"""Gradio UI for the NotebookLM-style application.

Spec references:
- `specs/02_architecture.md`: Gradio frontend with HF OAuth login and notebook switching.
- `specs/04_interfaces.md`: all backend interactions go through module APIs.
- `specs/07_security.md`: authentication and per-user isolation.
- `specs/08_ui_spec.md`: login status, notebook selector, upload, chat, and artifact panels.
- `specs/10_test_plan.md`: explicit error handling and testable UI helpers.
"""

from __future__ import annotations

from pathlib import Path
import os
import sys
from typing import Any
from uuid import uuid4

import gradio as gr


PROJECT_ROOT = Path(__file__).resolve().parent
SRC_ROOT = PROJECT_ROOT / "src"
if str(SRC_ROOT) not in sys.path:
    sys.path.insert(0, str(SRC_ROOT))

DATA_ROOT = Path(os.getenv("NOTEBOOKLM_DATA_ROOT", "/tmp/notebooklm")).expanduser()

from ingestion.chunking import sentence_aware_chunk, semantic_chunk
from ingestion.embedder import embed_texts
from ingestion.extractors import (
    extract_text_from_pdf,
    extract_text_from_pptx,
    extract_text_from_txt,
    extract_text_from_url,
)
from ingestion.indexer import upsert_chunks
from notebooklm_clone.artifacts import (
    ArtifactRef,
    generate_podcast_transcript,
    generate_quiz,
    generate_report,
)
from notebooklm_clone.auth import NotAuthenticatedError, get_current_user
from notebooklm_clone.chat import ChatResponse, answer_question
from notebooklm_clone.export import export_notebook_zip
from notebooklm_clone.notebooks import (
    NotebookRecord,
    create_notebook,
    list_notebooks,
)


CHUNK_MAX_CHARS = 1200
CHUNK_OVERLAP_CHARS = 200


def _artifact_choices(paths: list[str]) -> list[tuple[str, str]]:
    """Map artifact paths into Gradio dropdown choices."""

    return [(Path(path).name, path) for path in paths]


def _require_user(request: gr.Request | None) -> str:
    """Extract the authenticated username from the request context."""

    if request is None:
        raise NotAuthenticatedError("Authenticated request context is required.")
    return get_current_user(request)


def _resolve_username(
    profile: gr.OAuthProfile | None,
    request: gr.Request | None,
    current_username: str | None = None,
) -> str:
    """Resolve the authenticated username from OAuth profile, request, or stored state."""

    if profile is not None:
        username: str | None = getattr(profile, "username", None)
        if isinstance(username, str) and username.strip():
            return username.strip()

    if current_username is not None and current_username.strip():
        return current_username.strip()

    return _require_user(request)


def _notebook_choices(notebooks: list[NotebookRecord]) -> list[tuple[str, str]]:
    """Map notebook records into dropdown choices."""

    return [(notebook["name"], notebook["id"]) for notebook in notebooks]


def _render_login_status(username: str) -> str:
    """Render the top-bar login status."""

    return f"**Signed in as:** `{username}`"


def _render_logged_out_status() -> str:
    """Render the top-bar status for unauthenticated visitors."""

    return "**Not signed in.** Use the Hugging Face login button to continue."


def _render_citations(citations: list[dict[str, Any]]) -> str:
    """Render structured citations into markdown for the chat panel."""

    if not citations:
        return ""

    lines: list[str] = ["", "", "Sources:"]
    for citation in citations:
        marker: str = str(citation.get("marker", ""))
        source_name: str = str(citation.get("source_name", ""))
        source_id: str = str(citation.get("source_id", ""))
        loc: Any = citation.get("loc")
        lines.append(f"- {marker} {source_name} (`{source_id}`) {loc}")
    return "\n".join(lines)


def _refresh_notebook_state(
    username: str,
    selected_notebook_id: str | None = None,
) -> tuple[str, gr.Dropdown]:
    """Build notebook dropdown UI state for the authenticated user."""

    notebooks: list[NotebookRecord] = list_notebooks(username)
    choices: list[tuple[str, str]] = _notebook_choices(notebooks)
    value: str | None = selected_notebook_id
    if value is None and notebooks:
        value = notebooks[0]["id"]
    if value is not None and value not in {notebook["id"] for notebook in notebooks}:
        value = notebooks[0]["id"] if notebooks else None
    return _render_login_status(username), gr.Dropdown(choices=choices, value=value)


def load_session(
    profile: gr.OAuthProfile | None,
    request: gr.Request,
) -> tuple[str, gr.Dropdown, list[dict[str, str]], gr.Dropdown, str, str]:
    """Initialize login status and notebook selector when the UI loads."""

    try:
        username: str = _resolve_username(profile, request)
        login_status, notebook_dropdown = _refresh_notebook_state(username)
    except NotAuthenticatedError:
        login_status = _render_logged_out_status()
        notebook_dropdown = gr.Dropdown(choices=[], value=None)
        username = ""
    empty_chat: list[dict[str, str]] = []
    artifact_dropdown = gr.Dropdown(choices=[], value=None)
    return login_status, notebook_dropdown, empty_chat, artifact_dropdown, username, "Session loaded."


def create_notebook_ui(
    notebook_name: str,
    current_username: str,
    profile: gr.OAuthProfile | None,
    request: gr.Request,
) -> tuple[str, gr.Dropdown, str, str, str]:
    """Create a notebook and refresh the selector."""

    username: str = _resolve_username(profile, request, current_username)
    notebook: NotebookRecord = create_notebook(username, notebook_name)
    login_status, dropdown = _refresh_notebook_state(username, notebook["id"])
    return login_status, dropdown, "", username, f"Notebook created: `{notebook['name']}`"


def on_notebook_change(_notebook_id: str | None, username: str = "") -> tuple[list[dict[str, str]], gr.Dropdown, str, str, list[str], str]:
    """Clear notebook-scoped UI state when the selected notebook changes."""

    existing_docs: list[str] = []
    if _notebook_id and username:
        try:
            from ingestion.indexer import _get_collection
            collection = _get_collection(username, _notebook_id)
            # Get all metadata to extract unique source_names
            all_meta = collection.get(include=["metadatas"])
            seen: set[str] = set()
            for meta in (all_meta.get("metadatas") or []):
                name = (meta or {}).get("source_name", "")
                if name and name not in seen:
                    seen.add(name)
                    existing_docs.append(name)
        except Exception:
            pass

    docs_md = _render_uploaded_docs(existing_docs)
    return [], gr.Dropdown(choices=[], value=None), "", "Notebook selection updated.", existing_docs, docs_md


def _extract_from_file(file_path: str) -> tuple[str, str]:
    """Dispatch local file extraction by suffix."""

    path = Path(file_path)
    suffix: str = path.suffix.lower()
    if suffix == ".pdf":
        doc = extract_text_from_pdf(path)
    elif suffix == ".pptx":
        doc = extract_text_from_pptx(path)
    elif suffix == ".txt":
        doc = extract_text_from_txt(path)
    else:
        raise ValueError(f"Unsupported upload type: {suffix}")
    return doc["text"], path.name


def _generate_context_header(source_name: str, text: str) -> str:
    """Generate a one-sentence contextual summary for chunk headers.

    Uses the LLM to summarize the document, giving the embedding model
    semantic context for disambiguation. Falls back to source_name if
    no API key is set or the call fails.
    """

    api_key: str = os.getenv("OPENAI_API_KEY", "").strip()
    model: str = os.getenv("NOTEBOOKLM_CHAT_MODEL", "gpt-4o-mini").strip()
    if not api_key:
        return source_name

    try:
        from openai import OpenAI
        client = OpenAI(api_key=api_key)
        # Use only the first 2000 chars to keep the call fast/cheap
        preview = text[:2000]
        response = client.chat.completions.create(
            model=model,
            messages=[
                {
                    "role": "system",
                    "content": (
                        "Write ONE concise sentence summarizing what this document is about. "
                        "Focus on the specific subject matter and key topics. "
                        "Do not start with 'This document'. Just state the subject."
                    ),
                },
                {"role": "user", "content": preview},
            ],
            temperature=0.0,
            max_tokens=60,
        )
        summary = (response.choices[0].message.content or "").strip().rstrip(".")
        if summary:
            return f"{source_name} | {summary}"
    except Exception:
        pass

    return source_name


def _render_uploaded_docs(doc_list: list[str]) -> str:
    """Render the uploaded documents list as markdown."""
    if not doc_list:
        return "*No sources uploaded yet.*"
    lines = ["**Uploaded Sources:**"]
    for i, name in enumerate(doc_list, 1):
        lines.append(f"{i}. 📄 {name}")
    return "\n".join(lines)


def _step(msg: str) -> str:
    """Format an in-progress step message."""
    return f"⏳ **{msg}**"


def ingest_upload_ui(
    notebook_id: str | None,
    file_path: str | None,
    uploaded_docs: list[str] | None,
    current_username: str,
    profile: gr.OAuthProfile | None,
    request: gr.Request,
):
    """Ingest an uploaded local file (generator — yields live status)."""

    username: str = _resolve_username(profile, request, current_username)
    if not notebook_id:
        raise gr.Error("Select a notebook before uploading a source.")
    if not file_path:
        raise gr.Error("Choose a file to upload.")

    docs = list(uploaded_docs or [])
    docs_md = _render_uploaded_docs(docs)

    # Step 1: Extract text
    yield _step("Extracting text from file…"), docs, docs_md
    source_text, source_name = _extract_from_file(file_path)

    # Step 2: Generate contextual header
    yield _step(f"Generating context header for *{source_name}*…"), docs, docs_md
    header = _generate_context_header(source_name, source_text)

    # Step 3: Chunking
    yield _step(f"Chunking *{source_name}*…"), docs, docs_md
    chunking_method = os.getenv("NOTEBOOKLM_CHUNKING_METHOD", "semantic").strip().lower()
    if chunking_method == "semantic":
        chunks = semantic_chunk(text=source_text, max_chars=CHUNK_MAX_CHARS, header=header)
    else:
        chunks = sentence_aware_chunk(text=source_text, max_chars=CHUNK_MAX_CHARS, overlap_chars=CHUNK_OVERLAP_CHARS, header=header)
    if not chunks:
        raise gr.Error("No indexable text was extracted from the source.")

    # Step 4: Embedding
    yield _step(f"Embedding {len(chunks)} chunks…"), docs, docs_md
    embeddings = embed_texts([c["chunk_text"] for c in chunks])
    location_hints = [{"start_char": c["start_char"], "end_char": c["end_char"]} for c in chunks]

    # Step 5: Indexing
    yield _step(f"Indexing {len(chunks)} chunks…"), docs, docs_md
    summary = upsert_chunks(
        username=username, notebook_id=notebook_id, source_id=str(uuid4()),
        chunks=chunks, embeddings=embeddings,
        meta={"source_name": source_name, "location_hints": location_hints},
    )

    # Done — update docs list
    if source_name not in docs:
        docs.append(source_name)
    yield f"✅ Indexed **{summary['chunk_count']}** chunks from `{source_name}`.", docs, _render_uploaded_docs(docs)


def ingest_url_ui(
    notebook_id: str | None,
    url: str,
    uploaded_docs: list[str] | None,
    current_username: str,
    profile: gr.OAuthProfile | None,
    request: gr.Request,
):
    """Ingest a URL source (generator — yields live status)."""

    username: str = _resolve_username(profile, request, current_username)
    if not notebook_id:
        raise gr.Error("Select a notebook before ingesting a URL.")
    if not url or not url.strip():
        raise gr.Error("Enter a URL to ingest.")

    source_label = url.strip()
    docs = list(uploaded_docs or [])
    docs_md = _render_uploaded_docs(docs)

    # Step 1: Fetch URL
    yield _step("Fetching URL content…"), docs, docs_md
    doc = extract_text_from_url(source_label)

    # Step 2: Generate contextual header
    yield _step(f"Generating context header…"), docs, docs_md
    header = _generate_context_header(source_label, doc["text"])

    # Step 3: Chunking
    yield _step("Chunking content…"), docs, docs_md
    chunking_method = os.getenv("NOTEBOOKLM_CHUNKING_METHOD", "semantic").strip().lower()
    if chunking_method == "semantic":
        chunks = semantic_chunk(text=doc["text"], max_chars=CHUNK_MAX_CHARS, header=header)
    else:
        chunks = sentence_aware_chunk(text=doc["text"], max_chars=CHUNK_MAX_CHARS, overlap_chars=CHUNK_OVERLAP_CHARS, header=header)
    if not chunks:
        raise gr.Error("No indexable text was extracted from the URL.")

    # Step 4: Embedding
    yield _step(f"Embedding {len(chunks)} chunks…"), docs, docs_md
    embeddings = embed_texts([c["chunk_text"] for c in chunks])
    location_hints = [{"start_char": c["start_char"], "end_char": c["end_char"]} for c in chunks]

    # Step 5: Indexing
    yield _step(f"Indexing {len(chunks)} chunks…"), docs, docs_md
    summary = upsert_chunks(
        username=username, notebook_id=notebook_id, source_id=str(uuid4()),
        chunks=chunks, embeddings=embeddings,
        meta={"source_name": source_label, "location_hints": location_hints},
    )

    # Done
    if source_label not in docs:
        docs.append(source_label)
    yield f"✅ Indexed **{summary['chunk_count']}** chunks from `{source_label}`.", docs, _render_uploaded_docs(docs)


def send_chat_ui(
    notebook_id: str | None,
    question: str,
    rag_mode: str,
    history: list[dict[str, str]] | None,
    current_username: str,
    profile: gr.OAuthProfile | None,
    request: gr.Request,
) -> tuple[str, list[dict[str, str]]]:
    """Send one chat question and append the grounded answer to the chat history."""

    username: str = _resolve_username(profile, request, current_username)
    if not notebook_id:
        raise gr.Error("Select a notebook before sending a message.")
    if not question or not question.strip():
        raise gr.Error("Message cannot be empty.")

    chat_history: list[dict[str, str]] = history or []
    try:
        response: ChatResponse = answer_question(username, notebook_id, question.strip(), rag_mode)
    except Exception as e:
        chat_history.append({"role": "user", "content": question.strip()})
        chat_history.append({"role": "assistant", "content": f"Error: {e}"})
        return "", chat_history

    updated_history: list[dict[str, str]] = list(history or [])
    updated_history.append({"role": "user", "content": question.strip()})
    updated_history.append(
        {
            "role": "assistant",
            "content": response["content"] + _render_citations(response["citations"]),
        }
    )
    return "", updated_history


def _append_artifact_path(current_paths: list[str] | None, artifact: ArtifactRef) -> tuple[list[str], gr.Dropdown]:
    """Append one generated artifact path and refresh the download list."""

    paths: list[str] = list(current_paths or [])
    if artifact["path"] not in paths:
        paths.append(artifact["path"])
    return paths, gr.Dropdown(choices=_artifact_choices(paths), value=artifact["path"])


def generate_report_ui(
    notebook_id: str | None,
    artifact_paths: list[str] | None,
    current_username: str,
    profile: gr.OAuthProfile | None,
    request: gr.Request,
) -> tuple[list[str], gr.Dropdown, str]:
    """Generate a report artifact and update the download list."""

    username: str = _resolve_username(profile, request, current_username)
    if not notebook_id:
        raise gr.Error("Select a notebook before generating a report.")
    artifact = generate_report(username, notebook_id)
    paths, dropdown = _append_artifact_path(artifact_paths, artifact)
    return paths, dropdown, f"Report generated: `{Path(artifact['path']).name}`"


def generate_quiz_ui(
    notebook_id: str | None,
    artifact_paths: list[str] | None,
    current_username: str,
    profile: gr.OAuthProfile | None,
    request: gr.Request,
) -> tuple[list[str], gr.Dropdown, str]:
    """Generate a quiz artifact and update the download list."""

    username: str = _resolve_username(profile, request, current_username)
    if not notebook_id:
        raise gr.Error("Select a notebook before generating a quiz.")
    artifact = generate_quiz(username, notebook_id)
    paths, dropdown = _append_artifact_path(artifact_paths, artifact)
    return paths, dropdown, f"Quiz generated: `{Path(artifact['path']).name}`"


def generate_podcast_ui(
    notebook_id: str | None,
    artifact_paths: list[str] | None,
    current_username: str,
    profile: gr.OAuthProfile | None,
    request: gr.Request,
) -> tuple[list[str], gr.Dropdown, str]:
    """Generate a podcast transcript artifact and update the download list."""

    username: str = _resolve_username(profile, request, current_username)
    if not notebook_id:
        raise gr.Error("Select a notebook before generating a transcript.")
    artifact = generate_podcast_transcript(username, notebook_id)
    paths, dropdown = _append_artifact_path(artifact_paths, artifact)
    return paths, dropdown, f"Transcript generated: `{Path(artifact['path']).name}`"


def select_artifact_download(artifact_path: str | None) -> Path | None:
    """Map the selected artifact path into a downloadable file."""

    if not artifact_path:
        return None
    return Path(artifact_path)


def export_notebook_ui(
    notebook_id: str | None,
    current_username: str,
    profile: gr.OAuthProfile | None,
    request: gr.Request,
) -> tuple[Path, str]:
    """Export the selected notebook as a zip archive."""

    username: str = _resolve_username(profile, request, current_username)
    if not notebook_id:
        raise gr.Error("Select a notebook before exporting.")
    export_path: Path = export_notebook_zip(username, notebook_id)
    return export_path, f"Notebook exported: `{export_path.name}`"


with gr.Blocks(title="NotebookLM Clone") as demo:
    artifact_paths_state = gr.State(value=[])
    username_state = gr.State(value="")
    uploaded_docs_state = gr.State(value=[])

    gr.Markdown("# NotebookLM Clone")
    with gr.Row():
        login_button = gr.LoginButton()
        login_status = gr.Markdown("Not signed in.")
        notebook_dropdown = gr.Dropdown(
            label="Notebook",
            choices=[],
            value=None,
            interactive=True,
        )

    with gr.Row():
        new_notebook_name = gr.Textbox(label="New Notebook", placeholder="Create a notebook")
        create_notebook_button = gr.Button("Create Notebook", variant="primary")
    activity_status = gr.Markdown("Ready.")

    with gr.Row():
        with gr.Column():
            gr.Markdown("## Upload")
            file_input = gr.File(
                label="Upload source",
                file_types=[".pdf", ".pptx", ".txt"],
                type="filepath",
            )
            upload_button = gr.Button("Ingest Upload")
            url_input = gr.Textbox(label="URL", placeholder="https://example.com/article")
            url_button = gr.Button("Ingest URL")
            ingest_status = gr.Markdown()
            gr.Markdown("---")
            uploaded_docs_display = gr.Markdown("*No sources uploaded yet.*")

        with gr.Column():
            gr.Markdown("## Chat")
            chat_history = gr.Chatbot(
                elem_id="chat-history",
                show_label=False,
            )
            with gr.Row():
                chat_input = gr.Textbox(
                    show_label=False,
                    placeholder="Ask a question about your sources...",
                    scale=4,
                )
                rag_mode = gr.Radio(
                    choices=["Fast", "Reasoning"],
                    value="Reasoning",
                    label="RAG Mode",
                    scale=1,
                    interactive=True,
                )
            chat_submit = gr.Button("Send", variant="primary")

        with gr.Column():
            gr.Markdown("## Artifacts")
            report_button = gr.Button("Generate Report")
            quiz_button = gr.Button("Generate Quiz")
            podcast_button = gr.Button("Generate Transcript")
            artifact_dropdown = gr.Dropdown(
                label="Generated Artifacts",
                choices=[],
                value=None,
            )
            artifact_download = gr.DownloadButton(label="Download Artifact")
            export_button = gr.Button("Export Notebook Zip")
            export_download = gr.DownloadButton(label="Download Notebook Zip")

    demo.load(
        load_session,
        inputs=None,
        outputs=[login_status, notebook_dropdown, chat_history, artifact_dropdown, username_state, activity_status],
    )

    create_notebook_button.click(
        create_notebook_ui,
        inputs=[new_notebook_name, username_state],
        outputs=[login_status, notebook_dropdown, new_notebook_name, username_state, activity_status],
    )

    notebook_dropdown.change(
        on_notebook_change,
        inputs=[notebook_dropdown, username_state],
        outputs=[chat_history, artifact_dropdown, ingest_status, activity_status, uploaded_docs_state, uploaded_docs_display],
    ).then(
        lambda: [],
        inputs=None,
        outputs=[artifact_paths_state],
    )

    upload_button.click(
        ingest_upload_ui,
        inputs=[notebook_dropdown, file_input, uploaded_docs_state, username_state],
        outputs=[ingest_status, uploaded_docs_state, uploaded_docs_display],
    )

    url_button.click(
        ingest_url_ui,
        inputs=[notebook_dropdown, url_input, uploaded_docs_state, username_state],
        outputs=[ingest_status, uploaded_docs_state, uploaded_docs_display],
    )

    chat_submit.click(
        send_chat_ui,
        inputs=[notebook_dropdown, chat_input, rag_mode, chat_history, username_state],
        outputs=[chat_input, chat_history],
    )
    chat_input.submit(
        send_chat_ui,
        inputs=[notebook_dropdown, chat_input, rag_mode, chat_history, username_state],
        outputs=[chat_input, chat_history],
    )

    report_button.click(
        generate_report_ui,
        inputs=[notebook_dropdown, artifact_paths_state, username_state],
        outputs=[artifact_paths_state, artifact_dropdown, activity_status],
    )

    quiz_button.click(
        generate_quiz_ui,
        inputs=[notebook_dropdown, artifact_paths_state, username_state],
        outputs=[artifact_paths_state, artifact_dropdown, activity_status],
    )

    podcast_button.click(
        generate_podcast_ui,
        inputs=[notebook_dropdown, artifact_paths_state, username_state],
        outputs=[artifact_paths_state, artifact_dropdown, activity_status],
    )

    artifact_dropdown.change(
        select_artifact_download,
        inputs=[artifact_dropdown],
        outputs=[artifact_download],
    )

    export_button.click(
        export_notebook_ui,
        inputs=[notebook_dropdown, username_state],
        outputs=[export_download, activity_status],
    )


if __name__ == "__main__":
    demo.launch(allowed_paths=[str(DATA_ROOT)])