import os
import gradio as gr

from backend_app.ingest import run_ingestion
from backend_app.rag_hf import RAGEngineHF
from backend_app.config import (
    DATA_DIR,
    FAISS_INDEX_PATH,
    DOCSTORE_PATH,
    BOT_WELCOME,
    BOT_NAME
)

# ---------- Prepare data once ----------
os.makedirs(DATA_DIR, exist_ok=True)

if not (os.path.exists(FAISS_INDEX_PATH) and os.path.exists(DOCSTORE_PATH)):
    run_ingestion()

rag = RAGEngineHF()


def respond(message, history, lang):
    message = (message or "").strip()
    if not message:
        return ""

    try:
        result = rag.answer(message, preferred_lang=lang)
        answer = (result.get("answer") or "").strip()
        return answer if answer else "I couldn’t find enough information to answer that. Please rephrase."
    except Exception:
        return "I ran into a temporary error while generating the answer. Please try again."


with gr.Blocks(title=BOT_NAME) as demo:
    gr.Markdown(f"## {BOT_NAME}")
    gr.Markdown(BOT_WELCOME)

    lang = gr.Dropdown(
        choices=["English", "Sinhala", "Tamil"],
        value="English",
        label="Response language"
    )

    # ✅ Minimal ChatInterface for maximum compatibility
    gr.ChatInterface(
        fn=respond,
        additional_inputs=[lang]
    )

demo.queue()

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