"""Dataset Card Drafter - MVP Space.

Watches davanstrien/* datasets and opens PRs with auto-generated descriptions.
"""

import asyncio
import json
import logging
import os
from datetime import datetime
from pathlib import Path

import gradio as gr
from huggingface_hub import (
    DatasetCard,
    WebhookPayload,
    WebhooksServer,
    get_repo_discussions,
)

from description_generator import ViewerNotReadyError, generate_description

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Space URL for attribution
SPACE_URL = "https://huggingface.co/spaces/librarian-bots/dataset-card-drafter"
BOT_NAME = "librarian-bot"
PR_TITLE = "Add dataset description"

# Configuration
WATCHED_PREFIXES = ["davanstrien/"]  # Repos to watch
MIN_DESCRIPTION_LENGTH = 100  # Chars below which we generate

# Persistence directory
DATA_DIR = Path("/data") if Path("/data").exists() else Path("./local_data")
DATA_DIR.mkdir(exist_ok=True)
PROCESSED_FILE = DATA_DIR / "processed.json"
PENDING_FILE = DATA_DIR / "pending.json"

# Retry configuration
RETRY_DELAYS = [60, 120, 300]  # Seconds to wait between retries (1min, 2min, 5min)


def load_processed() -> dict:
    """Load processed datasets from persistence."""
    if PROCESSED_FILE.exists():
        return json.loads(PROCESSED_FILE.read_text())
    return {}


def save_processed(data: dict) -> None:
    """Save processed datasets to persistence."""
    PROCESSED_FILE.write_text(json.dumps(data, indent=2))


def load_pending() -> dict:
    """Load pending datasets from persistence."""
    if PENDING_FILE.exists():
        return json.loads(PENDING_FILE.read_text())
    return {}


def save_pending(data: dict) -> None:
    """Save pending datasets to persistence."""
    PENDING_FILE.write_text(json.dumps(data, indent=2))


def add_to_pending(dataset_id: str, reason: str) -> None:
    """Add a dataset to the pending queue."""
    pending = load_pending()
    pending[dataset_id] = {
        "added": datetime.now().isoformat(),
        "reason": reason,
        "retries": 0,
    }
    save_pending(pending)
    logger.info(f"Added {dataset_id} to pending queue: {reason}")


def remove_from_pending(dataset_id: str) -> None:
    """Remove a dataset from the pending queue."""
    pending = load_pending()
    if dataset_id in pending:
        del pending[dataset_id]
        save_pending(pending)
        logger.info(f"Removed {dataset_id} from pending queue")


def is_watched_repo(repo_name: str) -> bool:
    """Check if a repo is in our watched list."""
    return any(repo_name.startswith(prefix) for prefix in WATCHED_PREFIXES)


def should_generate(card: DatasetCard) -> bool:
    """Check if a dataset card needs a description."""
    if not card.text:
        return True
    return len(card.text.strip()) < MIN_DESCRIPTION_LENGTH


def has_existing_pr(dataset_id: str) -> bool:
    """Check if there's already an open PR from librarian-bot for this dataset."""
    try:
        discussions = list(get_repo_discussions(dataset_id, repo_type="dataset"))
        for discussion in discussions:
            if not discussion.is_pull_request:
                continue
            # Check if it's from librarian-bot and matches our PR title
            if discussion.author == BOT_NAME and discussion.title == PR_TITLE:
                # Check if still open
                if discussion.status == "open":
                    return True
        return False
    except Exception:
        # If we can't check, err on the side of caution
        return False


def build_pr_description(description: str) -> str:
    """Build the PR description with attribution and the generated content."""
    return f"""## Auto-generated Dataset Description

This PR was automatically generated by the [Dataset Card Drafter]({SPACE_URL}) Space.

### Suggested Description

{description}

---

*If you find this description helpful, please merge the PR. If you'd like to edit it first, you can modify the README.md in this branch before merging.*

*Generated by [{BOT_NAME}]({SPACE_URL})*"""


async def process_dataset(dataset_id: str, inference_token: str, pr_token: str) -> dict:
    """Process a single dataset: check, generate, and open PR.

    Args:
        dataset_id: The dataset to process
        inference_token: Token for inference API calls (e.g., davanstrien's token)
        pr_token: Token for opening PRs (librarian-bot's token)

    Returns a status dict with results.
    """
    # Check for existing open PR first
    if has_existing_pr(dataset_id):
        remove_from_pending(dataset_id)  # Clean up if it was pending
        return {"status": "skipped", "reason": "open PR already exists"}

    # Load current card (or create new if none exists)
    try:
        card = DatasetCard.load(dataset_id)
    except Exception:
        # No README exists - create a new card
        card = DatasetCard("")
        logger.info(f"No existing card for {dataset_id}, creating new one")

    # Check if description needed
    if not should_generate(card):
        remove_from_pending(dataset_id)  # Clean up if it was pending
        return {"status": "skipped", "reason": "description exists"}

    # Generate description using inference token
    try:
        description = generate_description(dataset_id, inference_token)
    except ViewerNotReadyError as e:
        return {"status": "pending", "reason": str(e)}
    except Exception as e:
        return {"status": "error", "reason": f"generation failed: {e}"}

    if not description:
        return {"status": "error", "reason": "empty description generated"}

    # Update card and push as PR using librarian-bot token
    card.text = description

    try:
        commit_info = card.push_to_hub(
            repo_id=dataset_id,
            repo_type="dataset",
            commit_message=PR_TITLE,
            commit_description=build_pr_description(description),
            create_pr=True,
            token=pr_token,
        )
        pr_url = getattr(commit_info, "pr_url", str(commit_info))
    except Exception as e:
        return {"status": "error", "reason": f"PR creation failed: {e}"}

    # Success - remove from pending if it was there
    remove_from_pending(dataset_id)

    return {"status": "pr_created", "pr_url": pr_url, "description": description}


async def retry_pending_dataset(dataset_id: str) -> None:
    """Background task to retry a pending dataset after delays."""
    inference_token = os.getenv("HF_TOKEN")
    pr_token = os.getenv("LIBRARIAN_BOT_TOKEN")

    if not inference_token or not pr_token:
        logger.error("Missing tokens for retry")
        return

    for i, delay in enumerate(RETRY_DELAYS):
        logger.info(f"Waiting {delay}s before retry {i + 1} for {dataset_id}")
        await asyncio.sleep(delay)

        # Update retry count
        pending = load_pending()
        if dataset_id not in pending:
            logger.info(f"{dataset_id} no longer pending, stopping retries")
            return
        pending[dataset_id]["retries"] = i + 1
        save_pending(pending)

        # Try processing
        result = await process_dataset(dataset_id, inference_token, pr_token)

        if result["status"] == "pr_created":
            logger.info(f"Successfully processed {dataset_id} on retry {i + 1}")
            # Log to processed
            processed = load_processed()
            processed[dataset_id] = {
                "pr_url": result.get("pr_url"),
                "timestamp": datetime.now().isoformat(),
                "status": "pr_created",
                "trigger": "retry",
                "retry_attempt": i + 1,
            }
            save_processed(processed)
            return
        elif result["status"] != "pending":
            # Got a definitive answer (error or skipped), stop retrying
            logger.info(f"Stopping retries for {dataset_id}: {result}")
            remove_from_pending(dataset_id)
            return

    # Exhausted retries
    logger.warning(f"Exhausted retries for {dataset_id}")
    pending = load_pending()
    if dataset_id in pending:
        pending[dataset_id]["exhausted"] = True
        save_pending(pending)


# Gradio UI
with gr.Blocks(title="Dataset Card Drafter") as demo:
    gr.Markdown("# Dataset Card Drafter MVP")
    gr.Markdown(
        f"Watching datasets matching: `{'`, `'.join(WATCHED_PREFIXES)}`\n\n"
        f"Triggers when description < {MIN_DESCRIPTION_LENGTH} characters."
    )

    with gr.Tab("Status"):
        status_display = gr.JSON(label="Processed Datasets", value=load_processed)
        refresh_btn = gr.Button("Refresh")
        refresh_btn.click(fn=load_processed, outputs=status_display)

    with gr.Tab("Pending"):
        gr.Markdown(
            "Datasets waiting for the viewer to be ready.\n\n"
            "Background retries happen at 1min, 2min, 5min intervals."
        )
        pending_display = gr.JSON(label="Pending Datasets", value=load_pending)
        pending_refresh_btn = gr.Button("Refresh")
        pending_refresh_btn.click(fn=load_pending, outputs=pending_display)

        # Manual retry button
        retry_input = gr.Textbox(
            label="Dataset ID to retry",
            placeholder="davanstrien/dataset-name",
        )
        retry_btn = gr.Button("Retry Now")
        retry_output = gr.JSON(label="Result")

        async def manual_retry(dataset_id: str):
            if not dataset_id:
                return {"status": "error", "reason": "no dataset ID provided"}

            inference_token = os.getenv("HF_TOKEN")
            pr_token = os.getenv("LIBRARIAN_BOT_TOKEN")

            if not inference_token or not pr_token:
                return {"status": "error", "reason": "tokens not configured"}

            result = await process_dataset(dataset_id, inference_token, pr_token)

            if result.get("status") == "pr_created":
                processed = load_processed()
                processed[dataset_id] = {
                    "pr_url": result.get("pr_url"),
                    "timestamp": datetime.now().isoformat(),
                    "status": "pr_created",
                    "trigger": "manual_retry",
                }
                save_processed(processed)

            return result

        retry_btn.click(
            fn=manual_retry,
            inputs=retry_input,
            outputs=retry_output,
        )

    with gr.Tab("Manual Test"):
        gr.Markdown(
            "Test description generation without opening a PR.\n\n"
            "**Note:** This requires `HF_TOKEN` to be set."
        )
        test_input = gr.Textbox(
            label="Dataset ID",
            placeholder="davanstrien/test-dataset",
        )
        test_btn = gr.Button("Generate Description (Preview)")
        test_output = gr.Textbox(label="Generated Description", lines=5)
        test_status = gr.JSON(label="Status")

        def test_generate(dataset_id: str):
            if not dataset_id:
                return "", {"status": "error", "reason": "no dataset ID provided"}

            hf_token = os.getenv("HF_TOKEN")
            if not hf_token:
                return "", {"status": "error", "reason": "HF_TOKEN not set"}

            try:
                description = generate_description(dataset_id, hf_token)
                return description, {"status": "success", "length": len(description)}
            except Exception as e:
                return "", {"status": "error", "reason": str(e)}

        test_btn.click(
            fn=test_generate,
            inputs=test_input,
            outputs=[test_output, test_status],
        )

    with gr.Tab("Trigger PR"):
        gr.Markdown(
            "Manually trigger description generation and PR creation.\n\n"
            "**Warning:** This will open a real PR!\n\n"
            "Requires `HF_TOKEN` (for inference) and `LIBRARIAN_BOT_TOKEN` (for PRs)."
        )
        trigger_input = gr.Textbox(
            label="Dataset ID",
            placeholder="davanstrien/test-dataset",
        )
        trigger_btn = gr.Button("Generate & Open PR", variant="primary")
        trigger_output = gr.JSON(label="Result")

        async def trigger_pr(dataset_id: str):
            if not dataset_id:
                return {"status": "error", "reason": "no dataset ID provided"}

            inference_token = os.getenv("HF_TOKEN")
            pr_token = os.getenv("LIBRARIAN_BOT_TOKEN")

            if not inference_token:
                return {"status": "error", "reason": "HF_TOKEN not set"}
            if not pr_token:
                return {"status": "error", "reason": "LIBRARIAN_BOT_TOKEN not set"}

            result = await process_dataset(dataset_id, inference_token, pr_token)

            # Save to processed log
            if result.get("status") == "pr_created":
                processed = load_processed()
                processed[dataset_id] = {
                    "pr_url": result.get("pr_url"),
                    "timestamp": datetime.now().isoformat(),
                    "status": "pr_created",
                    "trigger": "manual",
                }
                save_processed(processed)

            return result

        trigger_btn.click(
            fn=trigger_pr,
            inputs=trigger_input,
            outputs=trigger_output,
        )


# WebhooksServer with automatic secret verification
app = WebhooksServer(ui=demo, webhook_secret=os.getenv("WEBHOOK_SECRET"))


@app.add_webhook("/dataset_update")
async def handle_dataset_webhook(payload: WebhookPayload) -> dict:
    """Handle dataset creation/update webhooks."""
    # Filter for datasets only
    if payload.repo.type != "dataset":
        return {"status": "skipped", "reason": "not a dataset"}

    # Filter for watched repos
    if not is_watched_repo(payload.repo.name):
        return {"status": "skipped", "reason": "not in watched list"}

    dataset_id = payload.repo.name

    # Get tokens
    inference_token = os.getenv("HF_TOKEN")
    pr_token = os.getenv("LIBRARIAN_BOT_TOKEN")

    if not inference_token:
        return {"status": "error", "reason": "HF_TOKEN not configured"}
    if not pr_token:
        return {"status": "error", "reason": "LIBRARIAN_BOT_TOKEN not configured"}

    # Process the dataset
    result = await process_dataset(dataset_id, inference_token, pr_token)

    # Handle pending status - queue for retry
    if result.get("status") == "pending":
        add_to_pending(dataset_id, result.get("reason", "viewer not ready"))
        # Spawn background retry task (non-blocking)
        asyncio.create_task(retry_pending_dataset(dataset_id))
        logger.info(f"Queued {dataset_id} for background retry")
        return result

    # Save to processed log
    processed = load_processed()
    processed[dataset_id] = {
        "pr_url": result.get("pr_url"),
        "timestamp": datetime.now().isoformat(),
        "status": result.get("status"),
        "reason": result.get("reason"),
        "trigger": "webhook",
        "event": payload.event.action if payload.event else None,
    }
    save_processed(processed)

    return result


if __name__ == "__main__":
    app.launch(ssr_mode=False)