages


def format_decision_panel(decision_text: str) -> str:
    """
    Format the final decision as a styled Markdown panel.

    Args:
        decision_text: Raw decision text from the moderator.

    Returns:
        Formatted Markdown string for display.
    """
    if not decision_text:
        return ""

    return f"""
---

{decision_text}

---
"""


# ─────────────────────────────────────────────────────────────
# Gradio Interface
# ─────────────────────────────────────────────────────────────

TITLE = """
<div class="madia-header">
    <h1>Madia</h1>
    <p class="madia-tagline">Intelligence collective pour vos choix gastronomiques</p>
    <p class="madia-desc">Nos experts — chef cuisinier, nutritionniste, écologiste et modérateur — débattent ensemble pour vous proposer le repas idéal, adapté à vos envies et vos valeurs.</p>
</div>
"""

EXAMPLES = [
    "Propose un repas équilibré pour 4 personnes ce soir en été",
    "Je veux un déjeuner rapide et sain pour le bureau",
    "Un dîner romantique pour 2 personnes avec un budget de 30€",
    "Un repas végétarien pour un dimanche en famille",
    "Que manger après une séance de sport intense ?",
]

CSS = """
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');

/* ── Global Reset ── */
.gradio-container {
    font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif !important;
    max-width: 960px !important;
    margin: 0 auto !important;
}

/* ── Header ── */
.madia-header {
    text-align: center;
    padding: 32px 16px 24px;
}

.madia-header h1 {
    font-size: 2.8rem;
    font-weight: 700;
    letter-spacing: -0.03em;
    margin: 0 0 8px;
    background: linear-gradient(135deg, #6366f1, #8b5cf6, #a78bfa);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
    background-clip: text;
}

.madia-tagline {
    font-size: 1.1rem;
    font-weight: 500;
    color: #64748b;
    margin: 0 0 12px;
    letter-spacing: 0.01em;
}

.madia-desc {
    font-size: 0.92rem;
    color: #94a3b8;
    max-width: 600px;
    margin: 0 auto;
    line-height: 1.6;
}

/* ── Input Area ── */
.input-area textarea {
    border-radius: 12px !important;
    border: 1.5px solid #e2e8f0 !important;
    transition: border-color 0.2s ease, box-shadow 0.2s ease !important;
    font-size: 0.95rem !important;
}

.input-area textarea:focus {
    border-color: #8b5cf6 !important;
    box-shadow: 0 0 0 3px rgba(139, 92, 246, 0.1) !important;
}

/* ── Buttons ── */
.primary-btn {
    background: linear-gradient(135deg, #6366f1, #8b5cf6) !important;
    border: none !important;
    border-radius: 12px !important;
    color: white !important;
    font-weight: 600 !important;
    font-size: 0.95rem !important;
    letter-spacing: 0.02em !important;
    padding: 12px 24px !important;
    transition: all 0.25s ease !important;
    box-shadow: 0 4px 14px rgba(99, 102, 241, 0.25) !important;
}

.primary-btn:hover {
    transform: translateY(-1px) !important;
    box-shadow: 0 6px 20px rgba(99, 102, 241, 0.35) !important;
}

.secondary-btn {
    background: transparent !important;
    border: 1.5px solid #e2e8f0 !important;
    border-radius: 12px !important;
    color: #64748b !important;
    font-weight: 500 !important;
    font-size: 0.85rem !important;
    padding: 8px 16px !important;
    transition: all 0.2s ease !important;
}

.secondary-btn:hover {
    border-color: #cbd5e1 !important;
    color: #475569 !important;
    background: #f8fafc !important;
}

/* ── Status Bar ── */
.status-bar {
    text-align: center;
    padding: 8px 0;
}

.status-bar p {
    font-size: 0.88rem;
    color: #64748b;
    font-weight: 500;
}

/* ── Chatbot ── */
.chatbot-area .chatbot {
    border-radius: 16px !important;
    border: 1.5px solid #e2e8f0 !important;
}

/* ── Decision Panel ── */
.decision-panel {
    border-radius: 16px !important;
    border: 1.5px solid #e2e8f0 !important;
    padding: 4px !important;
}

/* ── Examples ── */
.examples-section {
    margin-top: 8px;
}

.examples-section .label-wrap span {
    font-size: 0.9rem !important;
    font-weight: 600 !important;
    color: #475569 !important;
}

/* ── Footer ── */
footer { display: none !important; }

.madia-footer {
    text-align: center;
    padding: 20px 0 8px;
    font-size: 0.8rem;
    color: #94a3b8;
}

.madia-footer a {
    color: #8b5cf6;
    text-decoration: none;
    font-weight: 500;
}
"""

with gr.Blocks(
    title="Madia — Votre assistant gastronomique",
) as demo:

    gr.HTML(TITLE)

    with gr.Row(equal_height=False):
        with gr.Column(scale=4, elem_classes="input-area"):
            user_input = gr.Textbox(
                label="Votre demande",
                placeholder="Ex: Propose un repas équilibré pour 4 personnes ce soir...",
                lines=2,
                max_lines=4,
            )
            user_location = gr.Textbox(
                label="Votre ville ou région (optionnel)",
                placeholder="Ex: Paris, Kinshasa, London...",
                lines=1,
            )
        with gr.Column(scale=1, min_width=140):
            submit_btn = gr.Button(
                "Lancer le débat",
                variant="primary",
                size="lg",
                elem_classes="primary-btn",
            )
            clear_btn = gr.Button(
                "Effacer l'historique",
                variant="secondary",
                elem_classes="secondary-btn",
            )

    status_text = gr.Markdown(
        "*En attente de votre demande...*",
        elem_classes="status-bar",
    )

    # Hidden state to store LangChain BaseMessage history across runs
    agent_messages = gr.State([])

    chatbot = gr.Chatbot(
        label="Débat entre experts",
        height=480,
        elem_classes="chatbot-area",
    )

    decision_output = gr.Markdown(
        label="Décision Finale",
        value="",
        elem_classes="decision-panel",
    )

    with gr.Accordion("Exemples de demandes", open=False, elem_classes="examples-section"):
        gr.Examples(
            examples=EXAMPLES,
            inputs=user_input,
        )

    gr.HTML('<div class="madia-footer">Madia — Propulsé par l\'intelligence collective</div>')

    # Wire up the UI
    def clear_all():
        return [], "", "*En attente de votre demande...*", [], "", ""

    clear_btn.click(
        fn=clear_all,
        inputs=[],
        outputs=[chatbot, decision_output, status_text, agent_messages, user_input, user_location],
    )

    submit_btn.click(
        fn=run_debate,
        inputs=[user_input, user_location, chatbot, agent_messages],
        outputs=[chatbot, decision_output, status_text, agent_messages],
    )

    user_input.submit(
        fn=run_debate,
        inputs=[user_input, user_location, chatbot, agent_messages],
        outputs=[chatbot, decision_output, status_text, agent_messages],
    )
    
    user_location.submit(
        fn=run_debate,
        inputs=[user_input, user_location, chatbot, agent_messages],
        outputs=[chatbot, decision_output, status_text, agent_messages],
    )

if __name__ == "__main__":
    demo.launch(
        css=CSS,
        theme=gr.themes.Soft(
            primary_hue="violet",
            secondary_hue="slate",
            neutral_hue="slate",
            font=gr.themes.GoogleFont("Inter"),
        ),
        ssr_mode=False,
    )