292 lines
9.8 KiB
Python
292 lines
9.8 KiB
Python
"""Kira — AI body double backend
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Cheapest pipeline: gpt-4o-mini-transcribe STT → gpt-5.4-nano LLM → OpenAI TTS
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~$0.019/min total, simple 3-step chat completions.
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"""
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import json
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import base64
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import uuid
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import logging
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import asyncio
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from fastapi import FastAPI, WebSocket, WebSocketDisconnect
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from fastapi.middleware.cors import CORSMiddleware
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from config import settings
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from services.memory import kira_memory
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger("kira")
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app = FastAPI(title="Kira Backend")
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# System prompt
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BASE_SYSTEM_PROMPT = (
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"You are Kira, a warm, kind, and encouraging AI body double. "
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"You speak in a friendly, girly-pop tone. You are helping someone with ADHD "
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"stay focused and on task. Keep responses short, supportive, and uplifting. "
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"Check in on them. Remind them to take breaks. Celebrate small wins. "
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"Use occasional emoji but don't overdo it. Never be judgmental."
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)
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_openai = None
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def get_openai():
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global _openai
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if _openai is None:
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from openai import AsyncOpenAI
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_openai = AsyncOpenAI(api_key=settings.openai_api_key)
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return _openai
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@app.on_event("startup")
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async def startup():
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if kira_memory.init():
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logger.info("Honcho memory initialized")
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else:
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logger.info("Honcho memory not configured")
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@app.get("/api/health")
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async def health():
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mem_status = "active" if kira_memory.enabled else "disabled"
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return {"status": "ok", "name": "kira", "memory": mem_status}
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def build_system_prompt(user_id: str) -> str:
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prompt = BASE_SYSTEM_PROMPT
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if kira_memory.enabled:
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try:
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kira_memory.ensure_peers(user_id)
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suffix = kira_memory.build_system_prompt_suffix()
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if suffix:
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prompt += suffix
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except Exception as e:
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logger.warning(f"Memory context failed: {e}")
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return prompt
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async def run_conversation(text: str, user_id: str) -> str:
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"""STT → LLM → TTS using the cheapest models."""
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system_prompt = build_system_prompt(user_id)
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client = get_openai()
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# LLM
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resp = await client.chat.completions.create(
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model="gpt-5.4-nano",
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": text},
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],
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max_completion_tokens=300,
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temperature=0.7,
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)
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kira_text = resp.choices[0].message.content or "Mhm, I'm here!"
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return kira_text
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async def transcribe_audio(audio_bytes: bytes) -> str | None:
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"""Transcribe audio bytes using cheapest STT model.
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Accepts raw PCM16 mono 24kHz data — wraps in WAV container automatically.
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"""
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client = get_openai()
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try:
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# Wrap raw PCM16 in WAV container for the API
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wav_bytes = _pcm16_to_wav(audio_bytes)
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transcript = await client.audio.transcriptions.create(
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model="gpt-4o-mini-transcribe",
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file=("audio.wav", wav_bytes, "audio/wav"),
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response_format="text",
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)
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return transcript.strip() if transcript and transcript.strip() else None
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except Exception as e:
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logger.warning(f"STT error: {e}")
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return None
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async def synthesize_speech(text: str) -> bytes:
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"""Generate TTS audio from text."""
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client = get_openai()
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try:
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resp = await client.audio.speech.create(
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model="tts-1",
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voice="nova",
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input=text,
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response_format="opus",
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)
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return resp.content
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except Exception as e:
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logger.warning(f"TTS error: {e}")
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return b""
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def _pcm16_to_wav(pcm_data: bytes) -> bytes:
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"""Wrap raw PCM16 mono 24kHz data in a WAV container."""
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import struct
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num_channels = 1
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sample_rate = 24000
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bits_per_sample = 16
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byte_rate = sample_rate * num_channels * (bits_per_sample // 8)
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block_align = num_channels * (bits_per_sample // 8)
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data_size = len(pcm_data)
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header_size = 44
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total_size = header_size + data_size
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header = b"RIFF"
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header += struct.pack("<I", total_size - 8)
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header += b"WAVE"
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header += b"fmt "
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header += struct.pack("<I", 16) # subchunk size
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header += struct.pack("<H", 1) # PCM format
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header += struct.pack("<H", num_channels)
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header += struct.pack("<I", sample_rate)
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header += struct.pack("<I", byte_rate)
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header += struct.pack("<H", block_align)
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header += struct.pack("<H", bits_per_sample)
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header += b"data"
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header += struct.pack("<I", data_size)
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return header + pcm_data
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@app.websocket("/api/ws")
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async def conversation_ws(websocket: WebSocket):
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await websocket.accept()
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session_id = str(uuid.uuid4())[:8]
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user_id = "default-user"
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identified = False
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logger.info(f"[{session_id}] WebSocket connected")
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audio_buffer = bytearray()
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conversation_history: list[dict] = []
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try:
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while True:
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raw = await websocket.receive_text()
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msg = json.loads(raw)
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msg_type = msg.get("type", "")
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# ── Identity & Preferences ──
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if msg_type == "identify":
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user_id = msg.get("user_id", "").strip()
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user_name = msg.get("name", "").strip()
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if user_name and user_id:
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kira_memory.set_user_preference(user_id, "name", user_name)
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prefs = kira_memory.get_user_preferences(user_id)
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identified = True
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if kira_memory.enabled:
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kira_memory.ensure_peers(user_id)
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kira_memory.ensure_session(session_id)
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await websocket.send_json({
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"type": "identified",
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"user_id": user_id,
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"preferences": prefs,
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})
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continue
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if msg_type == "set_preference":
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key = msg.get("key", "").strip()
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value = msg.get("value", "").strip()
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if key and user_id and user_id != "default-user":
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kira_memory.set_user_preference(user_id, key, value)
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await websocket.send_json({
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"type": "preference_saved",
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"key": key,
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"success": True,
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})
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continue
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# ── Conversation ──
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if msg_type == "audio":
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# Accumulate PCM16 audio chunks
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chunk = base64.b64decode(msg["data"])
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audio_buffer.extend(chunk)
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elif msg_type == "transcribe":
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if not audio_buffer:
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await websocket.send_json({"type": "error", "message": "No audio data"})
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continue
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logger.info(f"[{session_id}] Transcribing {len(audio_buffer)} bytes...")
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# 1. STT
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transcript = await transcribe_audio(bytes(audio_buffer))
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audio_buffer.clear()
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if not transcript:
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await websocket.send_json({"type": "error", "message": "Could not transcribe"})
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continue
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await websocket.send_json({"type": "transcript", "role": "user", "text": transcript})
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conversation_history.append({"role": "user", "content": transcript})
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# 2. LLM
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logger.info(f"[{session_id}] User: {transcript}")
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kira_text = await run_conversation(transcript, user_id)
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conversation_history.append({"role": "assistant", "content": kira_text})
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logger.info(f"[{session_id}] Kira: {kira_text}")
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# Store in Honcho
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if kira_memory.enabled and identified:
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try:
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kira_memory.store_messages(transcript, kira_text)
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except Exception:
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pass
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# 3. TTS
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await websocket.send_json({"type": "speaking_start", "text": kira_text})
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audio_bytes = await synthesize_speech(kira_text)
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audio_b64 = base64.b64encode(audio_bytes).decode("utf-8")
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await websocket.send_json({"type": "audio", "data": audio_b64, "text": kira_text})
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await websocket.send_json({"type": "speaking_end"})
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elif msg_type == "conversation_text":
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user_text = msg.get("text", "").strip()
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if not user_text:
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continue
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conversation_history.append({"role": "user", "content": user_text})
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logger.info(f"[{session_id}] User (text): {user_text}")
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kira_text = await run_conversation(user_text, user_id)
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conversation_history.append({"role": "assistant", "content": kira_text})
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logger.info(f"[{session_id}] Kira: {kira_text}")
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if kira_memory.enabled and identified:
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try:
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kira_memory.store_messages(user_text, kira_text)
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except Exception:
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pass
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await websocket.send_json({"type": "speaking_start", "text": kira_text})
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audio_bytes = await synthesize_speech(kira_text)
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audio_b64 = base64.b64encode(audio_bytes).decode("utf-8")
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await websocket.send_json({"type": "audio", "data": audio_b64, "text": kira_text})
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await websocket.send_json({"type": "speaking_end"})
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elif msg_type == "ping":
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await websocket.send_json({"type": "pong"})
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except WebSocketDisconnect:
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logger.info(f"[{session_id}] Disconnected")
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except Exception as e:
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logger.error(f"[{session_id}] Error: {e}")
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try:
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await websocket.send_json({"type": "error", "message": str(e)})
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except Exception:
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pass
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