"""Gradio web UI for the Bosch HVAC Product Knowledge Bot.

Run with: python app.py
"""
import os
from dotenv import load_dotenv
import gradio as gr

from src.rag import BoschProductRAG

load_dotenv()

CATALOG_PATH = os.path.join(os.path.dirname(__file__), "src", "products_catalog.json")

# Build the RAG index once at startup (not per-request).
_rag = BoschProductRAG()
_rag.build_index(_rag.load_products(CATALOG_PATH))


def answer_question(question: str) -> str:
    if not question or not question.strip():
        return "Please enter a question."

    result = _rag.query(question)

    sources_md = "\n".join(
        f"- [{s['name']}]({s['url']})" if s["url"] else f"- {s['name']}"
        for s in result["sources"]
    )

    return (
        f"### Answer\n{result['answer']}\n\n"
        f"### Sources\n{sources_md or '_none retrieved_'}\n\n"
        f"---\n*Latency: {result['latency_ms']:.0f}ms*"
    )


demo = gr.Interface(
    fn=answer_question,
    inputs=gr.Textbox(
        label="Ask about Bosch HVAC products",
        placeholder="e.g. Which heat pump is best for retrofits?",
        lines=2,
    ),
    outputs=gr.Markdown(label="Response"),
    title="Bosch HVAC Product Knowledge Bot",
    description=(
        "RAG-powered assistant for Bosch HVAC sales & spec questions. "
        "Retrieval: FAISS + Google Gemini embeddings. Generation: Gemini Flash Lite."
    ),
    examples=[
        "Which heat pump is best for retrofits?",
        "What's the most energy-efficient system you offer?",
        "Do you have a smart thermostat?",
        "What cooling systems use R32 refrigerant?",
    ],
)

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