certifai/src/pages/chat.rs

use dioxus::prelude::*;

use crate::components::ChatBubble;
use crate::models::{ChatMessage, ChatRole, ChatSession};

/// ChatGPT-style chat interface with session list and message area.
///
/// Full-height layout: left panel shows session history,
/// right panel shows messages and input bar.
#[component]
pub fn ChatPage() -> Element {
    let sessions = use_signal(mock_sessions);
    let mut active_session_id = use_signal(|| "session-1".to_string());
    let mut input_text = use_signal(String::new);

    // Clone data out of signals before entering the rsx! block to avoid
    // holding a `Signal::read()` borrow across potential await points.
    let sessions_list = sessions.read().clone();
    let current_id = active_session_id.read().clone();
    let active_session = sessions_list.iter().find(|s| s.id == current_id).cloned();

    rsx! {
        section { class: "chat-page",
            div { class: "chat-sidebar-panel",
                div { class: "chat-sidebar-header",
                    h3 { "Conversations" }
                    button { class: "btn-icon", "+" }
                }
                div { class: "chat-session-list",
                    for session in &sessions_list {
                        {
                            let is_active = session.id == current_id;
                            let class = if is_active {
                                "chat-session-item chat-session-item--active"
                            } else {
                                "chat-session-item"
                            };
                            let id = session.id.clone();
                            rsx! {
                                button { class: "{class}", onclick: move |_| active_session_id.set(id.clone()),
                                    div { class: "chat-session-title", "{session.title}" }
                                    div { class: "chat-session-date", "{session.created_at}" }
                                }
                            }
                        }
                    }
                }
            }
            div { class: "chat-main-panel",
                if let Some(session) = &active_session {
                    div { class: "chat-messages",
                        for msg in &session.messages {
                            ChatBubble { key: "{msg.id}", message: msg.clone() }
                        }
                    }
                } else {
                    div { class: "chat-empty",
                        p { "Select a conversation or start a new one." }
                    }
                }
                div { class: "chat-input-bar",
                    button { class: "btn-icon chat-attach-btn", "+" }
                    input {
                        class: "chat-input",
                        r#type: "text",
                        placeholder: "Type a message...",
                        value: "{input_text}",
                        oninput: move |evt: Event<FormData>| {
                            input_text.set(evt.value());
                        },
                    }
                    button { class: "btn-primary chat-send-btn", "Send" }
                }
            }
        }
    }
}

/// Returns mock chat sessions with sample messages.
fn mock_sessions() -> Vec<ChatSession> {
    vec![
        ChatSession {
            id: "session-1".into(),
            title: "RAG Pipeline Setup".into(),
            messages: vec![
                ChatMessage {
                    id: "msg-1".into(),
                    role: ChatRole::User,
                    content: "How do I set up a RAG pipeline with Ollama?".into(),
                    attachments: vec![],
                    timestamp: "10:30".into(),
                },
                ChatMessage {
                    id: "msg-2".into(),
                    role: ChatRole::Assistant,
                    content: "To set up a RAG pipeline with Ollama, you'll need to: \
                        1) Install Ollama and pull your preferred model, \
                        2) Set up a vector database (e.g. ChromaDB), \
                        3) Create an embedding pipeline for your documents, \
                        4) Wire the retrieval step into your prompt chain."
                        .into(),
                    attachments: vec![],
                    timestamp: "10:31".into(),
                },
            ],
            created_at: "2026-02-18".into(),
        },
        ChatSession {
            id: "session-2".into(),
            title: "GDPR Compliance Check".into(),
            messages: vec![
                ChatMessage {
                    id: "msg-3".into(),
                    role: ChatRole::User,
                    content: "What data does CERTifAI store about users?".into(),
                    attachments: vec![],
                    timestamp: "09:15".into(),
                },
                ChatMessage {
                    id: "msg-4".into(),
                    role: ChatRole::Assistant,
                    content: "CERTifAI stores only the minimum data required: \
                        email address, session tokens, and usage metrics. \
                        All data stays on your infrastructure."
                        .into(),
                    attachments: vec![],
                    timestamp: "09:16".into(),
                },
            ],
            created_at: "2026-02-17".into(),
        },
        ChatSession {
            id: "session-3".into(),
            title: "MCP Server Configuration".into(),
            messages: vec![ChatMessage {
                id: "msg-5".into(),
                role: ChatRole::User,
                content: "How do I add a new MCP server?".into(),
                attachments: vec![],
                timestamp: "14:00".into(),
            }],
            created_at: "2026-02-16".into(),
        },
    ]
}