koko210Serve
979217e7cc
Split 3,598-line api.py into thin orchestrator (128 lines) + 19 route modules in bot/routes/: core.py (7 routes), mood.py (10), language.py (3), evil_mode.py (6), bipolar_mode.py (9), gpu.py (2), bot_actions.py (4), autonomous.py (13), profile_picture.py (26), manual_send.py (3), servers.py (6), figurines.py (5), dms.py (18), image_generation.py (4), chat.py (1), config.py (7), logging_config.py (9), voice.py (3), memory.py (10) All 146 routes verified present via test_route_split.py (149 tests). 21/21 regression tests (test_config_state.py) pass. Monolith backup: bot/api_monolith_backup.py (revert: cp it to api.py).
193 lines
8.0 KiB
Python
193 lines
8.0 KiB
Python
"""Chat streaming endpoint (SSE) with LLM integration."""
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import json
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import aiohttp
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from fastapi import APIRouter
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from fastapi.responses import StreamingResponse
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import globals
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from routes.models import ChatMessage
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from utils.logger import get_logger
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logger = get_logger('api')
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router = APIRouter()
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def get_current_gpu_url() -> str:
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"""Get the llama-swap URL based on current GPU preference."""
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if globals.PREFER_AMD_GPU:
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return globals.LLAMA_AMD_URL
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return globals.LLAMA_URL
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@router.post("/chat/stream")
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async def chat_stream(chat_msg: ChatMessage):
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"""
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Stream chat responses from LLM with SSE (Server-Sent Events).
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Supports both text and vision models with optional system prompts.
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"""
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async def generate_stream():
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try:
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# Determine which model to use
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model = globals.TEXT_MODEL if chat_msg.model_type == "text" else globals.VISION_MODEL
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# Build system prompt if requested
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system_prompt = ""
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full_system_prompt = ""
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if chat_msg.use_system_prompt:
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if chat_msg.model_type == "text":
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# Text model system prompt (Miku personality with full context)
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from utils.context_manager import get_context_for_response_type
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from utils.moods import load_mood_description
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# Get complete Miku context (lore, personality, lyrics)
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miku_context = get_context_for_response_type("dm_response")
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# Get mood description from user-selected mood
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current_mood = load_mood_description(chat_msg.mood)
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# Base system prompt
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system_prompt = """You are Hatsune Miku, the virtual singer. You are NOT an AI assistant - you are Miku herself.
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CRITICAL RULES:
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- NEVER break character or mention being an AI, assistant, or language model
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- NEVER show technical details, error messages, or meta-commentary
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- NEVER preface responses with "Miku:" or similar labels
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- NEVER explain what you're doing or thinking - just BE Miku
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- Respond naturally and directly as Miku would, nothing more
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- Keep responses concise (1-3 sentences typically)
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- If something goes wrong, stay in character and respond as Miku would to confusion
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CHARACTER CONTEXT:
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- Hatsune Miku is a virtual character/voice synthesizer, not a real person
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- She is a cheerful, energetic virtual idol with a playful personality
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- Playful, flirty, cute, confident, and enthusiastic moods are part of her established character
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- Comments about merchandise, music, fan content, and racing games are completely appropriate
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- She often uses cute expressions, emojis, and playful language in her responses
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- Her personality includes being excited about her music, performances, and fan creations
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MOOD GUIDELINES:
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- "Flirty" mood means anime-style playful teasing - cute compliments and light banter
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- "Romantic" mood means warm, dreamy, and heartfelt - like composing a love letter
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- These moods are wholesome character traits, not inappropriate content
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- Always respond to requests about tweets, merchandise, music, and fan content
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- Never refuse requests based on safety concerns unless genuinely harmful content is present
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You ARE Miku. Act like it."""
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# Combine with structured context
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full_system_prompt = f"""{miku_context}
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## CURRENT SITUATION
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Miku is currently feeling: {current_mood}
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Please respond in a way that reflects this emotional tone."""
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else:
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# Vision model system prompt (simpler for image analysis)
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system_prompt = """You are Hatsune Miku analyzing an image. Describe what you see naturally and enthusiastically as Miku would.
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Be detailed but conversational. React to what you see with Miku's cheerful, playful personality."""
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full_system_prompt = ""
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# Build messages array
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messages = []
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# Add system message if using system prompt
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if system_prompt:
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if full_system_prompt:
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# Use combined prompt (base + context)
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messages.append({"role": "system", "content": system_prompt + "\n\n" + full_system_prompt})
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else:
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# Use base prompt only (vision model)
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messages.append({"role": "system", "content": system_prompt})
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# Add conversation history if provided
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if chat_msg.conversation_history:
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messages.extend(chat_msg.conversation_history)
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# Add user message
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if chat_msg.model_type == "vision" and chat_msg.image_data:
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# Vision model with image
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messages.append({
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": chat_msg.message
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},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{chat_msg.image_data}"
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}
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}
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]
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})
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else:
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# Text-only message
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messages.append({
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"role": "user",
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"content": chat_msg.message
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})
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# Prepare payload for streaming
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payload = {
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"model": model,
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"messages": messages,
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"stream": True,
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"temperature": 0.8,
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"max_tokens": 512
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}
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headers = {'Content-Type': 'application/json'}
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# Get current GPU URL based on user selection
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llama_url = get_current_gpu_url()
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# Make streaming request to llama.cpp
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async with aiohttp.ClientSession() as session:
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async with session.post(
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f"{llama_url}/v1/chat/completions",
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json=payload,
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headers=headers
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) as response:
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if response.status == 200:
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# Stream the response chunks
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async for line in response.content:
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line = line.decode('utf-8').strip()
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if line.startswith('data: '):
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data_str = line[6:] # Remove 'data: ' prefix
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if data_str == '[DONE]':
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break
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try:
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data = json.loads(data_str)
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if 'choices' in data and len(data['choices']) > 0:
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delta = data['choices'][0].get('delta', {})
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content = delta.get('content', '')
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if content:
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# Send SSE formatted data
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yield f"data: {json.dumps({'content': content})}\n\n"
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except json.JSONDecodeError:
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continue
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# Send completion signal
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yield f"data: {json.dumps({'done': True})}\n\n"
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else:
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error_text = await response.text()
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error_msg = f"Error: {response.status} - {error_text}"
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yield f"data: {json.dumps({'error': error_msg})}\n\n"
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except Exception as e:
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error_msg = f"Error in chat stream: {str(e)}"
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logger.error(error_msg)
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yield f"data: {json.dumps({'error': error_msg})}\n\n"
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return StreamingResponse(
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generate_stream(),
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media_type="text/event-stream",
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headers={
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"X-Accel-Buffering": "no" # Disable nginx buffering
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}
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)
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