#  agent.py — Agent loop dan tool execution.
#  submit() adalah entry point: membaca input buffer, mengirim ke LLM,
#  memproses tool calls, dan menampilkan hasil di log.

import json
from datetime import datetime
from .render import draw
from scripts import ntro


def log(app, role, text):
    # Simpan item ke app.log untuk di-render oleh render.py
    app.log.append({
        "role": role,
        "text": text,
        "time": datetime.now().strftime("%H:%M"),
    })


def submit(app, stdscr):
    # Kirim query dari input buffer ke LLM.
    # Loop sampai LLM mengembalikan final answer (tanpa tool_calls)
    # atau mencapai max_iterations.

    query = "\n".join(app.input_buffer).strip()
    if not query:
        return

    log(app, "user", query)
    # Reset input buffer
    app.input_buffer = [""]
    app.input_line = 0
    app.input_col = 0
    app.scroll = 999999  # scroll ke paling bawah
    app.processing = True
    draw(app, stdscr)
    stdscr.refresh()

    app.messages.append({"role": "user", "content": query})

    stamp = ntro.start()

    for step in range(app.agent_max_iterations):
        stamp_step = ntro.start()
        log(app, "system", f"  step {step + 1} \u2014 Thinking...")
        app.scroll = 999999
        draw(app, stdscr)
        stdscr.refresh()

        response = app.llm.chat(app.messages, tools=app.TOOLS)

        # Hapus "step N — LLM..." log, ganti dengan hasil aktual
        app.log.pop()

        if response.tool_calls:
            # LLM meminta menjalankan tool(s)
            amsg = {
                "role": "assistant",
                "content": response.content,
                "tool_calls": response.tool_calls,
            }
            app.messages.append(amsg)
            if response.content and response.content.strip():
                log(app, "ai", response.content)
                app.scroll = 999999
                draw(app, stdscr)
                stdscr.refresh()
            for tc in response.tool_calls:
                tname = tc["function"]["name"]
                log(app, "system", f"  \u2192 {tname}")
                app.scroll = 999999
                draw(app, stdscr)
                stdscr.refresh()
                execute_tool(app, tc)
        else:
            # Final answer — tidak ada tool_calls
            if response.content:
                app.messages.append({
                    "role": "assistant",
                    "content": response.content,
                })
                log(app, "ai", response.content)
            log(app, "sep", "")
            app.processing = False
            app.scroll = 999999
            draw(app, stdscr)
            stdscr.refresh()
            ntro.end(stamp)
            return
        ntro.end(stamp_step)

    # Timeout — max iterations tercapai tanpa final answer
    log(app, "error", "Max iterations reached without final answer.")
    app.messages.append({"role": "assistant",
                          "content": "Max iterations reached without final answer."})
    app.processing = False
    ntro.end(stamp)


def execute_tool(app, tool_call):
    # Dispatch tool_call ke handler yang terdaftar di TOOL_HANDLERS.
    # search_code dan git_operation butuh penanganan argumen khusus.
    tname = tool_call["function"]["name"]
    targs = json.loads(tool_call["function"]["arguments"])
    handler = app.TOOL_HANDLERS.get(tname)

    if not handler:
        result = f"Tool {tname} not found"
    else:
        try:
            if tname == "search_code":
                result = handler(
                    pattern=targs["pattern"],
                    search_type=targs["search_type"],
                    path=targs.get("path", "."),
                )
            elif tname == "git_operation":
                result = handler(args=targs["args"])
            else:
                result = handler(**targs)
        except Exception as e:
            result = f"Error executing tool: {str(e)}"

    # Hasil tool disimpan ke messages agar bisa dikirim balik ke LLM
    app.messages.append({
        "role": "tool",
        "tool_call_id": tool_call["id"],
        "content": str(result),
    })