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Running
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Zero
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"""Model initialization and management"""
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from llama_index.llms.huggingface import HuggingFaceLLM
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from logger import logger
import config
try:
from TTS.api import TTS
TTS_AVAILABLE = True
except ImportError:
TTS_AVAILABLE = False
TTS = None
def initialize_medical_model(model_name: str):
"""Initialize medical model (MedSwin) - download on demand"""
if model_name not in config.global_medical_models or config.global_medical_models[model_name] is None:
logger.info(f"Initializing medical model: {model_name}...")
model_path = config.MEDSWIN_MODELS[model_name]
tokenizer = AutoTokenizer.from_pretrained(model_path, token=config.HF_TOKEN)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
trust_remote_code=True,
token=config.HF_TOKEN,
torch_dtype=torch.float16
)
config.global_medical_models[model_name] = model
config.global_medical_tokenizers[model_name] = tokenizer
logger.info(f"Medical model {model_name} initialized successfully")
return config.global_medical_models[model_name], config.global_medical_tokenizers[model_name]
def initialize_tts_model():
"""Initialize TTS model for text-to-speech"""
if not TTS_AVAILABLE:
logger.warning("TTS library not installed. TTS features will be disabled.")
return None
if config.global_tts_model is None:
try:
logger.info("Initializing TTS model for voice generation...")
config.global_tts_model = TTS(model_name=config.TTS_MODEL, progress_bar=False)
logger.info("TTS model initialized successfully")
except Exception as e:
logger.warning(f"TTS model initialization failed: {e}")
logger.warning("TTS features will be disabled. If pyworld dependency is missing, try: pip install TTS --no-deps && pip install coqui-tts")
config.global_tts_model = None
return config.global_tts_model
def get_or_create_embed_model():
"""Reuse embedding model to avoid reloading weights each request"""
if config.global_embed_model is None:
logger.info("Initializing shared embedding model for RAG retrieval...")
config.global_embed_model = HuggingFaceEmbedding(model_name=config.EMBEDDING_MODEL, token=config.HF_TOKEN)
return config.global_embed_model
def get_llm_for_rag(temperature=0.7, max_new_tokens=256, top_p=0.95, top_k=50):
"""Get LLM for RAG indexing (uses medical model)"""
medical_model_obj, medical_tokenizer = initialize_medical_model(config.DEFAULT_MEDICAL_MODEL)
return HuggingFaceLLM(
context_window=4096,
max_new_tokens=max_new_tokens,
tokenizer=medical_tokenizer,
model=medical_model_obj,
generate_kwargs={
"do_sample": True,
"temperature": temperature,
"top_k": top_k,
"top_p": top_p
}
)
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