openmark/ui/app.py

"""

OpenMark Gradio UI — 3 tabs:

  1. Chat    — talk to the LangGraph agent

  2. Search  — instant semantic search with filters

  3. Stats   — knowledge base overview

"""

import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.stdout.reconfigure(encoding="utf-8")

import gradio as gr
from openmark import config

# Load once at startup — fail gracefully if credentials are not configured
print("Loading OpenMark...")
_embedder = None
_agent    = None
_setup_error = None

try:
    from openmark.embeddings.factory import get_embedder
    from openmark.agent.graph import build_agent, ask
    from openmark.stores import chroma as chroma_store
    from openmark.stores import neo4j_store
    _embedder = get_embedder()
    _agent    = build_agent()
    print("OpenMark ready.")
except Exception as e:
    _setup_error = str(e)
    print(f"OpenMark setup incomplete: {e}")


# ── Chat tab ──────────────────────────────────────────────────

_NOT_READY = (
    "## Setup required\n\n"
    "This Space is a **demo shell** — it requires your own credentials to run.\n\n"
    "See the [GitHub repo](https://github.com/OthmanAdi/OpenMark) for full setup instructions."
)


def chat_fn(message: str, history: list, thread_id: str):
    if _agent is None:
        history.append((message, _NOT_READY))
        return history, ""
    if not message.strip():
        return history, ""
    response = ask(_agent, message, thread_id=thread_id or "default")
    history.append((message, response))
    return history, ""


# ── Search tab ────────────────────────────────────────────────

def search_fn(query: str, category: str, min_score: float, n_results: int):
    if _embedder is None:
        return _NOT_READY
    if not query.strip():
        return "Enter a search query."

    cat = category if category != "All" else None
    ms  = min_score if min_score > 0 else None

    results = chroma_store.search(
        query, _embedder, n=int(n_results),
        category=cat, min_score=ms,
    )

    if not results:
        return "No results found."

    lines = []
    for r in results:
        lines.append(
            f"**{r['rank']}. {r['title'] or r['url']}**\n"
            f"🔗 {r['url']}\n"
            f"📁 {r['category']} | 📌 {', '.join(t for t in r['tags'] if t)} | "
            f"⭐ {r['score']} | 🎯 {r['similarity']:.3f} similarity\n"
        )
    return "\n---\n".join(lines)


# ── Stats tab ─────────────────────────────────────────────────

def stats_fn():
    if _embedder is None:
        return _NOT_READY
    chroma = chroma_store.get_stats()
    neo4j  = neo4j_store.get_stats()

    # Category breakdown from Neo4j
    cat_rows = neo4j_store.query("""

        MATCH (b:Bookmark)-[:IN_CATEGORY]->(c:Category)

        RETURN c.name AS category, count(b) AS count

        ORDER BY count DESC

    """)
    cat_lines = "\n".join(f"  {r['category']:<35} {r['count']:>5}" for r in cat_rows)

    # Top tags
    tag_rows = neo4j_store.query("""

        MATCH (b:Bookmark)-[:TAGGED]->(t:Tag)

        RETURN t.name AS tag, count(b) AS count

        ORDER BY count DESC LIMIT 20

    """)
    tag_lines = ", ".join(f"{r['tag']} ({r['count']})" for r in tag_rows)

    return (
        f"## OpenMark Knowledge Base\n\n"
        f"**ChromaDB vectors:** {chroma.get('total', 0)}\n"
        f"**Neo4j bookmarks:** {neo4j.get('bookmarks', 0)}\n"
        f"**Neo4j tags:** {neo4j.get('tags', 0)}\n"
        f"**Neo4j categories:** {neo4j.get('categories', 0)}\n\n"
        f"### By Category\n```\n{cat_lines}\n```\n\n"
        f"### Top Tags\n{tag_lines}"
    )


# ── Build UI ──────────────────────────────────────────────────

def build_ui():
    with gr.Blocks(title="OpenMark") as app:
        gr.Markdown("# OpenMark — Your Personal Knowledge Graph")

        if _setup_error:
            # No credentials — show landing page only
            gr.Markdown(
                "**8,000+ bookmarks, LinkedIn saves, and YouTube videos** indexed with "
                "[pplx-embed](https://huggingface.co/collections/perplexity-ai/pplx-embed), "
                "searchable with ChromaDB and Neo4j, queryable via a LangGraph agent.\n\n"
                "Built by [Ahmad Othman Ammar Adi](https://github.com/OthmanAdi) · "
                "[GitHub](https://github.com/OthmanAdi/OpenMark) · "
                "[Dataset](https://huggingface.co/datasets/codingwithadi/openmark-bookmarks)\n\n"
                "---\n\n"
                "## Run it yourself\n\n"
                "This Space requires your own data and credentials. "
                "Clone the repo and follow the setup guide:\n\n"
                "```bash\n"
                "git clone https://github.com/OthmanAdi/OpenMark.git\n"
                "cd OpenMark\n"
                "pip install -r requirements.txt\n"
                "cp .env.example .env  # add your keys\n"
                "python scripts/ingest.py\n"
                "python openmark/ui/app.py\n"
                "```"
            )
        else:
            categories = ["All"] + config.CATEGORIES
            with gr.Tabs():

                # Tab 1: Chat
                with gr.Tab("Chat"):
                    thread = gr.Textbox(value="default", label="Session ID", scale=1)
                    chatbot = gr.Chatbot(height=500)
                    msg_box = gr.Textbox(
                        placeholder="Ask anything about your saved bookmarks...",
                        label="Message", lines=2,
                    )
                    send_btn = gr.Button("Send", variant="primary")

                    send_btn.click(
                        chat_fn,
                        inputs=[msg_box, chatbot, thread],
                        outputs=[chatbot, msg_box],
                    )
                    msg_box.submit(
                        chat_fn,
                        inputs=[msg_box, chatbot, thread],
                        outputs=[chatbot, msg_box],
                    )

                # Tab 2: Search
                with gr.Tab("Search"):
                    with gr.Row():
                        q_input   = gr.Textbox(placeholder="Search your knowledge base...", label="Query", scale=3)
                        cat_input = gr.Dropdown(categories, value="All", label="Category")
                    with gr.Row():
                        score_input = gr.Slider(0, 10, value=0, step=1, label="Min Quality Score")
                        n_input     = gr.Slider(5, 50, value=10, step=5, label="Results")
                    search_btn    = gr.Button("Search", variant="primary")
                    search_output = gr.Markdown()

                    search_btn.click(
                        search_fn,
                        inputs=[q_input, cat_input, score_input, n_input],
                        outputs=search_output,
                    )
                    q_input.submit(
                        search_fn,
                        inputs=[q_input, cat_input, score_input, n_input],
                        outputs=search_output,
                    )

                # Tab 3: Stats
                with gr.Tab("Stats"):
                    refresh_btn  = gr.Button("Refresh Stats")
                    stats_output = gr.Markdown()

                    refresh_btn.click(stats_fn, outputs=stats_output)
                    app.load(stats_fn, outputs=stats_output)

    return app


if __name__ == "__main__":
    ui = build_ui()
    ui.launch(server_name="0.0.0.0", server_port=7860, share=False)