#!/usr/bin/env python3
"""
Pipecat Bot Template — OpenAILLMService avec API Hermes
========================================================
Template minimal pour un bot texte via WebSocket.
Adapté de ~/workspace/jarvis-pipecat/bot.py

Prérequis: pipecat-ai[websocket]>=1.4.0, openai, uvicorn, websockets
"""

import json, os, subprocess, sys
from fastapi import FastAPI, WebSocket
from fastapi.responses import HTMLResponse
import uvicorn

# ⚠️ Pipecat 1.4.0 : utiliser `import module as m` (from-import cassé)
import pipecat.frames.frames as pframes
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.frame_processor import FrameProcessor, FrameDirection
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams, FastAPIWebsocketTransport

# ─── Config ──────────────────────────────────────────────────
PORT = int(os.environ.get("BOT_PORT", "7081"))
HERMES_URL = os.environ.get("HERMES_API_URL", "http://localhost:8642/v1")
HERMES_KEY = os.environ.get("HERMES_API_KEY", "")
SYSTEM_PROMPT = "Tu es un assistant vocal concis. Parle en français, 3 phrases max."


# ─── Processeur custom : texte → LLMMessagesFrame ────────────
class TextToLLMProcessor(FrameProcessor):
    """Remplace le context aggregator (buggé en 1.4.0)."""

    def __init__(self, system_prompt: str):
        super().__init__()
        self._messages = [{"role": "system", "content": system_prompt}]

    async def process_frame(self, frame, direction):
        await self.push_frame(frame, direction)
        if isinstance(frame, pframes.InputTransportMessageFrame):
            text = frame.message
            if isinstance(text, str) and text.strip():
                self._messages.append({"role": "user", "content": text})
                if len(self._messages) > 21:
                    self._messages = [self._messages[0]] + self._messages[-20:]
                await self.push_frame(pframes.LLMMessagesFrame(messages=list(self._messages)))
        elif isinstance(frame, pframes.LLMFullResponseStartFrame):
            self._response_text = ""
        elif isinstance(frame, pframes.TextFrame):
            if hasattr(self, '_response_text'):
                self._response_text += frame.text
        elif isinstance(frame, pframes.LLMFullResponseEndFrame):
            if hasattr(self, '_response_text') and self._response_text.strip():
                self._messages.append({"role": "assistant", "content": self._response_text.strip()})
                del self._response_text


# ─── App ─────────────────────────────────────────────────────
app = FastAPI()


@app.websocket("/ws")
async def ws_endpoint(websocket: WebSocket):
    transport = FastAPIWebsocketTransport(
        websocket=websocket,
        params=FastAPIWebsocketParams(add_wav_header=False, session_timeout=None),
    )
    llm = OpenAILLMService(
        api_key=HERMES_KEY,
        base_url=HERMES_URL,
        settings=OpenAILLMService.Settings(model="Hermes Agent", temperature=0.8, max_tokens=300, system_instruction=SYSTEM_PROMPT),
    )
    proc = TextToLLMProcessor(SYSTEM_PROMPT)
    pipeline = Pipeline(processors=[transport.input(), proc, llm, transport.output()])
    task = PipelineTask(pipeline)
    runner = PipelineRunner()
    await runner.run(task)


if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=PORT)