"""Step 5 — Gradio chat UI for the Medical RAG Chatbot.

Run locally:
    python app.py

Then open the printed local URL in your browser. This same file is the
entry point HuggingFace Spaces looks for, so it deploys with no changes.
"""
from __future__ import annotations

import gradio as gr

from src.rag_pipeline import RAGChatbot

DISCLAIMER = (
    "⚠️ **Research demo only — not medical advice.** "
    "Answers are generated from a small set of documents and may be incomplete "
    "or wrong. Always consult a qualified ophthalmologist."
)

EXAMPLE_QUESTIONS = [
    "What is diabetic retinopathy?",
    "How is the severity of diabetic retinopathy graded?",
    "What deep learning methods are used to detect diabetic retinopathy?",
    "Why is early screening for diabetic retinopathy important?",
]

def ensure_vector_store() -> None:
    """Build the ChromaDB index on first run if it doesn't exist yet.

    This makes the app self-contained on HuggingFace Spaces: if the vector
    store is missing, it downloads the source PDFs (when needed) and ingests
    them automatically, so the Space works without a manual build step.
    """
    from src.config import CHROMA_DIR, DATA_DIR

    if CHROMA_DIR.exists():
        return  # already built — nothing to do

    print("No vector store found — building it now (first-run setup) ...")
    # If there are no source PDFs yet, try to fetch the defaults.
    if not list(DATA_DIR.glob("*.pdf")):
        try:
            from src.download_sources import main as download_main
            download_main()
        except Exception as exc:  # noqa: BLE001
            print(f"  (could not auto-download sources: {exc})")
    # Build the index from whatever PDFs are present.
    from src.ingest import main as ingest_main
    ingest_main()


# Load the pipeline once at startup. If something is missing (no docs or no key),
# show the error in the UI instead of crashing silently.
_load_error: str | None = None
bot: RAGChatbot | None = None
try:
    ensure_vector_store()
    bot = RAGChatbot()
except Exception as exc:  # noqa: BLE001
    _load_error = str(exc)


def _format_sources(sources) -> str:
    if not sources:
        return ""
    seen = []
    for d in sources:
        src = d.metadata.get("source", "unknown")
        page = d.metadata.get("page", "?")
        label = f"{src} (p.{page})"
        if label not in seen:
            seen.append(label)
    return "\n\n**Sources used:** " + "; ".join(seen)


def respond(message: str, history):
    """Gradio chat callback."""
    if _load_error:
        return f"⚠️ Setup incomplete: {_load_error}"
    if not message.strip():
        return "Please type a question about diabetic retinopathy."
    resp = bot.ask(message)
    return resp.answer + _format_sources(resp.sources)


with gr.Blocks(title="Medical RAG Chatbot") as demo:
    gr.Markdown("# 🩺 Medical RAG Chatbot — Diabetic Retinopathy")
    gr.Markdown(
        "Ask questions about diabetic retinopathy. Answers are grounded in "
        "open-access research papers using Retrieval-Augmented Generation "
        "(LangChain · ChromaDB · sentence-transformers · Llama 3.3 70B on Groq)."
    )
    gr.Markdown(DISCLAIMER)

    gr.ChatInterface(
        fn=respond,
        examples=EXAMPLE_QUESTIONS,
    )

if __name__ == "__main__":
    demo.launch()