"""
Jarvis AI Automation Planner — Hugging Face Space Application

Exposes two interfaces:
  1. Gradio chat UI at /  (browser testing)
  2. OpenAI-compatible  POST /v1/chat/completions  (mobile clients)
"""

import json
import logging
import os
import time
import uuid

import gradio as gr
import uvicorn
from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse

from jarvis.agent import JarvisAgent
from jarvis.tools import ToolRegistry

# ---------------------------------------------------------------------------
# Logging
# ---------------------------------------------------------------------------
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s  %(name)-20s  %(levelname)-7s  %(message)s",
)
logger = logging.getLogger("jarvis.app")

# ---------------------------------------------------------------------------
# Initialise agent (loaded once at startup)
# ---------------------------------------------------------------------------
registry = ToolRegistry()
agent = JarvisAgent(registry=registry)
logger.info("Jarvis agent ready  •  model=%s  •  tools=%s", agent.model, registry.tool_names())

# ---------------------------------------------------------------------------
# Gradio chat interface
# ---------------------------------------------------------------------------


def chat_fn(message: str, _history: list) -> str:
    """Gradio handler — takes a user message and returns the JSON plan."""
    plan = agent.plan(message)
    return json.dumps(plan, indent=2)


demo = gr.ChatInterface(
    fn=chat_fn,
    title="🤖 Jarvis — AI Automation Planner",
    description=(
        "Type a command (e.g. *\"download a galaxy image\"*) and Jarvis will "
        "return a structured JSON automation plan for your mobile device."
    ),
    examples=[
        "Download a galaxy image",
        "Open YouTube",
        "Search water bottle for boys",
        "Download a galaxy image and send it to Arun",
        "Set an alarm for 7 AM",
        "Search Google for latest AI news",
    ],
    type="messages",
)

# ---------------------------------------------------------------------------
# Custom FastAPI routes (mounted alongside Gradio)
# ---------------------------------------------------------------------------

app = FastAPI(title="Jarvis – AI Automation Planner", version="1.0.0")

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)


@app.post("/v1/chat/completions")
async def chat_completions(request: Request):
    """OpenAI-compatible chat completions endpoint for mobile clients."""
    try:
        body = await request.json()
    except Exception:
        return JSONResponse({"error": "Invalid JSON body."}, status_code=400)

    messages = body.get("messages", [])
    if not messages:
        return JSONResponse({"error": "No messages provided."}, status_code=400)

    user_msg = ""
    for m in reversed(messages):
        if m.get("role") == "user":
            user_msg = m.get("content", "")
            break

    if not user_msg:
        return JSONResponse({"error": "No user message found."}, status_code=400)

    plan = agent.plan(user_msg)

    return JSONResponse({
        "id": f"chatcmpl-{uuid.uuid4().hex[:12]}",
        "object": "chat.completion",
        "created": int(time.time()),
        "model": agent.model,
        "choices": [
            {
                "index": 0,
                "message": {
                    "role": "assistant",
                    "content": json.dumps(plan),
                },
                "finish_reason": "stop",
            }
        ],
        "usage": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0},
    })


@app.get("/v1/models")
async def list_models():
    return {
        "object": "list",
        "data": [{"id": agent.model, "object": "model", "owned_by": "jarvis"}],
    }


@app.get("/health")
async def health():
    return {"status": "ok", "model": agent.model, "tools": registry.tool_names()}


# ---------------------------------------------------------------------------
# Mount Gradio onto the FastAPI app at root
# ---------------------------------------------------------------------------
app = gr.mount_gradio_app(app, demo, path="/")

# ---------------------------------------------------------------------------
# Entry point
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    uvicorn.run("app:app", host="0.0.0.0", port=7860, log_level="info")